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Category Archives: Geophysics
Android seismograph app evaluation
Introduction
Over the last couple of years I have looked at a number of apps of all sorts. Some were seismometers. Out of those, two had the desired (to me) 3-component recording and export capabilities: Seismometer by Yellowagents, which is for for iPhone, and Seismograph alpha by Calvico, which is for Android and it’s the one I am talking about in this post.
These are several screen captures from Seismograph’s download page.
seismograph alpha
This looked good, so I downloaded it, and played with it a bit.
Functionality
You can change the color of the three components in the settings. I moved away from the default green, red, and blue (below, left) since the green and red can be confusing for color blind viewers, and used magenta, green, and black, which are less confusing (below, right). I also switched from black to white background and increased the saturation of the new colors.
You can also change the recording sensitivity on the fly with the pinch, see below.
Really cool. But after the initial excitement, I moved on. Because really, how many times can you tap the phone from every possible direction, export the data, load it in Excel, stare at it for a while, show it to your colleagues at work, and friends and family at home?
Here comes the dynamite
I recently had an opportunity to go back to this app and record some real data from a dynamite blast in a construction site!
Next to my office in Stavanger they have been building a new condo complex (personal note: as I publish this I am no longer in Stavanger, I moved back to Canada). Of course this being Norway, they soon had to deal with granite, and a whole pile of it.
OK, perhaps not so much rock and not as much as 25 tons of explosive at once, but you can see in the picture below (from Google Street view) that there’s a good amount of rock outcropping behind the parking lot and to the right of the car (and underneath it).
before
All that rock had to go, and so it is that for more than 3 weeks, there were a couple of charges shot every day. This is very good as once I set my mind on recording one shot, it took me a few experiments to get the setup right. But before I get to that, here is a photo I took a few weeks after the works had started, right after I recorded the shot I am using for this post.
after
The next two photos below show, respectively, one of the several excavation fronts, and the big pile of granite removed.
Looking at the excavation front
….
The big pile
Recording of the raw data
As I said it was good there were so many shots as it took me some time to get the right setting and a good recording. The two biggest issues were:
– the positioning of the phone: I started in my office, first placing the phone on the window sill, but I soon realize the ‘coupling’ was not so good as the sill was aluminum and a bit shaky. The floor was a lot better, but I finally tried on cement in the parking lot outside of the building, which proved even better.
– the phone lock: I figured out that the recording stops when the phone locks and it is resumed when you unlock it. The first time this happened right before the shot, which I lost. Also, having to touch your phone to unlock it while recording will result in the recording of a very noisy section.
I finally got to get a good recording. Here’s the raw data for those readers that may want to play with it.
Manipulating the data
This is the part that took me a bit of work. The first thing to do is to generate a time vector from the recorded date column. This is a fairly common task when working with field data. In the code box below I added the first 50 rows of time data. The date, which I called calendar time, in the second column. The first column, with sample number, was not in the recorded data, I added it here for reference. The third column is the output time vector, which is cumulative time. To get to this result I copied the milliseconds only from the calendar time column. I used UltraEdit, my favourite text editor, which allows to work with columns and column portions, not just rows.
Then I used a MS Excel formula to generate the cumulative time vector. Once I got to this I recognized that the sample rate is variable. I added a column called dt, which is the difference between pairs of consecutive samples, to make it more clear. I am not too sure if THIS is a common occurrence or not. In my MSc thesis worked with ORION land seismometers equipped with three-component geophones and had to correct for drift and with dead time (a short interruption in recording every minute due to GPS clock updates) but not with uneven sample rate. Has anyone seen this before?
