# Upscaling geophysical logs with Python using Pandas and Bruges

With a few hours of work last weekend, I finished putting together a Jupyter notebook tutorial, started at the Geophysics Python sprint 2018, demonstrating how to:

• Use Agile Scientific’s `Welly` to load two wells with several geophysical logs
• Use `Pandas``Welly`, and `NumPy` to: remove all logs except for compressional wave velocity (Vp), shear wave velocity (Vs), and density (RHOB); store the wells in individual DataFrames; make the sampling rate common to both wells; check for null values; convert units from imperial to metric; convert slowness to velocity; add a well name column
• Split the DataFrame by well using `unique` values in the well name column
• For each group/well use Agile Scientific’s `Bruges` ‘s `Backus` average to upscale all curves individually
• Add the upscaled curves back to the DataFrame

Matt Hall, (organizer), told me during a breakfast chat on the first day of the sprint that this tutorial would be a very good to have since it is one of the most requested examples by neophyte users of the `Bruges` library; I was happy to oblige.

The code for the most important bit, the last two items in the above list, is included below:

```# Define parameters for the Backus filter
lb = 40   # Backus length in meters
dz = 1.0  # Log sampling interval in meters

# Do the upscaling work
wells_bk = pd.DataFrame()
grouped = wells['well'].unique()
for well in grouped:
new_df = pd.DataFrame()
Vp = np.array(wells.loc[wells['well'] == well, 'Vp'])
Vs = np.array(wells.loc[wells['well'] == well, 'Vs'])
rhob = np.array(wells.loc[wells['well'] == well, 'RHOB'])
Vp_bks, Vs_bks, rhob_bks = br.rockphysics.backus(Vp, Vs, rhob, lb, dz)
new_df['Vp_bk'] = Vp_bks
new_df['Vs_bk'] = Vs_bks
new_df['rhob_bk'] = rhob_bks
wells_bk = pd.concat([wells_bk, new_df])

# Add to the input DataFrame
wells_final = (np.concatenate((wells.values, wells_bk.values), axis=1))
cols = list(wells) + list(wells_bk)
wells_final_df = pd.DataFrame(wells_final, columns=cols)
```

And here is a plot comparing the raw and upscaled Vp and Vs logs for one of the wells:

Please check the notebook if you want to try the full example.

# Geophysics Python sprint 2018 – day 2 and beyond, part II

In the last post I wrote about what Volodymyr and I worked on during a good portion of day two of the sprint in October, and continued to work on upon our return to Calgary.

In addition to that I also continued to work on a notebook example, started in day one, demonstrating on how to upscale sonic and density logs from more than one log at a time using Bruges ‘ `backus`and Panda’s `groupby`. This will be the focus of a future post.

The final thing I did was to write, and test an `error_flag` function for `Bruges`. The function calculates the difference between a predicted and a real curve; it flags errors in prediction if the difference between the curves exceeds a user-defined distance (in standard deviation units) from the mean difference. Another option available is to check whether the curves have opposite slopes (for example one increasing, the other decreasing within a specific interval). The result is a binary error log that can then be used to generate QC plots, to evaluate the performance of the prediction processes in a more (it is my hope) insightful way.

The inspiration for this stems from a discussion over coffee I had 5 or 6 years ago with Glenn Larson, a Geophysicist at Devon Energy, about the limitations of (and alternatives to) using a single global score when evaluating the result of seismic inversion against wireline well logs (the ground truth). I’d been holding that in the back of my mind for years, then finally got to it last Fall.

Summary statistics can also be calculated by stratigraphic unit, as demonstrated in the accompanying Jupyter Notebook.

# Computer vision in geoscience: recover seismic data from images, introduction

In a recent post titled Unweaving the rainbow, Matt Hall described our joint attempt (partly successful) to create a Python tool to enable recovery of digital data from any pseudo-colour scientific image (and a seismic section in particular, like the one in Figure 1), without any prior knowledge of the colormap.

