Why Wind Power Scales as v³: An Intuition Built from First Principles

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Posted on March 9, 2026 by matteomycarta

and a regulator’s motivation for caring

The Three Methods: A Regulator’s Ladder for Evaluating Energy Claims

Suppose a developer submits a proposal for the Middle Bank area on the Scotian Shelf: 926 turbines, each rated at 15 MW, at 4.2D spacing (where D is the rotor diameter—240 m for this turbine, so 4.2D ≈ 1,000 m between towers), claiming annual energy production (AEP) of 60 TWh. Is that plausible?

You have three increasingly sophisticated ways to check.

METHOD 1 Nameplate (30 seconds, back of envelope)

The simplest possible estimate:

AEP = N × P_rated × 8760 hours × CF

Where N is the number of turbines, P_rated is each turbine’s maximum output (15 MW here), 8760 is the hours in a year, and CF is the capacity factor—the fraction of rated output the turbine actually produces over a year. For offshore wind, CF typically falls between 0.40 and 0.55.

Check the units: N is dimensionless (a count), P_rated is in MW, 8760 is in hours, and CF is dimensionless (a fraction). So AEP comes out in MW × hours = MWh—or equivalently, dividing by 10&sup6, TWh. It’s just (number of turbines) × (power per turbine) × (hours per year) × (fraction of time at full output).

For our developer’s claim:

Assumption	AEP
CF = 0.40 (conservative)	926 × 15 MW × 8760 × 0.40 = 48.7 TWh
CF = 0.50 (typical offshore)	926 × 15 MW × 8760 × 0.50 = 60.8 TWh
CF = 0.55 (optimistic)	926 × 15 MW × 8760 × 0.55 = 66.9 TWh

The developer’s 60 TWh falls in range—right at a typical offshore CF. Not obviously wrong. But this tells you nothing about whether CF = 0.40 or 0.55 is appropriate for this site. The capacity factor is doing all the work, and you borrowed it from industry averages rather than deriving it from the actual wind resource.

What the nameplate method hides

It treats CF as an input. But CF is an output—it’s determined by the wind speed distribution, the turbine’s power curve, wake interactions, and availability. It’s the answer, not the question.

METHOD 2 Ginsberg Swept Area (5 minutes, needs mean wind speed)

If you know the site’s mean wind speed, you can estimate power from first principles:

P_available = ½ × ρ × A × v_avg³

Where ρ is air density (~1.225 kg/m³ at sea level), A is the rotor’s swept area (π × D² / 4), and v_avg is the mean wind speed at hub height.

The derivation. Consider a cylinder of air passing through the rotor in time t. Its length is v × t, so its volume is A × v × t, and its mass is ρ × A × v × t. The kinetic energy of that air is:

KE = ½ × m × v² = ½ × (ρ × A × v × t) × v² = ½ × ρ × A × v³ × t

Divide both sides by t to get power (energy per unit time):

P = KE / t = ½ × ρ × A × v³

That’s where the v³ comes from: v once from the mass flow rate (how fast air arrives), v² from the kinetic energy per unit mass (how much energy it carries). Ginsberg (2019) walks through this same derivation; the full physical reasoning for why this matters is developed in The Starting Point below.

But there’s a catch. Wind speed varies, and because power scales as v³, the average of the cubes is not the cube of the average. A site with v_avg = 9 m/s but gusty conditions produces more energy than a site with a steady 9 m/s, because the high-wind moments contribute disproportionately (v³ is convex).

Ginsberg handles this with the Energy Pattern Factor (EPF)—a multiplier that corrects the mean-cubed estimate for the actual shape of the wind speed distribution:

Mean Power Density = ½ × ρ × EPF × v_avg³

For Rayleigh-distributed winds (shape factor k = 2), EPF ≈ 1.91. This corrects for the distribution without requiring the full wind record. Then to get AEP:

AEP = Mean Power Density × A × 8760 × η_turbine × η_availability

Where η_turbine accounts for the turbine’s conversion efficiency (C_p, the power coefficient—capped at 59.3% by the Betz limit, which is the theoretical maximum any turbine can extract from the wind) and η_availability for downtime.

This is more physical—you’re deriving CF from the wind resource rather than assuming it. For the Scotian Shelf, with mean winter wind of 9.3 m/s and summer 7.1 m/s at hub height, the swept area method produces a site-specific estimate rather than borrowing a generic CF from global averages.

What the swept area method hides

It treats each turbine as if it sees the undisturbed wind. In reality, downstream turbines sit in the wakes of upstream ones. A 926-turbine farm at 4.2D spacing will have interior turbines seeing 70–80% of the freestream velocity. Since power scales as v³, that 20–30% velocity deficit translates to 50–65% power loss for those turbines.

METHOD 3 Wake Modeling (hours to days, needs wind distribution + layout)

This is PyWake territory—PyWake is an open-source wind farm simulation tool (developed by DTU Wind Energy) that models how upstream turbines reduce wind speed for downstream ones. You specify the turbine layout, the wind climatology (direction + speed distribution), and a wake deficit model. The simulation propagates wakes through the farm, computing the actual wind speed each turbine sees, and integrates over all wind conditions to produce AEP.

Here’s where v³ bites hardest. Consider a turbine sitting 5D downstream of another in a 9.3 m/s winter wind. The Bastankhah–Porté-Agel Gaussian (bell-curve shaped) deficit model—used in Ma et al. (2025)—predicts the centerline velocity deficit from the wake expansion rate (k* = 0.04, typical for offshore low-turbulence conditions) and the upstream turbine’s thrust coefficient (C_T—a measure of how hard the rotor pushes back against the wind; C_T ≈ 0.78 for the IEA 15 MW reference turbine—the benchmark design used in the Ma et al. study—at 9.3 m/s, which is below rated speed). At 5D downstream, the model gives a 28% velocity deficit.

Your first instinct might be: 28% less wind, 28% less power. But the cubic says otherwise:

Freestream turbine sees 9.3 m/s → P ∝ (9.3)³ = 804
Wake-affected turbine sees 6.7 m/s → P ∝ (6.7)³ = 301

That’s 63% less power, not 28%. The cubic more than doubles the impact of the velocity deficit. And in a dense 926-turbine farm, most interior turbines are wake-affected.

Wake losses for the Scotian Shelf scenarios range from 19% (sparse layout, winter) to 46% (dense layout, summer), according to the Ma et al. (2025) simulations. For Middle Bank specifically, the losses are 22% in winter and 41% in summer. At the high end, nearly half the energy you’d expect from nameplate calculations never materializes—a correction too large for any regulator to wave through on trust.

This is what PyWake computes: the v³-amplified impact of every upstream turbine on every downstream one, integrated over all wind directions and speeds across the full year.

The Ladder

Method	Input	What it captures	What it misses
Nameplate	N, P_rated, assumed CF	Quick plausibility check	Everything about the site
Ginsberg	v_avg, A, EPF	Wind resource physics, v³	Wake interactions, layout effects
PyWake	v(t,θ) (speed × direction), layout, turbine curves	Wake losses, spacing trade-offs	(This is the target capability)

Each method reveals a limitation that motivates the next. And the single thread connecting all three is why power scales as v³—because understanding the cubic relationship tells you why the nameplate method hides so much, why the EPF correction exists, and why wake-induced velocity deficits are so devastating.

That’s what this document builds.

The Starting Point

The power available in wind passing through a turbine’s swept area is:

P = ½ × ρ × A × v³

Where:

ρ = air density (kg/m³)
A = swept area (m²)
v = wind velocity (m/s)

The formula is easy to derive—v appears in the mass flow rate (ρ × A × v) and v² appears in kinetic energy (½ × m × v²), so power scales as v³. The math is straightforward.

What’s less obvious is why we work with power at all. Why not go directly from energy density (½ × ρ × v²) to annual energy production? Why the detour through instantaneous power?

This document develops an intuition for that question.

The Cylinder Mental Model

Imagine standing at a wind turbine and watching air flow through the rotor over an entire year. You could visualize this as an impossibly long cylinder:

Cross-section = the swept area (π × D² / 4)
Length = the total distance air has traveled past the rotor over the year
If the wind blew at a constant 10 m/s for a year, your cylinder would be about 315 million meters long (10 m/s × 31.5 million seconds).

To find the total energy, you might try:

Energy = (energy density) × (volume)

The energy density of moving air is ½ × ρ × v² (joules per cubic meter). The volume is A × L, where L is the cylinder length. Multiply and done?

Not quite. Here’s where it gets awkward.

The Awkwardness: A Cylinder That Won’t Cooperate

The wind doesn’t blow at a constant speed. Your cylinder is made of “slices”—some added during high-wind moments, some during calm. Each slice has its own energy density depending on what v was when that slice passed through.

You might still try to salvage the simple approach:

Energy = (average energy density) × (total volume)

But you can’t cleanly separate these terms.

When v is high:

The cylinder extends faster (more meters of air arriving per second)
Those slices are energy-rich (½ × ρ × v² is large)

When v is low:
The cylinder extends slowly
Those slices are energy-poor
The high-v slices are both thicker (more length added per unit time) and richer (more joules per cubic meter). The low-v slices are both thinner and poorer.

This coupling wrecks any attempt at simple averaging. If you average energy density across time, you underweight the thick, juicy slices. If you try to average across volume, you need v for both terms—energy density (½ × ρ × v²) AND slice thickness (v × dt). Both depend on v, and v is different for every slice. You’re back to needing the full wind record anyway.