sample, calendar time, t(ms), dt(ms)
1, 013 06 27 12:19:36.972, 0, 11 2, 013 06 27 12:19:36.983, 11, 5 3, 013 06 27 12:19:36.988, 16, 2 4, 013 06 27 12:19:36.990, 18, 2 5, 013 06 27 12:19:36.992, 20, 3 6, 013 06 27 12:19:36.995, 23, 3 7, 013 06 27 12:19:36.998, 26, 1 8, 013 06 27 12:19:36.999, 27, 2 9, 013 06 27 12:19:37.001, 29, 2 10, 013 06 27 12:19:37.003, 31, 2 11, 013 06 27 12:19:37.005, 33, 2 12, 013 06 27 12:19:37.007, 35, 2 13, 013 06 27 12:19:37.009, 37, 9 14, 013 06 27 12:19:37.018, 46, 2 15, 013 06 27 12:19:37.020, 48, 17 16, 013 06 27 12:19:37.037, 65, 1 17, 013 06 27 12:19:37.038, 66, 2 18, 013 06 27 12:19:37.040, 68, 34 19, 013 06 27 12:19:37.074, 102, 2 20, 013 06 27 12:19:37.076, 104, 2 21, 013 06 27 12:19:37.078, 106, 2 22, 013 06 27 12:19:37.080, 108, 2 23, 013 06 27 12:19:37.082, 110, 2 24, 013 06 27 12:19:37.084, 112, 2 25, 013 06 27 12:19:37.086, 114, 5 26, 013 06 27 12:19:37.091, 119, 2 27, 013 06 27 12:19:37.093, 121, 4 28, 013 06 27 12:19:37.097, 125, 34 29, 013 06 27 12:19:37.131, 159, 5 30, 013 06 27 12:19:37.136, 164, 2 31, 013 06 27 12:19:37.138, 166, 2 32, 013 06 27 12:19:37.140, 168, 4 33, 013 06 27 12:19:37.144, 172, 2 34, 013 06 27 12:19:37.146, 174, 14 35, 013 06 27 12:19:37.160, 188, 5 36, 013 06 27 12:19:37.165, 193, 14 37, 013 06 27 12:19:37.179, 207, 7 38, 013 06 27 12:19:37.186, 214, 11 39, 013 06 27 12:19:37.197, 225, 12 40, 013 06 27 12:19:37.209, 237, 17 41, 013 06 27 12:19:37.226, 254, 2 42, 013 06 27 12:19:37.228, 256, 14 43, 013 06 27 12:19:37.242, 270, 7 44, 013 06 27 12:19:37.249, 277, 11 45, 013 06 27 12:19:37.260, 288, 16 46, 013 06 27 12:19:37.276, 304, 3 47, 013 06 27 12:19:37.279, 307, 14 48, 013 06 27 12:19:37.293, 321, 8 49, 013 06 27 12:19:37.301, 329, 11 50, 013 06 27 12:19:37.312, 340, 7
To get around this I decided to resample the data, which I did in Matlab.
I used a new sample rate of 10 ms after taking the average of all values in the dt column, which turned out to be almost exactly 10 ms. I suspect this may be the ‘nominal’ sample rate for the app, although I could not find confirmation of this anywhere.
Here’s the interpolated time vector (although it is not really interpolated), which I used to resample (this time there was actual interpolation) the x,y, and z components. For those interested, here is the final ASCII file with interpolated x,y,z,time.
First quick look
Below is a plot of the data, which I generated in Matlab. There are two sections of high signal in all three recorded components. The first one is at the beginning of the recording, and it is due to the phone still moving for a couple of seconds after I touched it to star the recording. The second section, right after 2 x 10^4 ms, is the recorded shot.
Future posts
That’s it for this post. In my next post or two I will take a closer look at the data: I will use a tap test to assess polarity, will run a spectral analysis, and try hodogram plots, and hopefully much more.
Related posts (external)
NASA’s Perpetual Ocean animation
A couple of months ago AGU blogger Dan Satterfield posted a great article titled The Turbulence of Van Gogh and the Labrador Shelf Current.
For getting maps and art together, I thought it could not be topped. Then today, I stumbled into NASA’s Perpetual Ocean animation: beautiful!
From the original Source: This is an animation of ocean surface currents from June 2005 to December 2007 from NASA satellites. Watch how bigger currents like the Gulf Stream in the Atlantic Ocean and the Kuroshio in the Pacific carry warm waters across thousands of miles at speeds greater than four miles per hour (six kilometers per hour); how coastal currents like the Agulhas in the Southern Hemisphere move equatorial waters toward Earth’s poles; and how thousands of other ocean currents are confined to particular regions and form slow-moving, circular pools called eddies. Credit: NASA/SVS
Related sites
More media options, including a 20 minute version at 30 fps can be found here
MIT general circulation model MITgcm
ECCO2: Phase II of MIT/JPL project Estimating the Circulation and Climate of the Ocean
Seismic terrain displays
Introduction
A couple of years ago I stumbled in a great 2001 paper by Beyer [1] on The Leading Edge. Being interested in visualization techniques I was drawn by the display in Figure 1 (which is a low resolution copy from Figure 2 in the paper). But what really amazed me was the suggestion that a display like this could be created in a few minutes, without doing any interpretation, by just manipulating instantaneous phase slices. With the only condition of having data of fair quality, this promised to be an awesome reconnaissance tool.