Figure 1. Test image: a photo of a distorted seismic section on my wall.

Please check our GitHub repository for the code and slides and watch Matt’s talk (very insightful and very entertaining) from the 2017 Calgary Geoconvention below:

In the next two post, coming up shortly, I will describe in greater detail my contribution to the project, which focused on developing a computer vision pipeline to automatically detect  where the seismic section is located in the image, rectify any distortions that might be present, and remove all sorts of annotations and trivia around and inside the section. The full workflow is included below (with sections I-VI developed to date):

• I – Image preparation, enhancement:
1. Convert to gray scale
2. Optional: smooth or blur to remove high frequency noise
3. Enhance contrast
• II – Find seismic section:
1. Convert to binary with adaptive or other threshold method
2. Find and retain only largest object in binary image
3. Fill its holes
4. Apply opening and dilation to remove minutiae (tick marks and labels)
• III – Define rectification transformation
1. Detect contour of largest object find in (2). This should be the seismic section.
2. Approximate contour with polygon with enough tolerance to ensure it has 4 sides only
3. Sort polygon corners using angle from centroid
4. Define new rectangular image using length of largest long and largest short sides of initial contour
5. Estimate and output transformation to warp polygon to rectangle
• IV – Warp using transformation
• V – Blanking annotations inside seismic section (if rectangular):
2. Pre-process and apply canny filter
3. Find contours in the canny filter smaller than input size
4. Sort contours (by shape and angular relationships or diagonal lengths)
5. Loop over contours:
1. Approximate contour
2. If approximation has 4 points AND the 4 semi-diagonals are of same length: fill contour and add to mask
• VI – Use mask to remove text inside rectangle in the input and blank (NaN) the whole rectangle.
• VII – Optional: tools to remove arrows and circles/ellipses:
1. For arrows – contours from (4) find ones with 7 sizes and low convexity (concave) or alternatively Harris corner and count 7 corners, or template matching
2. For ellipses – template matching or regionprops
• VIII – Optional FFT filters to remove timing lines and vertical lines

You can download from GitHub all the tools for the automated workflow (parts I-VI) in the module mycarta.py, as well as an example Jupyter Notebook showing how to run it.

The first post focuses on the image pre-processing and enhancement, and the detection of the seismic line (sections I and II, in green); the second one deals with the rectification of the seismic (sections IV to V, in blue). They are not meant as full tutorials, rather as a pictorial road map to (partial) success, but key Python code snippets will be included and discussed.

# Welcome to MyCarta, part II

I started this blog in 2012; in these 3 1/2 years it has been a wonderful way to channel some of my interests in image processing, geophysics, and visualization (in particular colour), and more recently Python.

During this time, among other things, I learned how to build and maintain a blog, I packaged a popular Matlab function, wrote an essay for Agile Geoscience’s first book on Geophysics, presented at the 2012 CSEG Geoconvention, and wrote two tutorials for The Leading Edge. Last, but not least, I made many new friends and professional connections.

Starting with 2016 I would like to concentrate my efforts on building useful (hopefully) and fun (for me at least) open source (this one is for sure) tools in Python. This is going to be my modus operandi:

• do some work, get to some milestones
• upload the relevant IPython/Jupiter Notebooks on GitHub

Here are a couple of examples of ongoing projects:

#### rainbowbot

The idea for this project was inspired by Matt Hall of Agile Geoscience. The plan is to eventually build a web app that will:

#### sketch2model

This is a project started at the 2015 Calgary Geoscience Hackathon organized by Agile Geoscience with Elwyn Galloway, Evan Saltman, and Ben Bougher. The original idea, proposed by Elwyn at the Hackathon, was to make an app that would turn an image of geological sketch into a model, as in the figure below.

The implementation of the finished app involves using morphological filtering and other image processing methods to enhance the sketch image and convert it into a model with discrete bodies, then pass it on to Agile’s modelr.io to create a synthetic.