Total energy ≟ ½ρ · v̄² · A · v̄ · t = ½ρA · v̄³ · t ← WRONG

Why wrong? Because the cube amplifies differences. A gust at 12 m/s contributes (12)³ = 1,728 to the energy integral, while a lull at 6 m/s contributes only (6)³ = 216. The gust is worth 8× the lull, not 2×. Averaging the wind speed before cubing it buries this asymmetry.

Energy = Σ ½ρA · v(t)³ · Δt ← sum over each hour

The regulator’s takeaway

When a developer reports “mean wind speed 9.3 m/s,” that single number is not enough to evaluate their AEP claim. Two sites with identical means but different variability will produce different amounts of energy—and the gustier site wins, thanks to the v³ amplification.

A Geophysics Parallel: Degrees of Entanglement

To see why this is so stubborn, consider a spectrum of cases from reservoir geophysics:

Core data (you can measure each property independently):

In a layered reservoir, each bed has a permeability (k) and a thickness (h). From core samples, you measure them separately—ruler for thickness, core plug for permeability. A thick layer can have low permeability; a thin layer can have high permeability. They’re independent. Averaging works (arithmetic, harmonic, or geometric depending on flow geometry).

Seismic inversion (the properties are independent, but the measurement tangles them):

Now try to estimate k and h from seismic reflection data. You don’t see them separately anymore. The seismic response convolves them—a thick low-k layer might look like a thin high-k layer. They’re physically independent, but entangled in the measurement. You can try to untangle them, but it’s hard.

Wind (the two properties are the same variable):

Energy density is ½ × ρ × v². Slice thickness is v × dt. Both ARE v. There’s no underlying separation to recover. It’s not that the measurement convolves them—they’re the same variable wearing two hats.

Case	Property vs. Weight	Separable?
Core data	k and h independent, measured separately	Yes
Seismic inversion	k and h independent, convolved in measurement	Hard
Wind	½ρv² and v×dt are both v	Impossible—nothing to untangle

Wind sits at the extreme end: the entanglement isn’t observational, it’s definitional.

The Root Cause: The Carrier IS the Cargo

Most energy delivery systems have a carrier and a cargo that are independent.

The Truck and Coal Analogy

Imagine you’re receiving coal deliveries by truck. Two things determine how much energy arrives per hour:

How fast the trucks arrive (delivery rate)
How much energy is in each truckload (energy content)
These are independent. You could:

Speed up the trucks without changing the coal quality
Switch to higher-grade coal without changing the delivery schedule
Double one while halving the other

The truck’s velocity has nothing to do with the coal’s BTU content. Two separate knobs, two separate decisions.

Concrete examples of this independence:

Slow trucks, high-grade coal: One delivery per week, but it’s anthracite. Few arrivals, lots of BTUs per ton.
Fast trucks, low-grade coal: Ten deliveries per day, but it’s lignite. Frequent arrivals, few BTUs per ton.

Both are perfectly coherent. You could even tune them to deliver the same total energy per month. The truck schedule and the coal grade are set by different people making different decisions—the dispatcher and the mine, say.

This independence is typical of energy delivery systems:

System	Carrier	Cargo
Coal truck	Truck (speed adjustable)	Coal (energy content independent of truck speed)
Power line	Wire (current adjustable)	Electrons (voltage adjustable independently)
Gas pipeline	Pipe flow (rate adjustable)	Gas (BTU content independent of flow rate)

You can speed up delivery without changing what’s being delivered. Two knobs.

Wind Breaks This Independence

Wind is different. There are no trucks. The air’s motion delivers it to you, and the air’s motion is the energy. There is no “air truck” bringing “energy cargo.” The velocity that transports air to your rotor is the same velocity that determines how much kinetic energy that air contains.

Think about what would need to be true for wind to behave like coal trucks: you’d need slow-moving air that somehow contained lots of kinetic energy, or fast-moving air with little energy. That’s a contradiction. The air’s kinetic energy is ½ × m × v², where v is the same velocity that’s bringing it to you.

The impossible wind analogues would be:

Slow breeze carrying “anthracite air” (high energy density)
Fast wind carrying “lignite air” (low energy density)

These don’t exist. There’s no mine selecting the air’s energy grade independently of the velocity that delivers it. The energy grade is v². The dispatcher and the mine are the same person, turning the same knob.

Coal trucks have two degrees of freedom. Wind has one.

One phenomenon, two consequences. One knob.

A Bridge Analogy: The Bullet Conveyor Belt

Imagine a conveyor belt covered with bullets, all pointing at a target. The bullets are arranged in rows across the belt. When they reach the end, they fly off and hit the target.

You have two ways to increase the damage:

Add more bullets per row (wider rows):

Each meter of belt carries more bullets. More bullets hit the target per second. But each bullet hits just as hard as before. Double the bullets per row, double the damage. Simple.

Speed up the belt:

Here’s where it gets strange. Speeding up the belt does two things at once:

Bullets arrive faster (more hits per second)
Each bullet is moving faster when it flies off, so it hits harder (damage per bullet goes up)

You can’t get one without the other. There’s no way to make bullets arrive faster while keeping them gentle, or make them hit harder while keeping arrivals slow. One dial, two consequences.

That’s wind.

Air density and rotor size are like bullets per row—you can adjust them separately. But wind speed is like belt speed. When v goes up:

More air arrives per second (delivery rate, proportional to v)
Each parcel of air carries more punch (energy density, proportional to v²)

Multiply them together: v × v² = v³.

The belt speed controls both how often bullets arrive and how hard they hit. Wind speed controls both how fast air arrives and how much energy it carries. One knob. Two consequences. That’s where the cubic comes from.

This is why v appears twice in the power equation:

Delivery rate (volume flow): A × v
Energy content (energy density): ½ × ρ × v²

Multiply them: ½ × ρ × A × v³

The v² and the v aren’t two separate variables that happen to move together. They’re two aspects of a single physical reality — one velocity, showing up twice in the equation for two different physical reasons. You cannot crank up the delivery rate while holding energy content fixed. The air delivers itself.

The Firehose Intuition

You’re standing in front of a firehose. Someone doubles the water velocity.

You don’t get hit by faster water AND more water as if those were two separate decisions. There’s one dial: velocity. Turning it up necessarily does both:

Each drop hits harder (v²)—because it’s moving faster
More drops arrive per second (v)—because they’re moving faster

Same cause, two consequences.

Total punishment: 4 × 2 = 8×

That’s the v³. Not two correlated effects, but one effect with two faces.

Why Integration Solves the Problem

Given the coupling, how do we actually calculate annual energy production?

Integration refuses to average.

Instead of trying to summarize the whole year with bulk quantities, integration says:

“Fine. I’ll go moment by moment. At this instant, v = 7 m/s. What’s the power? Good. Now the next instant, v = 7.2 m/s. What’s the power? Good. Next…”

At each infinitesimal moment, v is just one number. The coupling is trivially resolved—the same v goes into both the “how fast is the cylinder growing” calculation and the “how rich is this slice” calculation.

Power right now = ½ × ρ × A × v³ right now

No averaging. No untangling. Just one v, doing its two jobs, at this instant.

Then add up all the instants:

Energy = integral of P dt = integral of ½ × ρ × A × v³ dt

The Insight

Integration doesn’t untangle the coupling. It shrinks to a scale where the coupling doesn’t matter—because at an instant, there’s nothing to correlate. There’s just one v, with its two consequences, right now.

The sum of countless “right nows” is your answer.

When Would Averaging Work? A Thought Experiment

To sharpen the intuition, ask: what would need to be true for simple averaging to work?

The Bubble Cylinder

Return to the cylinder mental model, but change one thing. Imagine the cylinder always advances at constant speed—say, 10 m/s, all year. The energy isn’t carried by the air’s motion anymore. Instead, imagine energy as “bubbles” suspended in the air, and what varies moment to moment is the bubble density.

Now you can average:

Energy = (average bubble density) × (fixed volume)

The cylinder grows at a constant rate. Some hours have dense bubbles, some have sparse bubbles, but each hour contributes the same thickness of cylinder. The two terms—total volume and average energy density—are decoupled. Multiply at the end, done.

This is mathematically identical to the coal truck. The carrier (cylinder advancing at constant speed) is independent of the cargo (bubble density). Two knobs.

A Physical Example: Hot Water in a Pipe

What’s a real system with varying carrier speed but constant cargo density?

A pipe delivering hot water. The pump speed varies—sometimes fast, sometimes slow. But the thermal energy per liter is set by the water temperature, say 60 deg C. That’s independent of flow rate.

Flow fast → more liters per second, each at 60 deg C
Flow slow → fewer liters per second, each still at 60 deg C

The energy density (joules per liter, set by temperature) is decoupled from the delivery rate (liters per second, set by pump speed). Two knobs.

You can work with averages:

Energy delivered = (energy per liter) × (total liters delivered)

Or: (constant energy density) × (average flow rate) × (time)

The varying pump speed affects how much volume arrives, but each parcel’s richness is the same regardless of how fast it traveled.

Why Wind Doesn’t Give You This Escape

For wind to behave like hot water, you’d need the air to carry something whose concentration doesn’t depend on wind speed—say, a constant pollen count per cubic meter. Wind speed varies, but pollen density stays fixed. Now the cylinder’s “cargo” is independent of how fast it’s growing. Average pollen density, multiply by total volume, done.

But wind’s kinetic energy doesn’t work this way. The “temperature” of the air—its energy density, ½ × ρ × v²—is its velocity. There’s no separate thermostat. The air’s motion is both the carrier and the cargo.