Figure 1 – Copyright SEG
The theory
The idea that instantaneous phase is a great attribute for interpretation has been around for a long time. There is, for example, a 1989 Exploration Geophysics paper by Duff and Mason [2]. These two authors argue that amplitude time slices are a suboptimal choice, and that instantaneous phase slices should be preferred. They give three reasons:
1) on amplitude time slices only relatively strong events remain above the bias level after gain and scaling. Weak events are submerged below the bias and remain unmappable. Although it is the topic of a future post, it is worth mentioning I think this effect is exacerbated by the common but unfortunate choice of a divergent color palette with white in the middle. White is so bright (I call it white hole) that even more low amplitude events become indiscernible.
2) discrete boundaries corresponding to unique positions on the wavelet are displayed on instantaneous phase slices – this intra wavelet detail is lost on amplitude slices.
Figure 2 – after Duff and Mason, Figure 2
3) instantaneous time slices give DIRECTLY the sense of time dip for dipping events. In Figure 2 I show 2 parallel dipping reflectors, represented by 5 (non consecutive) traces, and 2 (non-consecutive) instantaneous phase slices (at arbitrary t1 an t2). I marked 5 discrete phase events for the top dipping reflector. The sense of time dip is given (with appropriate color palette) by the sense of color transition. Conversely, this intra wavelet detail would be lost on the amplitude time slices, with amplitudes between the black center trace and the red traces, and amplitudes between the red traces and the green traces lost within a single broad zone. The difference is probably not as dramatic nowadays with the increase in dynamic ranges available, but using instantaneous phase slices still remains advantageous for detailed mapping.
Beyer’s seismic terrain is just a natural extension of the instantaneous time slice as ( quoted from [1]):”… it then follows that the instantaneous phase (-180 deg to +180 deg) can simply be rescaled to the wavelength in ms of pseudoseismic two-way time… Seismic terrain can be thought of as a type of instantaneous wavelength generated from instantaneous phase along a time slice”. With reference to the top dipping reflector in Figure 3, the method allows generating converting the brown phase segment to the dipping blue segment (and similarly the yellow phase segment to the dipping green segment for bottom dipping event).
Figure 3
The practice – Petrel
Let’s see how we can create a terrain display similar to that in Figure 1 using Petrel.
Raw data
The process starts with migrated seismic data, from which we need generate both the phase and frequency component to get us the instantaneous wavelength.
For this tutorial I use a public seismic dataset (BPA9901) available on the Norwegian Public Data Portal. In Figure 4 below I am showing an amplitude time slice (above the Chalk) from the migrated seismic volume.
Figure 4
Step 1 – generate phase component
The first step is to generate an instantaneous phase attribute volume. This is found in the volume attributes. In Figure 5 below I am showing the instantaneous phase slice corresponding to the amplitude time slice of Figure 4.
*** N.B. *** If significant regional dips are observed in the seismic data, care should be taken in some cases it may be beneficial (please see comment section) to remove them through flattening prior to the terrain generation.
Figure 5
Step 2 – generate frequency component
This is the trickiest part. In theory to get the instantaneous wavelength we would have to calculate the instantaneous frequency and divide the instantaneous frequency attribute can be very noisy and can have spurious values in areas of low amplitude in the input data. A good practical alternative is to measure a single value of the dominant wavelet period T in an area of relatively flat reflections near the zone of interest as I am showing in Figure 6.
For the more avid readers, this is all explained quite nicely in Beyer (quoted from [1]): “Complex trace relationships dictate that the wavelength is the phase component divided by the frequency component. Thus one may be compelled to derive the seismic terrain by dividing the extracted instantaneous phase by the extracted instantaneous frequency (carefully applying unit conversion of 1000 ms/ 360 deg or 2.78). However extracted instantaneous frequency tends to include spurious values in low amplitudes (approaching infinity according to literature and practice which correspond to poor data quality zones. Instantaneous frequency or even averaged instantaneous frequency renders the seismic terrain noisy, unrealistic, and misleading. Years of extensive use have shown that single value of visually estimated dominant wavelet period (i.e. cycles per second) produces a very high-quality seismic terrain that closely fits the seismic events over wide areas”.
Figure 6
Step 3 – generate the instantaneous wavelength (seismic terrain)
Having estimated the dominant wavelet period T (in ms) , I can now use it to generate the instantaneous wavelength. We are essentially converting the data from the range [-180 180] deg to the range [-T/2 T/2] ms.
In Petrel I do it in the calculator with a formula of the type:
terrain=(((instantaneous phase +180)*T/2)/180)
The actual formula used is shown on the top row of Figure 7 below. You will notice that it isn’t exactly the same as the above formula. I added 1 to 180 to avoid division of zero values, e.g.:
(([-180 180]+180)*28/180) = ([0 180]*28/180) = ([0 5040]/180) % not good!
whereas:
(([-180 180]+181)*28/180) = ([1 181]*28/180) = ([28 5068]/180) % good!
Once the division is performed I subtract T/2 again.
Notice from Figure 7 that because we added 181 but divided by 180 there is a small adjustment to be made by hand. I get this small adjustment by double clicking on the output volume to get the statistics. In this case it is +-0.16 ms, so I run a second time the formula (bottom row, Figure 7), this time subtracting T/2 +0.16 instead of just T/2.
Figure 7
Step 4 – display seismic terrain.
There are two options in Petrel to display the resulting seismic terrain volume:
Option 1 – display bump mapped terrain slices in 2D or 3D window
This is my preferred option for scanning up and down through the terrain slices. The bump mapping effect is done by double clicking on the terrain survey with a 2D or 3D window open and selected, in the Style tab>Intersection tab. In Figure 8 I am showing the bump mapped terrain slice corresponding to the instantaneous phase time slice of Figure 5.
Figure 8
Option 2 – display selected horizons of interest in 3D window
One may want to create a display such as the one in Figure 1, which for me is intended for a later stage, when integrating perhaps with extracted amplitude or attribute anomalies highlighting hydrocarbon presence.
As often in Petrel there are different ways of achieving the same result. This is how I do it. First, I create a flat surface, with a TWT value corresponding to the slice I am interested in (as in Figure 9, left panel). Then I extract and append to this surface the terrain values from that slice of interest (as in Figure 9, right panel).
Figure 9
Figure 10
Finally, in the calculations tab, I add a constant time shift corresponding to the time associated with the slice of interest: notice the difference in Z value between the left panel in Figure 10 (before the calculation), and the right panel (after the calculation). It is also necessary to use the extracted value as visual vertical position as illustrated in Figure 11.
Figure 11
I am showing the result in Figure 12. This is the same terrain slice as in Figure 8.
Figure 12
Discussion
As a quick QC I am displaying in Figure 13 a vertical section (corresponding to the thick black line in Figure 12) from the input seismic data, with the extracted surface drawn as a thin black line.
The terrain deteriorates to the far left as we approach the edge of the survey, with fold decreasing and noise increasing, and there is a cycle skip towards the far right. But all in all I think this is a very good result: it captures the faults well, and the whole process took less than 20 minutes with no picking.
Figure 13
Limitations
This method has one limitation: the maximum fault throws or stratigraphic relief (in milliseconds) that can be mapped is equal to the period T.
Acknowledgements
I wish to thank DONG Energy for agreeing to the publication of the seismic images, which were generated using company licensed Petrel.
** UPDATE **
A few readers asked clarifications on what the benefits and potential uses are of using this attribute. The short answer is that this is in fact a pseudo-horizon that tracks dipping seismic events accurately within the range of the period of the dominant frequency period (as seen in Figure 13), which makes it an excellent reconnaissance tool. A good quality first pass map can be made in minutes in areas where detailed mapping can take days. An excellent example is Figure 16 in the original paper [1]. More details can be found in the last two paragraphs of the paper: Reservoir-scale structural data from seismic terrain and Fast 3-D screening.
References
[1] Beyer, L. (2001). – Rapid 3-D screening with seismic terrain: deepwater Gulf of Mexico examples. The Leading Edge, 20 (4), 386–395.
[2] Duff, B.A., and Mason, D.J. (1989) – The instantaneous-phase time slice: A crucial display for enhancing 3-D interpretation. Exploration Geophysics 20 (2) 213 – 217
An introduction to color palettes for seismic amplitude – teaser
Introduction
In a future posts I will take a look at some of the color palettes used for seismic amplitude display, and discuss ways we can design more perceptual and more efficient ones.
For now, I would like to ask readers to look at two sets of seismic images and answer the survey questions in each section. Far from being exhaustive sets, these are meant as a teaser to get a conversation started and exchange opinions and preferences.
Stratigraphic interpretation
The seismic line below is inline 424 from the F3 dataset, offshore Netherlands from the Open Seismic Repository (licensed CC-BY-SA).