This is why integration isn’t optional. The coupling between delivery rate and energy content is fundamental to what kinetic energy is. You can’t engineer around it. You can only shrink to instants where there’s nothing to decouple.

Two Paths to the Integral: Measurement vs. Prediction

The integration solution demands that we know v at each instant. In practice, there are two ways to get this:

Path 1: Measure the Wind Record Directly

Deploy instruments and record v(t) over time. For offshore wind, this typically means floating LIDAR (Flidar)—a buoy-mounted remote sensing system that measures wind speed at hub height. A 1-3 year measurement campaign gives you a detailed wind speed record.

With this record, you can:

Bin the data by wind speed (how many hours at 4 m/s, 5 m/s, 6 m/s…)
Calculate power for each bin
Sum to get annual energy production

This is the integral computed directly from measurements.

Path 2: Predict from a Probability Distribution

The Ladder’s Method 2 already used the EPF shortcut. Here we see where it comes from — why the correction factor exists at all. What if you only have the average wind speed at a site? You might know v_avg = 9 m/s from regional data or a short measurement campaign, but not the full distribution.

Here’s the problem: you can’t just compute P = ½ × ρ × A × (v_avg)³.

Because of the v³ nonlinearity, mean(v³) ≠ mean(v)³ — the average of the cubes always exceeds the cube of the average.

The solution: assume a probability distribution for wind speeds. The most common choice is the Rayleigh distribution (a special case of Weibull with shape parameter k=2), which fits many sites reasonably well.

For a Rayleigh distribution, the ratio mean(v³) / mean(v)³ works out to approximately 1.91. This is the Energy Pattern Factor (EPF)—the same EPF we used in the Ladder’s Method 2, now derived from the distribution.

The tradeoff:

Flidar measurement → accurate, site-specific, expensive, time-consuming
EPF prediction → quick, cheap, approximate, assumes Rayleigh distribution holds

For preliminary screening (“Is this site worth investigating?”), the EPF approach is often sufficient. For detailed project assessment and financing, you need the full wind speed distribution — either from a measurement campaign or from validated reanalysis data. The next section shows how that distribution is used.

From Power to Annual Energy Production

In practice, this integral is evaluated using wind speed statistics:

Measure (or model) the distribution of wind speeds at a site—how many hours per year at 4 m/s, at 5 m/s, at 6 m/s, etc.
For each wind speed bin, calculate power using P = ½ × Cp × ρ × A × v³ (where Cp is the turbine’s efficiency, limited by the Betz limit of 59.3%)
Multiply each power by the hours at that wind speed
Sum across all bins

The result is Annual Energy Production (AEP), typically in MWh or GWh per year.

This is the integral in discrete form: breaking the year into bins where v is approximately constant, computing power for each bin, multiplying by time, summing.

The Scaling Relationships (Summary)

Change	Power scales as	Doubling gives you
Wind speed	v³	8x power
Rotor diameter	D²	4x power
Swept area	A	2x power

Why These Matter

The v³ dominates everything. A mediocre turbine at a windy site beats an excellent turbine at a calm site.

Error propagation is brutal. A 10% error in wind speed estimates becomes a ~33% error in power predictions (1.1³ ~ 1.33). This is why wind resource assessment demands years of careful measurement.

Power vs. Energy: Power (watts) is the instantaneous rate—what the physics gives you. Energy (watt-hours) is the accumulated total—what you sell. The bridge between them is integration over time.

The Swept Area Method: The Engineer’s Lever

So v³ dominates the physics. Why do wind energy textbooks make such a fuss about the “swept area method”?

Because you can’t control the wind. You can control the rotor.

The Knobs You Actually Have

When designing or selecting a turbine, you don’t get to dial up v. The wind is what it is at your site. What you can choose is rotor diameter—and through it, swept area.

This makes the D² relationship the engineer’s primary lever:

Rotor diameter	Swept area	Relative power
50 m	~2,000 m²	1x
100 m	~7,900 m²	4x
150 m	~17,700 m²	9x
200 m	~31,400 m²	16x

Going from a 50m rotor to a 200m rotor—a 4x increase in diameter—gives you 16x the power. That’s a big deal.

Why Turbines Keep Getting Bigger

In the 1980s, rotor diameter was about 15 meters. Today’s largest offshore rotors exceed 230 meters. That’s roughly a 15x increase in diameter, which means:

(15)² ~ 225x more swept area
225x more power per turbine (at the same wind speed)

This is why the industry relentlessly pursues larger rotors despite the engineering challenges. The scaling reward is enormous—even though it’s “only” quadratic.

The Terminology Trap

Ginsberg (2019) writes:

“Power increases exponentially with swept area”

This is wrong — the relationship is quadratic, not exponential. But the impulse is understandable: Ginsberg is trying to emphasize that doubling the diameter does far more than double the output.

Better ways to convey the same idea:

“Power scales with the square of rotor diameter—double the diameter, quadruple the output”
“Going from an 80m to a 160m rotor doesn’t double production—it quadruples it”
“The swept area method matters because area is the one variable you actually control”
“Larger rotors capture dramatically more energy” (vague but not wrong)

What to avoid:

“Exponential” (mathematically incorrect—different growth class entirely)
“Increases rapidly” without quantifying (invites misinterpretation)

The Full Picture

The v³ relationship tells you what physics allows. The D² relationship tells you what engineering can capture. Together:

P = ½ × ρ × A × v³ = ½ × ρ × (π × D² / 4) × v³

You can’t change ρ (air density is what it is). You can’t change v (the wind blows as it will). You can change D—and every doubling of diameter buys you a factor of four.

That’s why swept area deserves its own “method” in the textbooks. Not because the scaling is exponential—it isn’t. But because it’s the lever you actually get to pull.

Terminology Note

These relationships are:

Linear in area (P ~ A)
Quadratic in diameter (P ~ D²)
Cubic in velocity (P ~ v³)

None of them are exponential. True exponential growth (P ~ e^x or P ~ 2^x) means the exponent contains the variable. These are polynomial relationships—the variable is in the base, not the exponent.

The distinction matters: exponential functions eventually outgrow any polynomial. Saying “exponential” when you mean “cubic” or “quadratic” isn’t just imprecise—it’s a different class of mathematical behavior.

Key Takeaways

Wind power scales as v³ because velocity does double duty: it determines both how fast air arrives and how much energy that air contains.
The carrier is the cargo. Unlike most energy systems, you can’t decouple delivery rate from energy content. One knob, two consequences.
The cylinder model helps visualize annual energy as a long tube of variable-density air—but the coupling between slice thickness and slice richness prevents simple averaging.
Integration solves this by shrinking to moments where there’s only one v, then summing. It doesn’t untangle the coupling; it sidesteps it.
Power is the physics; energy is the economics. The cubic relationship governs instantaneous extraction. Integration over real wind distributions gives you what the turbine actually produces—and what investors actually care about.
The methods ladder follows from v³. The nameplate method hides the cubic sensitivity inside an assumed capacity factor. The Ginsberg method exposes it through the EPF correction. Wake modeling confronts it directly: a 25% velocity deficit in a wake means (0.75)³ = 42% of undisturbed power. Each method up the ladder gives you more honest engagement with the cubic.

Closing the Loop: Why This Path?

A natural question: why do we go through energy density and power at all? Why not calculate energy directly?

Here’s the logic chain:

Step 1: Energy Density is the Fundamental Physics

The kinetic energy per cubic meter of moving air is:

Energy density = ½ × ρ × v²

This is bedrock—it falls straight out of KE = ½ × m × v².

Step 2: But Energy Density Alone is Stuck

You might want to say:

Total energy = (energy density) × (volume)

But what volume? The air isn’t sitting still. It’s a flow, not a parcel. And worse: when v changes, the energy density changes AND the rate at which volume passes through changes. The carrier-is-the-cargo coupling makes any direct calculation treacherous.

Step 3: Multiply by Flow Rate to Get Power

Introduce the volume flow rate (A × v) and multiply:

Power = (energy density) × (volume flow rate) = ½ × ρ × v² × A × v = ½ × ρ × A × v³

Power is the natural quantity for a continuous flow. It answers: “Right now, at this instant, how much energy per second is passing through?”

Step 4: Power Lets You Work Instant by Instant

This is the key move. At each instant, v is just one number. The coupling that wrecked the cylinder averaging is trivially resolved—there’s nothing to correlate. One v, doing its two jobs (setting energy density AND delivery rate), right now.

No averaging required. No untangling. Just: what’s v? Compute power. Done.

Step 5: Integrate Power Over Time to Get Energy

Sum up the instants:

Energy = integral of P dt = integral of ½ × ρ × A × v³ dt

Each moment contributes its power × its duration. The integral handles the fact that v changes from moment to moment. The result is total energy—MWh, GWh, what you actually sell.

The Path

			
Energy density (½ × ρ × v²)
    |
    v
× flow rate (A × v)
    |
    v
Power (½ × ρ × A × v³)  <-- work instant by instant here
    |
    v
× time (integrate)
    |
    v
Energy (MWh, GWh/year)

		

We don’t go through power because it’s convenient. We go through power because it’s the only clean waypoint when the carrier is the cargo and v won’t hold still.

This is exactly what PyWake does at industrial scale: for each turbine in a 926-unit farm, at each hourly wind condition, it computes the local wind speed (accounting for upstream wakes), evaluates v³, and sums the result. The physics in this document is the physics inside that software.

References

Bastankhah, M. and Porté-Agel, F. (2014). A new analytical model for wind-turbine wakes. Renewable Energy, 70, 116–123. doi:10.1016/j.renene.2014.01.002

Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Ber, H., Dykes, K., Shields, M., Allen, C., and Viselli, A. (2020). Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine. NREL/TP-5000-75698.

Ginsberg, M. (2019). Harness It: Renewable Energy Technologies and Project Development Models Transforming the Grid. Business Expert Press. ISBN: 978-1-63157-931-8.

Ma, Y., Zhai, L., Nickerson, E. C., Bhatt, U. S., Bhatt, M. P., and Lin, H. (2025). Wind data assessment and energy estimation on the Scotian Shelf. Wind Energy Science, 10, 2965–2999. doi:10.5194/wes-10-2965-2025

Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and Réthoré, P.-E. (2019). DTUWindEnergy/PyWake. Zenodo. doi:10.5281/zenodo.2562662

Confidence intervals and prediction intervals in OLS regression: a geoscience worked example

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Posted on March 3, 2026 by matteomycarta

Introduction

I recently released an open source research bullshit detector. I ended up doing some house cleaning in he repo Data-science-tools-petroleum-exploration-and-production. The result is this new notebook — available in a teaching-oriented version and a practitioner-oriented version on GitHub — that walks through the distinction between regression confidence interval (CI) and the prediction interval (PI), using a real petroleum geology dataset.

When you fit an OLS regression to well data and plot the result, the output typically includes an uncertainty band around the regression line. That band can represent two very different questions, depending on how it is computed. One question is: “Where does the average production lie, for wells with a given gross pay?” The other is: “What production should we expect from the next individual well we drill?” These are not the same question, and conflating the two can lead to significantly different conclusions in a drilling decision context.

The two intervals

The confidence interval (CI) captures uncertainty about where the true regression line lies. Because our sample is limited, the estimated line is just one of many possible lines we could have obtained. The CI narrows as sample size increases, and answers: “What is the average production for wells at this gross pay value?”

The prediction interval (PI) captures uncertainty about where a new individual observation will fall. Even if the true regression line were known exactly, individual wells would still scatter around it due to natural variability. The PI always includes that residual scatter on top of parameter uncertainty — so it is always wider than the CI, and retains an irreducible minimum width even with infinite data.

Mathematically, the only difference between the two formulas is a +1 under the square root in the PI expression. That extra 1 represents the variance of a single new observation around the mean — what the notebook calls the irreducible scatter.

In statsmodels, both intervals come out of a single call: results.get_prediction().summary_frame(alpha=0.05), with the CI in columns mean_ci_lower / mean_ci_upper and the PI in obs_ci_lower / obs_ci_upper.

The dataset

The data comes from Lee Hunt’s (2013) paper Many correlation coefficients, null hypotheses, and high value (CSEG Recorder, December 2013). It contains measurements from 21 wells producing from a marine barrier sand, with variables including gross pay (m), porosity-height, position within the reservoir, pressure draw-down, and production in tens of barrels per day. Gross pay is the strongest single predictor of production (r = 0.87), so that is the starting point.

Where the difference matters: economic risk

The practical value of the CI vs. PI distinction becomes concrete when an economic cutoff is added. In the notebook the minimum economic production is set at 20 (tens of bbl/d), and the question is: what minimum gross pay should be required before drilling?

Looking at the regression line alone, ~3.5 m of gross pay looks sufficient — the predicted mean production at that thickness crosses the threshold. But the PI lower bound tells a different story: to have 95% confidence that the next well drilled will exceed the economic cutoff, approximately 12 m of gross pay is needed. The difference between 3.5 m and 12 m is enormous in practical terms — it could determine whether a prospect gets drilled at all. The figure below shows this directly.

Effect of sample size

The analysis is repeated with only 5 wells, representing an early appraisal scenario. The PI widens substantially, and the required minimum gross pay shifts upward again. As Hunt (2013) notes: the path forward is to either accept the uncertainty or work to reduce it — drill more wells, incorporate additional seismic data, and so on.

Adding predictors

In practice, production depends on more than gross pay. Adding Position and Pressure to the model — two physically meaningful predictors — improves R² and reduces the residual standard error. A partial-effect plot (holding Position and Pressure at their mean values, varying Gross pay) shows the multivariate PI is visibly narrower than the bivariate one. The side-by-side comparison carries the title “Adding Predictors Narrows the Prediction Interval.”

Closing

The key point is stated directly in the notebook: when assessing risk for the next well, reach for the PI, not the CI. The regression line and the CI answer a different question than the one a drilling decision requires.

Modernizing Python Code in the AI Era: A Different Kind of Learning

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Posted on January 24, 2026 by matteomycarta

A few years ago I wrote about advancing my Python coding skills after working through a couple of chapters from Daniel Chen’s excellent book Pandas for Everyone. In that post I showed how I improved code I’d written in 2018 for the SEG Machine Learning contest. The original code used unique() to get lists of well names, then looped through with list comprehensions to calculate flagged samples and proportions. The 2020 version replaced all that with groupby() and apply(), making it much more compact and Pythonic. For example, where I’d written a list comprehension like [result_a.loc[result_a.zone==z,'flag'].sum() for z in zones_a], I could now write simply result_a.groupby('zone', sort=False).flag.sum().values. The runtime also improved – from 86ms down to 52ms. I remember being quite happy with how much cleaner and more readable the code turned out, and how the learning from those two chapters made an immediate practical difference.

Recently, I had to modernize the Busting bad colormaps Panel app, which I built back in 2020 to demonstrate colormap distortion artifacts (something that – as you know – I care a lot about). The app had been deliberately frozen in time – I’d pinned specific library versions in the environment file because I knew things would eventually become obsolete, and I wanted it to stay functional for as long as possible without having to constantly fix compatibility issues.

But some of those issues had finally caught up with me, and the app had ben down for soem time. Last fall, working with Github copilot, I fixed some matplotlib 3.7+ compatibility problems – replace the deprecated cm.register_cmap() with plt.colormaps.register(), fix anrgb2gray error, and resolve a ValueError in the plotting functions.

But the deployment was also broken. In 2021, mybinder.org had switched to JupyterLab as the default interface, changing how apps needed to be deployed. Panel developers had to adapt their code to work with this new setup. The old Panel server URL pattern no longer worked. I tried to figure out the new URL pattern by browsing through the Binder documentation, but I couldn’t make sense of it and failed miserably. It was a short-lived effort that pushed me toward trying something different: full-on coding with Claude Opus 4.5 using Copilot in VSCode.

That’s what allowed me, this month, to complete the modernization process (though honestly, we still haven’t fully sorted out a Binder timeout issue).

A step back to 2020: Building the app from scratch

When I originally built the colormap app, I coded everything myself, experimenting with Panel features I’d never used before, figuring out the supporting functions and visualizations. I also got very good advice from the Panel Discourse channel when I got stuck.

One issue I worked on was getting the colormap collection switching to behave properly. After the first collection switch, the Colormaps dropdown would update correctly, but the Collections dropdown would become non-responsive. With help from experts on the Discourse channel, I figured out how to fix it using Panel’s param.Parameterized class structure.

2026: Working with Claude

The second, and hardest part of the modernization was done almost entirely by Claude Opus. Here’s what that looked like in practice:

Binder deployment: Claude independently figured out the new JupyterLab URL pattern (?urlpath=lab/tree/NotebookName.ipynb instead of the old ?urlpath=%2Fpanel%2FNotebookName). Only later, when fact-checking for this post, did we discover the history of Binder’s 2021 switch to JupyterLab and how Panel had to adapt. This helped, though we’re still working through some timeout issues.

Environment upgrade: Claude upgraded to Python 3.12 and Panel 1.8.5, bringing everything up to modern versions. The key packages are now Panel 1.8.5, param 2.3.1, and bokeh 3.8.1.

Code modernization: Claude spotted and fixed deprecated API calls – the style parameter for Panel widgets became styles.

Collection switching – Claude’s breakthrough: This was Claude’s biggest solo contribution. The collection switching broke during the update, and Claude independently diagnosed that the class-based param.Parameterized approach that had worked in Panel 0.x wasn’t reliable in Panel 1.x. Without me having to guide the solution, Claude figured out how to rewrite it using explicit widgets with param.watch callbacks.

The comparison shows the change:

The new approach uses explicit widget objects with callback functions, which works more reliably in Panel 1.x than the class-based parameterized approach.

New features: Claude integrated two new colormap collections I’d been wanting to add for years – Fabio Crameri’s scientific colormaps (cmcrameri) and Kristen Thyng’s cmocean colormaps. That brought the total from 3 to 5 colormap collections.

Here are examples of the app showing each of the new collections:

The app testing of **cmocean deep** colormap

The app testing of Crameri’s **batlow** colormap

Documentation: Claude updated the README with detailed step-by-step Binder instructions, added a troubleshooting section, and created a table documenting all five colormap collections.

I provided the requirements and guidance throughout, but I almost never looked at the implementation details – what I’ve taken to calling the “bits and bobs” of the code. I focused on what I needed to happen, Claude figured out how to make it happen.

What changed (and what didn’t)

I still understand what the code does conceptually. I can read it, review it, check that it’s correct. I know why we needed to move from Parameterized classes to explicit widgets, and I understand the reactive programming model. But I didn’t write those lines myself.

The work happens at a different level now. I bring the domain expertise (what makes a good colormap visualization), the requirements (needs to deploy on Binder, needs these specific colormap collections), and the quality judgment (that widget behavior isn’t quite right). Claude brings the implementation knowledge, awareness of modern best practices, and the ability to quickly adapt code patterns to new frameworks.