I generated an animation, played at 0.5 frames/second, where 8 different color palette are alternated in sequence. Please click on the image to see a full resolution animation. I also generated a 0.25 frame/second version and a 1 frame/second version.
Fault interpretation
The images used to create the panel below are portions of seismic displays kindly provided by Steve Lynch of 3rd Science Solutions, generated using data released by PeruPetro. I am grateful to both.
Thanks to Matt Hall and Evan Bianco of Agile Geoscience for their suggestions.
Color palettes for seismic structure maps and attributes
I created three color palettes for structure maps (seismic horizons, elevation maps, etcetera) and seismic attributes. To read about the palettes please check these previous blog posts:
The rainbow is dead…long live the rainbow! – Part 5 – CIE Lab linear L* rainbow
The rainbow is dead series – Part 7 – Perceptual rainbow palette – the method
The rainbow is dead series – Part 7 – Perceptual rainbow palette – the goodies
The palettes are available as plain ASCII files and also formatted for a number of platforms and software products:
Geosoft
Hampson-Russell
Kingdom
Madagascar
Matlab
OpendTect
Petrel
Seisware
Surfer
Voxelgeo
. Please download them from my Color Palettes page and follow instructions therein.
Enjoy!
Image courtesy of Sergey Fomel of the Madagascar Development blog
Perceptual rainbow palette – the method
With this post I would like to introduce my new, perceptually balanced rainbow color palette. I used the palette for the first time in How to assess a colourmap, an essay I wrote for 52 Things You Should Know About Geophysics, edited by Matt Hall and Evan Bianco of Agile Geoscience.
In my essay I started with the analysis of the spectrum color palette, the default in some seismic interpretation softwares, using my Lightness L* profile plot and Great Pyramid of Giza test surface (see this post for background on the tests and to download the Matlab code). The profile and the pyramid are shown in the top left image and top right image in Figure 1, from the essay.
Figure 1
In the plot the value of L* varies with the color of each sample in the spectrum, and the line is colored accordingly. This erratic profile highlights several issues with spectrum: firstly, the change in lightness is not monotonic. For example it increases from black (L*=0) to magenta [M] then drops from magenta to blue [B], then increases again and so on. This is troublesome if spectrum is used to map elevation because it will interfere with the correct perception of relief, particularly if shading is added. Additionally, the curve gradient changes many times, indicating a nonuniform perceptual distance between samples. There are also plateaus of nearly flat L*, creating bands of constant color (a small one at the blue, and a large one at the green [G]).
The Great Pyramid has monotonically increasing elevation (in feet – easier to code) so there should be no discontinuities in the surface if the color palette is perceptual. However, clearly using the spectrum we have introduced many artificial discontinuities that are not present in the data. For the bottom row in FIgure 1 I used my new color palette, which has a nice, monotonic, compressive Lightness profile (bottom left). Using this palette the pyramid surface (bottom right) is smoothly colored, without any perceptual artifact.
This is how I created the palette: I started with RGB triplets for magenta, blue, cyan, green, and yellow (no red), which I converted to L*a*b* triplets using Colorspace transformations, a Matlab function available on the Matlab File Exchange. I modified the new L* values by fitting them to an approximately cube law L* function (this is consistent with Stevens’ power law of perception), and adjusted a* and b* values using Lab charts like the one in Figure 2 (from CIELab Color Space by Gernot Hoffmann, Department of Mechanical Engineering, University of Emden) to get 5 colors moving up the L* axis along an imaginary spiral (I actually used tracing paper). Then I interpolated to 256 samples using the same ~cube law, and finally reconverted to RGB [1].
Figure 2
There was quite a bit of trial and error involved, but I am very happy with the results. In the animations below I compare the spectrum and the new palette, which I call cubeYF, as seen in CIELab color space. I generated these animations with the method described in this post, using the 3D color inspector plugin in ImageJ:
I also added Matlab’s default Jet rainbow – a reminder that defaults may be a necessity, but in many instances not the ideal choice:
OK, the new palette looks promising, insofar as modelling is concerned. But how would it fare using some real data? To answer this question I used a residual gravity map from my unpublished thesis in Geology at the University of Rome. I introduced this map and discussed the geological context and objectives of the geophysical study in a previous post, so please refer to that if you are curious about it. In this post I will go straight to the comparison of the color palettes; if you are unfamiliar with gravity data, try to imagine negative residuals as elevation below sea level, and positive residuals as elevation above seal level – you won’t miss out on anything.