This is really different from my 2020 experience. Back then, working through those Pandas patterns taught me techniques I could apply to other projects. Now, I’m learning what becomes possible when you can clearly articulate requirements and delegate the implementation.

The honest trade-off

There’s a trade-off here, and I’m trying to be honest about it. In 2020, working through the Panel widget patterns taught me things that stuck. In 2026, I got working, modernized code in a fraction of the time, but with less hands-on knowledge of Panel 1.x internals.

For this particular project, that trade-off made sense. I needed a working app deployed and accessible, not deep expertise in Panel migration patterns. But I’m conscious that I’m optimizing for different outcomes now: shipping features fast versus building deep technical understanding through hands-on work.

What this means going forward

After years of writing code line by line, this new way of working feels both efficient and different. I got more done in a couple of hours than I might have accomplished in several weeks working solo. The app is modernized, deployed, working better than ever, and even has new features I’d been wanting to add for years.

This has been a gamechanger for how I work. I still do the work that matters most to me: seeing the tool gap, coming up with the vision, iteratively prototyping to flesh out what I actually need. That’s substantial work, and it’s mine. But after that initial phase? A lot of the implementation will be done with Claude. The app is done and it’s great, and I know this is the path forward for me.

References

Chen, D.Y. (2018). Pandas for Everyone: Python Data Analysis. Addison-Wesley Professional.

Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation and StagLab 3.0. Geoscientific Model Development, 11, 2541-2562. https://www.fabiocrameri.ch/colourmaps/

Niccoli, M. (2020). Keep advancing your Python coding skills. MyCarta Blog. https://mycartablog.com/2020/10/22/keep-advancing-your-python-coding-skills/

Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M., and DiMarco, S.F. (2016). True colors of oceanography: Guidelines for effective and accurate colormap selection. Oceanography, 29(3), 9-13. https://matplotlib.org/cmocean/

Try the app yourself: The modernized colormap distortion app is available on GitHub and you can run it in Binder without installing anything.

The value of intellectual play: Mill, machine learning, and a drilling problem I couldn’t stop thinking about

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Posted on January 20, 2026 by matteomycarta

A few years back, I watched a CSEG talk by Lee Hunt (then at Jupiter Resources) called Value thinking: from the classical to the hyper-modern. One case study in particular stuck with me—so much so that I ended up exploring it in a Jupyter Lab notebook, bringing it up in a job interview, and eventually testing whether an AI could reason through it on its own.

This post is about that journey. It’s also about what happens when you let yourself get genuinely curious about someone else’s problem. And—fair warning—it involves a 19th-century philosopher, a seven-well dataset, and a neural network that learned to distrust AVO attributes.

The problem

Jupiter Resources had a history of occasionally encountering drilling trouble in the Wilrich reservoir—specifically, loss of circulation when encountering large systems of open fractures. Mud loss. The kind of problem that can cost you a well.

They had done extensive geophysical work with multiple seismic attributes that, in theory, should correlate with fractures: Curvature, Coherence, AVAz (amplitude variation with azimuth), VVAZ (velocity variation with azimuth), and Diffraction imaging. But they lacked direct calibration data for the drilling problem, and some of the attributes were giving conflicting results.

Lee Hunt, who led the team and the geophysical work, suspected from the start that the AVO-based attributes might be compromised. He had seen evidence as far back as 2014 that AVAz and VVAZ responses in the Wilrich were dominated by an overlying coal, not the fractures themselves—the attributes were measuring a different geological signal entirely. Diffraction imaging was planned early as a complementary measure, precisely because it might not be affected by the coals in the same way (personal communication).

Seven wells. Five attributes. Four of the wells had experienced drilling problems; three had not. Here’s the data:

The question: which attribute—or combination—could reliably predict drilling problems, so that future wells could be flagged ahead of time?

Mill’s Methods: 19th-century philosophy meets drilling risk

Rather than accept uncertainty and provide no geophysical guidance at all, the team at Jupiter tried something different: Mill’s Methods of Induction. Their goal was to find a pattern that could help them advise the operations team—flag high-risk well locations ahead of time so contingency plans could be in place. Mill’s Methods are a set of logical procedures for identifying causal relationships, laid out by philosopher John Stuart Mill in 1843. They’re often illustrated with a food poisoning example (who ate what, who got sick), but they work just as well here.

This approach was characteristic of Lee Hunt’s attitude toward quantitative geophysics—an attitude I had come to admire through his other work. A few years earlier, he had published a CSEG Recorder column called “Many correlation coefficients, null hypotheses, and high value,” a tutorial on statistics for geophysicists that included synthetic production data and an explicit invitation: “You can do it, too. Write in to tell us how.”

I took him up on it. I worked through his examples in Jupyter notebooks, built visualizations, explored prediction intervals, learned a good deal of scientific computing along the way. I reached out to him about the work. I even wrote up some of that exploration in a blog post on distance correlation and variable clustering—the kind of technical deep-dive where you’re learning as much about the tools as about the data. That extended engagement gave me a feel for his way of thinking: understand the statistics, accept the uncertainty, improve your techniques if you can—but don’t just throw up your hands when the data is messy.

Method of Agreement: Look at all the problem wells (A, B, F, G). What do they have in common? Curvature is TRUE for all four. So is Diffraction imaging. The other attributes vary.

Method of Difference: Compare problem wells to non-problem wells (C, D, E). Neither Curvature nor Diffraction alone perfectly discriminates—Well E has Curvature TRUE but no problem; Well D has Diffraction TRUE but no problem.

Joint Method: But here’s the key insight—Curvature AND Diffraction together form a perfect discriminator. Every well where both are TRUE had problems. Every well where at least one is FALSE did not.

This wasn’t a claim about causation. It was a decision rule: when the next well location shows both high curvature and diffraction anomalies, flag it as elevated risk and ensure contingency protocols are in place.

The logic is sound because of asymmetric costs. Preparing for mud loss (having lost circulation material on site, adjusting mud weight plans) is a minor expense. Not preparing when you should have—that’s where you lose time, money, sometimes the well. You don’t need certainty to justify preparation. You need a defensible signal.

What a neural network learned

I wanted to see if a data-driven approach would arrive at the same answer. Looking at the table myself, and spending some time applying Mill’s Methods, I had already seen the pattern—Curvature and Diffraction together were the key predictors. But I was curious: what would a simple neural network learn on its own?

I trained a two-layer network (no hidden layer)—mathematically equivalent to logistic regression—on the same seven wells. (Yes, seven wells. I know. But stay with me.)

The network classified all seven wells correctly. But the real insight came from the weights it learned:

Attribute	Weight
Curvature	+14.6
Diffraction	+9.7
Coherence	~0
AVAz	−4.9
VVAZ	−14.5

Curvature and Diffraction were strongly positive—predictive of problems. Coherence contributed almost nothing. But AVAz and VVAZ were negative—the network learned to suppress them.

A way to think about negative weights: imagine training a network to identify ducks from a set of photos that includes birds, ducks, and people in duck suits. The network will learn to weight “duck features” positively, but also to weight “human features” negatively—to avoid being fooled by the costumes. In the Wilrich case, the AVAz and VVAZ attributes were like duck suits: they looked like fracture indicators, but they were actually measuring something else.

This was interesting. All five attributes have theoretical justification for detecting fractures. Why would the network actively discount two of them?

When I mentioned this result to Lee Hunt, he confirmed what he had long suspected (personal communication): the AVAz and VVAZ responses in the Wilrich were dominated by an overlying coal, not the fractures themselves. He had measured this effect and documented it in a 2014 paper, where multiple attributes—including AVAz—showed statistically significant correlations to coal thickness rather than to reservoir properties. The neural network had learned, from just seven data points, to suppress exactly the attributes that Lee’s domain knowledge had already flagged as problematic.

This is Mill’s Method of Residues in action: if you know something else causes an observation, subtract it out. And it’s a reminder that domain knowledge and data-driven methods can converge on the same answer when both are applied honestly. I found this deeply satisfying.

What the AI got right—and what it missed

More recently, I revisited this problem using ChatGPT with the Wolfram plugin. I wanted to see if an AI, given just the table and a prompt about Mill’s Methods, could reason its way to the same conclusions.

It did—mechanically. It correctly identified Curvature and Diffraction as the consistent factors among problem wells. It noted that neither attribute alone was a perfect discriminator. It even offered to run logistic regression.

But it missed the interpretive leap. It hedged with phrases like “although there are exceptions” when in fact there were no exceptions to the conjunction rule. And it didn’t articulate the pragmatic framing: that the goal wasn’t to find the true cause, but to build a defensible decision rule under uncertainty.

That framing—the shift from epistemology to operations—required domain knowledge and judgment. The AI could apply Mill’s Methods. It couldn’t tell me why that application was useful here.

Drafting this post, I worked with a different AI—Claude—and found the collaboration more useful in a different way: not for solving the problem, but for reflection. Having to explain the context, the history, the why of my interest helped me articulate what I’d been carrying around in my head for years. Sometimes the value of a thinking partner isn’t in the answers, but in the questions that force you to be clearer.

Why this stuck with me

I’ll be honest: I kept thinking about this problem for years. It became part of a longer arc of engagement with Lee’s work—first the statistics tutorial, then the Wilrich case study, each building on the last.

When I interviewed for a geophysics position (Lee was retiring, and I was a candidate for his role), I mentioned this case study. I pulled out a pen and paper and wrote the entire seven-well table from memory. They seemed impressed—not because memorizing a table is hard, but because it signaled that I’d actually enjoyed thinking about it. That kind of retention only happens when curiosity is real.

I didn’t get the job. The other candidate had more operational experience, and that was the right call. But the process was energizing, and I’m sure that enthusiasm carried into my next opportunity, where I landed happily and stayed for over six years.

I tell this not to brag, but to make a point: intellectual play compounds. You don’t always see the payoff immediately. Sometimes you explore a problem just because it’s interesting—because someone like Lee writes “You can do it, too” and you decide to take him seriously—and it pays dividends in ways you didn’t expect.

The convergence

Three very different approaches—19th-century inductive logic, a simple neural network, and (later) an AI assistant—all pointed to the same answer: Curvature and Diffraction predict drilling problems in this dataset. The AVO attributes are noise, or worse, misleading.

When three methods converge, you can trust the signal. And you can make decisions accordingly.

That’s the real lesson here: rigorous reasoning under uncertainty isn’t about finding the One True Cause. It’s about building defensible heuristics, being honest about what you don’t know, and updating as new data comes in. Mill understood this in 1843. A neural network can learn it from seven wells. And sometimes, so can an AI—with a little help.

I hope you enjoyed this as much as I enjoyed putting it together.

The original case study was presented by Lee Hunt in his CSEG talk “Value thinking: from the classical to the hyper-modern.” The neural network analysis is in my Geoscience_ML_notebook_4. Lee documented the coal correlation issue in Hunt et al., “Precise 3D seismic steering and production rates in the Wilrich tight gas sands of West Central Alberta” (SEG Interpretation, May 2014), and later reflected on confirmation bias as an obstacle to recognizing such issues in “Useful Mistakes, Cognitive Biases and Seismic” (CSEG Recorder, April 2021). My thanks to Lee for the original inspiration, for confirming the geological context, and for sharing the original presentation materials.

References and Links

Hunt, L., 2013, Many correlation coefficients, null hypotheses, and high value: CSEG Recorder, December 2013. Link
Hunt, L., S. Hadley, S. Reynolds, R. Gilbert, J. Rule, M. Kinzikeev, 2014, Precise 3D seismic steering and production rates in the Wilrich tight gas sands of West Central Alberta: SEG Interpretation, May 2014.
Hunt, L., 2021, Useful Mistakes, Cognitive Biases and Seismic: CSEG Recorder, April 2021.
My neural network analysis: Geoscience_ML_notebook_4
My earlier exploration of Lee’s production data: Data exploration in Python: distance correlation and variable clustering
ChatGPT + Wolfram session on Mill’s Methods: Gist

AI/HI Transparency Statement Modified from Brewin http://www.theguardian.com/books/2024/apr/04/why-i-wrote-an-ai-transparency-statement-for-my-book-and-think-other-authors-should-too

Has any text been generated using AI?	Yes
Has any text been improved or corrected using HI?	Yes

Additional context: This post emerged from a conversation with Claude AI (Anthropic). I provided the source materials (a ChatGPT + Wolfram session, a Jupyter notebook, personal history with the problem), direction, and editorial judgment throughout. Claude drafted the post based on these inputs and our discussion of structure, voice, and framing. I reviewed multiple draft, revised as needed, rewrote some key sections, and made all final decisions about what went to publication. The core analysis—Mill’s Methods, the neural network, the interpretation—was done by me years before this collaboration; the AI’s role was in helping articulate and structure that work for a blog audience.

Geoscience Machine Learning bits and bobs – data completeness

Posted on September 19, 2020 by matteomycarta

2016 Machine learning contest – Society of Exploration Geophysicists

In a previous post I showed how to use pandas.isnull to find out, for each well individually, if a column has any null values, and sum to get how many, for each column. Here is one of the examples (with more modern, pandaish syntax compared to the example in the previous post:

for well, data in training_data.groupby('Well Name'): 
print(well)
print (data.isnull().values.any())
print (data.isnull().sum(), '\n')

Simple and quick, the output showed met that – for example – the well ALEXANDER D is missing 466 samples from the PE log:

ALEXANDER D
True
Facies         0
Formation      0
Well Name      0
Depth          0
GR             0
ILD_log10      0
DeltaPHI       0
PHIND          0
PE           466
NM_M           0
RELPOS         0
dtype: int64

A more appealing and versatile alternative, which I discovered after the contest, comes with the matrix function form the missingno library. With the code below I can turn each well into a Pandas DataFrame on the fly, then a missingno matrix plot.

for well, data in training_data.groupby('Well Name'): 

msno.matrix(data, color=(0., 0., 0.45)) 
fig = plt.gcf()
fig.set_size_inches(20, np.round(len(data)/100)) # heigth of the plot for each well reflects well length 
axes=fig.get_axes()
axes[0].set_title(well, color=(0., 0.8, 0.), fontsize=14, ha='center')

In each of the following plots, the sparklines at the right summarizes the general shape of the data completeness and points out the rows with the maximum and minimum nullity in the dataset. This to me is a much more compelling and informative way to inspect log data as it shows the data range where data is missing. The well length is also annotated on the bottom left, by which information I learned that Recruit F9 is much shorter than the other wells. And to ensure this is reinforced I introduced a line to modify each plot so that its height reflects the length. I really like it!

2020 Machine Predicted Lithology – FORCE

Since I am taking part in this year’s FORCE Machine Predicted Lithology challenge, I decided to take the above visualization up a notch. Using Ipywidget’s interactive and a similar logic (in this case data['WELL'].unique()) the tool below allows browsing wells using a Select widget and check the chosen well’s curves completeness, on the fly. You can try the tool in this Jupyter notebook.

In a second Jupyter notebook on the other hand, I used missingno matrix to make a quick visual summary plot of the entire dataset completion, log by log (all wells together):

Then, to explore in more depth the data completion, below I also plotted the library’s dendrogram plot. As explained in the library’s documentation, The dendrogram uses a hierarchical clustering algorithm (courtesy of Scipy) to bin variables against one another by their nullity correlation (measured in terms of binary distance). At each step of the tree the variables are split up based on which combination minimizes the distance of the remaining clusters. The more monotone the set of variables, the closer their total distance is to zero, and the closer their average distance (the y-axis) is to zero.

I find that looking at these two plots provides a very compelling and informative way to inspect data completeness, and I am wondering if they couldn’t be used to guide the strategy to deal with missing data, together with domain knowledge from petrophysics.

Interpreting the dendrogram in a top-down fashion, as suggested in the library documentation, my first thoughts are that this may suggest trying to predict missing values in a sequential fashion rather than for all logs at once. For example, looking at the largest cluster on the left, and starting from top right, I am thinking of testing use of GR to first predict missing values in RDEP, then both to predict missing values in RMED, then DTC. Then add CALI and use all logs completed so far to predict RHOB, and so on.

Naturally, this strategy will need to be tested against alternative strategies using lithology prediction accuracy. I would do that in the context of learning curves: I am imagining comparing the training and crossvalidation error first using only non NaN rows, then replace all NANs with mean, then compare separately this sequential log completing strategy with an all-in one strategy.

Busting bad colormaps with Python and Panel

Posted on September 17, 2020 by matteomycarta

I have not done much work with, or written here on the blog about colormaps and perception in quite some time.

Last spring, however, I decided to build a web-based app to show the effects of using a bad colormaps. This stemmed from two needs: first, to further my understanding of Panel, after working through the awesome tutorial by James Bednar, Panel: Dashboards (at PyData Austin 2019); and second, to enable people to explore interactively the effects of bad colormaps on their perception, and consequently the ability to on interpret faults on a 3D seismic horizon.

I introduced the app at the Transform 2020 virtual subsurface conference, organized by Software Underground last June. Please watch the recording of my lightning talk as it explains in detail the machinery behind it.

I am writing this post in part to discuss some changes to the app. Here’s how it looks right now:

The most notable change is the switch from one drop-down selector to two-drop-down selectors, in order to support both the Matplotlib collection and the Colorcet collection of colormaps. Additionally, the app has since been featured in the resource list on the Awesome Panel site, an achievement I am really proud of.

You can try the app yourself by either running the notebook interactively with Binder, by clicking on the button below:

or, by copying and pasting this address into your browser:

https://mybinder.org/v2/gh/mycarta/Colormap-distorsions-Panel-app/master?urlpath=%2Fpanel%2FDemonstrate_colormap_distortions_interactive_Panel

Let’s look at a couple of examples of insights I gained from using the app. For those that jumped straight to this example, the top row shows:

the horizon, plotted using the benchmark grayscale colormap, on the left
the horizon intensity, derived using skimage.color.rgb2gray, in the middle
the Sobel edges detected on the intensity, on the right

and the bottom row, shows:

the horizon, plotted using the Matplotlib gist_rainbow colormap, on the left
the intensity of the colormapped, in the middle. This is possible thanks to a function that makes a figure (but does not display it), plots the horizon with the specified colormap, then saves plot in the canvas to an RGB numpy array
the Sobel edges detected on the colormapped intensity, on the right

I think the effects of this colormaps are already apparent when comparing the bottom left plot to the top left plot. However, simulating perception can be quite revealing for those that have not considered these effects before. The intensity in the bottom middle plot is very washed out in the areas corresponding to green color in the bottom left, and as a result, many of the faults are not visible any more, or only with much difficulty, which is demonstrated by the Sobel edges in the bottom right.