In Figures 3 to 6 I colored the data using the above three color palettes, and grayscale as benchmark. I generated these figures using Matlab code I shared in my post Visualization tips for geoscientists: Matlab, and I presented three of them (grayscale, Spectrum, and cubeYF) at the 2012 convention of the Canadian Society of Exploration Geophysicists in Calgary (the extended abstract, which I co-authored with Steve Lynch of 3rd Science, is available here).
In Figure 3, the benchmark for the following figures, I use grayscale to represent the data, assigning increasing intensity from most negative gravity residuals in black to most positive residuals in white (as labeled next to the colorbar). Then, I used terrain slope to create shading: the higher the slope, the darker the shading that is assigned, which results in a pseudo-3D display that is very effective (please refer to Visualization tips for geoscientists: Surfer, for an explanation of the method, and Visualization tips for geoscientists: Matlab for code).
Figure 3 – Grayscale benchmark
In Figure 4 I color the pseudo-3D surface with the cubeYF rainbow. Using this color palette instead of grayscale allows viewers to appreciate smaller changes, more quickly assess differences, or conversely identify areas of similar anomaly, while at the same time preserving the peudo-3D effect. Now compare Figure 4 with Figure 5, where we use the spectrum to color the surface: this palette introduces several artefacts (sharp edges and bands of constant hue) which confuse the display and interfere with the perception of pseudo-relief, all but eliminating the effect. For Figure 6 I used Matlab’s default Jet color palette, which is better that the spectrum, and yet the relief effect is somewhat lost (due mainly to a sharp yellow edge and cyan band).
Figure 4 – cube YF rainbow
Figure 5 – Industry spectrum
Figure 6 – Matlab Jet
It looks like both spectrum and jet are poor choices when used for color representation of a surface, with the new color palette a far superior alternative. In the CSEG convention paper mentioned above (available here) Steve and I went further by showing that the spectrum not only has these perceptual artifacts and edges, but it is also very confusing for viewers with deficient color vision, a condition that occurs in about 8% of Caucasian males. We did that using computer software [2] to simulate how viewers with two types of deficient color vision, Deuteranopia and Tritanopia, would see the two colored surfaces, and we compare the results. In other words, we are now able to see the images as they would see them. Please refer to the paper for a full discussion on these simulation.
In here, I show in Figures 7 to 9 the Deuteranope simulations for cubeYF, spectrum, and jet, respectively. In all three simulations the hue discrimination has decreased, but while the spectrum and jet are now even more confusing, the cubeYF has preserved the relief effect.
Deuteranope Simulation of cube YF
Deuteranope Simulation of Industry spectrum
Deuteranope Simulation of Matlab Jet
That’s it for today. In my next post, to be published very shortly, you will get the palette, and a lot more.
References
A more perceptual color palette for structure maps, CSEG/CSPG 2012 convention, Calgary
How to assess a colourmap, in 52 Things You Should Know About Geophysics
Notes
[1] An alternative to the method I used would be to start directly in CIELab color space, and use a some kind of spiral *L lightness profile programmatically. For example:
– Using 3D helical curves from: http://www.mathworks.com/matlabcentral/fileexchange/25177-3d-curves
– Using Archimedes spiral
– Expanding on code by Steve Eddins at Mathworks (A path through L*a*b* color space) in this article , one could create a spiral cube lightness with something like:
%% this creates best-fit pure power law function
% Inspired by wikipedia - http://en.wikipedia.org/wiki/Lightness
l2=linspace(1,power(100,0.42),256);
L2=(power(l2,1/0.42))';
%% this makes cielab real cube function spiral
radius = 50;
theta = linspace(0.6*pi, 2*pi, 256).';
a = radius * sin(theta); b = radius * cos(theta);
Lab1 = [L2, a, b]; RGB_realcube=colorspace('RGB<-Lab',(Lab1));
[2] The simulations are created using ImageJ, an open source image manipulation program, and the Vischeck plug-in. I later discovered Dichromacy, anther ImageJ plug-in for these simulations, which has the advantage of being an open source plugin. They can also be performed on the fly (no upload needed) using the online tool Color Oracle.
Related posts
Do you know any cool apps?
I’d like to pick up my Apps page, which I sort of abandoned a while back.
If you have any great app to recommend, I’d love to hear about it so please add them in the comment section to this post. I am looking for Apps for Android and iPhone/iPad in the following categories – ideally free or very low-cost, possibly open-source:
Geology
Geophysics
Cartography and mapping
Planetary Science
Image Processing
Visualization