And if you are not quite convinced yet, I have created these hill-shaded maps, using Matt Hall”s delightful function from this notebook (and check his blog post):

Below is another example, using the Colocrcet cet_rainbow which is is one of Peter Kovesi’s perceptually uniform colormaps. I use many of Peter’s colormaps, but never used this one, because I use my own perceptual rainbow, which does not have a fully saturated yellow, or a fully saturated red. I think the app demonstrate, that even though they are more subtle , this rainbow still is introducing some artifacts. The yellow colour creates narrow flat bands, visible in the intensity and Sobel plots, and indicated by yellow arrows; the red colour is also bad as usual, causing an artificial decrease in intensity(magenta arrows).

Be a geoscience and data science detective

Featured

Posted on September 16, 2020 by matteomycarta

September 16, 2020

Introduction

These days everyone talks about data science. But here’s a question: if you are a geoscientist, and like me you have some interest in data science (that is, doing more quantitative and statistical analyses with data), why choose between the two? Do both… always! Your domain knowledge is an indispensable condition, and so is an attitude of active curiosity (for me, an even more important condition). So, my advice for aspiring geoscientists and data scientists is: “yes, do some course work, read some articles, maybe a book, get the basics of Python or R, if you do not know a programming language” but then jump right ahead into doing! But – please! – skip the Titanic, Iris, Cars datasets, or any other data from your MOOC or that many have already looked at! Get some data that is interesting to you as geoscientist, or put it together yourself.

Today I will walk you through a couple of examples; the first one, I presented as a lightning talk at the Transform 2020 virtual conference organized by Software Underground. This project had in fact begun at the 2018 Geophysics Sprint (organized by Agile Scientific ahead of the Annual SEG conference) culminating with this error flag demo notebook. Later on, inspired by watching the Stanford University webinar How to be a statistical detective, I decided to resume it, to focus on a more rigorous approach. And that leads to my last bit of advice, before getting into the details of the statistical analysis: keep on improving your geo-computing projects (if you want more advice on this aspect, take a look at my chapter from the upcoming Software Underground book, 52 things you should know about geocomputing): it will help you not only showcase your skills, but also your passion and motivation for deeper and broader understanding.

First example: evaluate the quality of seismic inversion from a published article

In this first example, the one presented at Transform 2020, I wanted to evaluate the quality of seismic inversion from a published article in the November 2009 CSEG Recorder Inversion Driven Processing. In the article, the evaluation was done by the authors at a blind well location, but only qualitatively, as illustrated in Figure 5, shown below for reference. In the top panel (a) the evaluation is for the inversion without additional processing (SPNA, Signal Protected Noise Attenuation); in the bottom panel (b) the evaluation is for the inversion with SPNA. On the right side of each panel the inverted seismic trace is plotted against the upscaled impedance well log (calculated by multiplying the well density log and the well velocity log from compressional sonic); on the right, the upscaled impedance log is inserted in a seismic impedance section as a colored trace (at the location of the well) using the same color scale and range used for the impedance section.

Figure 5 caption: Acoustic impedance results at the blind well for data without (a) and with (b) SPNA. The figure shows a 200 ms window of inverted seismic data with well B, the blind well, in the middle on the left, along with acoustic impedance curves for the well (red) and inverted seismic (blue) on the right. The data with SPNA shows a better fit to the well, particularly over the low frequencies.

What the authors reported in the figure caption is the extent to which the evaluation was discussed in the paper; unfortunately it is not backed up in any quantitative way, for example comparing a score, such as R^2, for the two methods. Please notice that I am not picking on this paper in particular, which in fact I rather quite liked, but I am critical of the lack of supporting statistics, and wanted to supplement the paper with my own.

In order to do that, I hand-digitized from the figure above the logs and inversion traces , then interpolated to regularly-sampled time intervals (by the way: if you are interested in a free tool to digitize plots, check use WebPlotDigitizer).

My plan was to split my evaluation in an upper and lower zone, but rather than using the seismically-picked horizon, I decided to add a fake top at 1.715 seconds, where I see a sharp increase in impedance in Figure 5. This was an arbitrary choice on my part of a more geological horizon, separating the yellow-red band from the green blue band in the impedance sections. The figure below shows all the data in a Matplotlib figure:

The first thing I did then, was to look at the Root Mean Square Error in the upper and lower zone obtained using the fake top. They are summarized in the table below:

Based on the RMSE , it looks like case b, the inversion with Signal Protected Noise Attenuated applied on the data, is a better result for the Upper zone, but not for the Lower one. This result is in agreement with my visual comparison of the two methods.

But lets’ dig a bit deeper. After looking at RMSE, I used the updated version of the error_flag function, which I first wrote at the 2018 Geophysics Sprint, listed below:

def error_flag(pred, actual, stat, dev = 1.0, method = 1):
    """Calculate the difference between a predicted and an actual curve 
    and return a curve flagging large differences based on a user-defined distance 
    (in deviation units) from either the mean difference or the median difference
    
Matteo Niccoli, October 2018. Updated in May 2020.
    
    Parameters:
        predicted : array
            predicted log array           
        actual : array
            original log array            
        stat : {‘mean’, ‘median’}
            The statistics to use. The following options are available:
                - mean: uses numpy.mean for the statistic, 
                and np.std for dev calculation
                - median: uses numpy.median for the statistic, 
                and scipy.stats.median_absolute_deviation (MAD) for dev calculation        
        dev : float, optional
            the standard deviations to use. The default is 1.0           
        method : int {1, 2, 3}, optional
            The error method to use. The following options are available
            (default is 1):
                1: difference between curves larger than mean difference plus dev
                2: curve slopes have opposite sign (after a 3-sample window smoothing)
                3: curve slopes of opposite sign OR difference larger than mean plus dev  
    Returns:
        flag : array
        The error flag array
    """   
    
    flag = np.zeros(len(pred))
    err = np.abs(pred-actual)
    
    if stat == 'mean':
        err_stat = np.mean(err)
        err_dev = np.std(err)
    elif stat == 'median':
        err_stat = np.median(err)
        err_dev = sp.stats.median_absolute_deviation(err)
        
    pred_sm = pd.Series(np.convolve(pred, np.ones(3), 'same'))
    actual_sm = pd.Series(np.convolve(actual, np.ones(3), 'same'))
    ss = np.sign(pred_sm.diff().fillna(pred_sm))
    ls = np.sign(actual_sm.diff().fillna(actual_sm))
                  
    if method == 1:
        flag[np.where(err>(err_stat + (dev*err_dev)))] = 1
    elif method == 2:      
        flag[np.where((ss + ls)==0 )]= 1
    elif method == 3:
        flag[np.where(np.logical_or(err>(err_stat + (dev*err_dev)), (ss+ls)==0 ))]= 1
    return flag

I believe this new version is greatly improved because:

Users now can choose between mean/standard deviation and median/median absolute deviation as a statistic for the error. The latter is more robust in the presence of outliers
I added a convolutional smoother prior to the slope calculation, so as to make it less sensitive to noisy samples
I expanded and improved the doctstring

The figure below uses the flag returned by the function to highlight areas of poorer inversion results, which I assigned based on passed to the function very restrictive parameters:

using median and a median absolute deviation of 0.5 to trigger flag
combining the above with checking for the slope sign

I also wrote short routines to count the number and percentage of samples that have been flagged, for each result, which are summarized in the table below:

The error flag method is in agreement with the RMS result: case b, the inversion on Signal Protected Noise Attenuated data is a better result for the Upper zone, but for the Lower zone the inversion without SPNA is the better one. Very cool!

But I was not satisfied yet. I was inspired to probe even deeper after a number of conversations with my friend Thomas Speidel, and reading the chapter on Estimation from Computational and Inferential Thinking (UC Berkeley). Specifically, I was left with the question in mind: “Can we be confident about those two “is better“ in the same way?

This question can be answered with a bootstrapped Confidence Interval for the proportions of flagged samples, which I do below using the code in the book, with some modifications and some other tools from the datascience library. The results are shown below. The two plots, one for the Upper and one for the Lower zone, show the distribution of bootstrap flagged proportions for the two inversion results, with SPNA in yellow, and without SPNA in blue, respectively, and the Confidence Intervals in cyan and brown, respectively (the CI upper and lower bounds are also added to the table, for convenience).

By comparing the amount (or paucity) of overlap between the distributions (and between the confidence intervals) in the two plots, I believe I can be more confident in the conclusion drawn for the Lower zone, which is that the inversion on data without SPNA is better (less proportion of flagged errors), as there is far less overlap.

I am very pleased with these results. Of course, there are some caveats to keep in mind, mainly that:

I may have introduced small, but perhaps significant errors with hand digitizing
I chose a statistical measure (the median and median absolute deviation) over a number of possible ones
I chose an arbitrary geologic reference horizon without a better understanding of the reservoir and the data, just the knowledge from the figure in the paper

However, I am satisfied with this work, because it points to a methodology that I can use in the future. And I am happy to share it! The Jupyter notebook is available on GitHub.

Second example: evaluate regression results from a published article

My second, more recent example, is shorter but no less interesting, in my opinion. It is a case study correlating inversion data to petrophysical estimates of porosity-height in the Doig and Montney Formations in Western Canada (from the paper Tight gas geophysics: AVO inversion for reservoir characterization, Close et al. CSEG Recorder, May 2010, an article which I enjoyed very much reading).

The authors indicated that Vp/Vs and/or Poisson’s ratio maps from seismic inversion are good indicators of porosity in the Lower Doig and Upper Montney reservoirs in the wells used in their study, so it was reasonable to try to predict Phi-H from Vp/Vs via regression. The figure below, from the article, shows one such Vp/Vs ratio map and the Vp/Vs vs. Phi-H cross-plot for 8 wells.

Figure 7 caption: Figure 7. a) Map of median Vp/Vs ratio value and porosity-height from 8 wells through the Lower Doig and Upper Montney. The red arrows highlight wells with very small porosity-height values and correspond in general to areas of higher Vp/Vs ratio. The blue arrow highlights a well at the edge of the seismic data where the inversion is adversely affected by decreased fold. The yellow line is the approximate location of a horizontal well where micro-seismic data were recorded during stimulation. b) Cross-plot of porosity-height values as shown in (a) against Vp/Vs extracted from the map. The correlation co-efficient of all data points (blue line) of 0.7 is improved to 0.9 (red line) by removing the data point marked by the blue arrow which again corresponds to the well near the edge of the survey (a).

They also show in the figure that by removing one of the data points, corresponding to a well near the edge of the survey (where the seismic inversion result is presumably not as reliable, due to lower offset and azimuth coverage), the correlation co-efficient is improved from 0.7 to 0.9 (red line).

So, the first thing I set out to do was to reproduce the crossplot. I again hand-digitized the porosity-height and Vp/Vs pairs in the cross-plot using again WebPlotDigitizer. However, switched around the axes, which seems more natural to me since the objectives of regression efforts would be to predict as the dependent variable Phi-h, at the location of yet to drill wells, given Vp/Vs from seismic inversion. I also labelled the wells using their row index, after having loaded them in a Pandas DataFrame. And I used Ordinary Least Square Regression from the statsmodels library twice: once with all data points, the second time after removal of the well labelled as 5 in my plot above.

So, I am able to reproduced the analysis from the figure. I think removing an outlier with insight from domain knowledge (the observation that poorer inversion result at this location is reasonable for the deviation from trend) is a legitimate choice. However, I would like to dig a bit deeper, to back up the decision with other analyses and tests, and to show how one might do it with their own data.

The first thing to look at is an Influence plot, which is a plot of the residuals, scaled by their standard deviation, against the leverage, for each observation. Influence plots are useful to distinguish between high leverage observations from outliers and are one of statsmodel ‘s standard Regression plots, so we get the next figure almost for free, with minor modifications to the default example). Here it is below, together with the OLS regression result, with all data points.

From the Influence plot it is very obvious that the point labelled as zero has high leverage (but not high normalized residual). This is not a concern because points with high leverage are important but do not alter much the regression model. On the other hand, the point labelled as 5 has very high normalized residual. This point is an outlier and it will influence the regression line, reducing the R^2 and correlation coefficient. This analysis is a robust way to legitimize removing that data point.

Next I run some inferential tests. As I’ve written in an earlier notebook on Data loading, visualization, significance testing, I find the critical r very useful in the context of bivariate analysis. The critical r is the value of the correlation coefficient at which you can rule out chance as an explanation for the relationship between variables observed in the sample, and I look at it in combination with the confidence interval of the correlation coefficient.

The two plots below display, in dark blue and light blue respectively, the upper and lower confidence interval bounds, as the correlation coefficient r varies between 0 and 1 (x axis). These two bounds will change with different number of wells (they will get closer with more wells, and farther apart with less wells). The lower bound intersects the x axis (y=0) axis at a value equal to the critical r (white dot). The green dots highlight the actual confidence interval for a specific correlation coefficient chosen, in this case 0.73 with 9 wells, and 0.9 with 8 wells.

By the way: these plots are screen captures from the interactive tool I built taking advantage of the Jupyter interactive functionality (ipywidgets). You can try the tool by running the Jupyter notebook.

With 9 wells, and cc=0.73, the resulting critical r = 0.67 tells us that for a 95% confidence level (0.05 alpha) we need at least a correlation coefficient of 0.67 in the sample (the 9 wells drilled) to be able to confidently say that there is correlation in the population (e.g. any well, future wells). However, the confidence interval is quite broad, ranging between 0.13 and 0.94 (you can get these numbers by running confInt(0.73, 9) in a cell.

With 8 wells (having removed the outlier), CC=0.9, the critical r is now 0.71, meaning that the requirement for rejecting the the null hypothesis (there is no association between Vp/Vs and Phi-H) is now a bit higher. However, a CC increased to 0.9, and only one less well, also results in a confidence interval ranging from 0.53 to 0.98, hence our confidence is greatly increased.

This second analysis also corroborates the choice of removing the outlier data point. One thing worth mentioning before moving on to the next test is that these confidence interval bounds are the expected population ones, based on the sample correlation coefficient and the number of observations; the data itself was not used. Of course, I could also have calculated, and shown you, the OLS regression confidence intervals, as I have done in this notebook (with a different dataset).

My final test involved using the distance correlation (dcor.distance_correlation) and p-value (dcor.independence.distance_covariance_test) from the dcor library. I have written before in a blog post how much I like the distance correlation, because it does not assume a linear relationship between variables, and because a distance correlation of zero does mean that there is no dependence between those two variables (contrary to Pearson and Spearman). In this case the relationship seems to be linear, but it is still a valuable test to compare DC and p-value before and after removing the outlier. Below is a summary of the test:

All data points:
D.C. =  0.745
p-value =  0.03939

Data without outlier:
D.C. =  0.917
p-value =  0.0012

The distance correlation values are very similar to the correlation coefficients from OLS, again going up once removed the outlier. But, more interestingly, with all data points, a p-value of 0.04079 is very close to the alpha of 0.05, whereas once we removed the outlier, the p-value goes down by a factor of 20, to 0.0018. This again backs up the decision to remove the outlier data point.

Upcoming book: 52 things you should know about Geocomputing

Posted on January 30, 2020 by matteomycarta

I am very excited to write that, after a successful second attempt at collecting enough essays, the book 52 Things You Should Know About Geocomputing, by Agile Libre, is very likely to become a reality.

* July 2020 UPDATE *

This project got a much needed boost during the hackathon at the 2020 Transform virtual event. Watch the video recording on the group YouTube channel here.

*********************

In one of the three chapter I submitted for this book, Prototype colourmaps for fault interpretation, I talk about building a widget for interactive generation of grayscale colourmaps with sigmoid lightness. The whole process is well described in the chapter and showcased in the accompanying GitHub repo.

This post is an opportunity for me to reflect on how revisiting old projects is very important. Indeed, I consider it an essential part of how I approach scientific computing, and a practical way to incorporate new insights, and changes (hopefully betterments) in your coding abilities.

In the fist version of the Jupyter notebook, submitted in 2017, all the calculations and all the plotting commands where packed inside a single monster function that was passed to ipywidgets.interact. Quite frankly, as time passed this approach seemed less and less Phytonic (aka mature) and no longer representative of my programming skills and style, and increased understanding of widget objects.

After a significant hiatus (2 years) I restructured the whole project in several ways:
– Converted Python 2 code to Python 3
– Created separate helper functions for each calculation and moved them to the top to improve on both clarity and reusability of the code.
– Improved and standardized function docstrings
– Optimized and reduced the number of parameters
– Switched from interact to interactive to enable access to the colormaparray in later cells (for printing, further plotting, and exporting).

The new notebook is here.

In a different chapter in the book I talk at more length about the need to Keep on improving your geocomputing projects.

The Three Methods: A Regulator’s Ladder for Evaluating Energy Claims

METHOD 1 Nameplate (30 seconds, back of envelope)

METHOD 2 Ginsberg Swept Area (5 minutes, needs mean wind speed)

METHOD 3 Wake Modeling (hours to days, needs wind distribution + layout)

The Ladder

The Starting Point

The Cylinder Mental Model

The Awkwardness: A Cylinder That Won’t Cooperate

A Geophysics Parallel: Degrees of Entanglement

The Root Cause: The Carrier IS the Cargo

The Truck and Coal Analogy

Wind Breaks This Independence

A Bridge Analogy: The Bullet Conveyor Belt

The Firehose Intuition

Why Integration Solves the Problem

The Insight

When Would Averaging Work? A Thought Experiment

The Bubble Cylinder

A Physical Example: Hot Water in a Pipe

Why Wind Doesn’t Give You This Escape

Two Paths to the Integral: Measurement vs. Prediction

Path 1: Measure the Wind Record Directly

Path 2: Predict from a Probability Distribution

From Power to Annual Energy Production

The Scaling Relationships (Summary)

Why These Matter

The Swept Area Method: The Engineer’s Lever

The Knobs You Actually Have

Why Turbines Keep Getting Bigger

The Terminology Trap

The Full Picture

Terminology Note

Key Takeaways

Closing the Loop: Why This Path?

Step 1: Energy Density is the Fundamental Physics

Step 2: But Energy Density Alone is Stuck

Step 3: Multiply by Flow Rate to Get Power

Step 4: Power Lets You Work Instant by Instant

Step 5: Integrate Power Over Time to Get Energy

The Path

References

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Introduction

The two intervals

The dataset

Where the difference matters: economic risk

Effect of sample size

Adding predictors

Closing

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A step back to 2020: Building the app from scratch

2026: Working with Claude

What changed (and what didn’t)

The honest trade-off

What this means going forward

References

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The problem

Mill’s Methods: 19th-century philosophy meets drilling risk

What a neural network learned

What the AI got right—and what it missed

Why this stuck with me

The convergence

References and Links

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2016 Machine learning contest – Society of Exploration Geophysicists

2020 Machine Predicted Lithology – FORCE

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Introduction

First example: evaluate the quality of seismic inversion from a published article

Second example: evaluate regression results from a published article

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*** July 2020 UPDATE ***

*********************

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* July 2020 UPDATE *