News of the month

The last news of the year. Here's what caught our eye in December.

Online learning, at a price

There was an online university revolution in 2012 — look for Udacity (our favourite), Coursera, edX, and others. Paradigm, often early to market with good new ideas, launched the Paradigm Online University this month. It's a great idea — but the access arrangement is the usual boring oil-patch story: only customers have access, and they must pay $150/hour — more than most classroom- and field-based courses! Imagine the value-add if it was open to all, or free to customers.

Android apps on your PC

BlueStacks is a remarkable new app for Windows and Mac that allows you to run Google's Android operating system on the desktop. This is potentially awesome news — there are over 500,000 apps on this platform. But it's only potentially awesome because it's still a bit... quirky. I tried running our Volume* and AVO* apps on my Mac and they do work, but they look rubbish. Doubtless the technology will evolve rapidly — watch this space. 

2PFLOPS HPC 4 BP

In March, we mentioned Total's new supercomputer, delivering 2.3 petaflops (quadrillion floating point operations per second). Now BP is building something comparable in Houston, aiming for 2 petaflops and 536 terabytes of RAM. To build it, the company has allocated 0.1 gigadollars to high-performance computing over the next 5 years.

Haralick textures for everyone

Matt wrote about OpendTect's new texture attributes just before Christmas, but the news is so exciting that we wanted to mention it again. It's exciting because Haralick textures are among the most interesting and powerful of multi-trace attributes — right up there with coherency and curvature. Their appearance in the free and open-source core of OpendTect is great news for interpreters.

That's it for 2012... see you in 2013! Happy New Year.

This regular news feature is for information only. We aren't connected with any of these organizations, and don't necessarily endorse their products or services. Except OpendTect, which we definitely do endorse.

Seismic texture attributes — in the open at last

I read Brian West's paper on seismic facies a shade over ten years ago (West et al., 2002, right). It's a very nice story of automatic facies classification in seismic — in a deep-water setting, presumably in the Gulf of Mexico. I have re-read it, and handed it to others, countless times.

Ever since, for over a decade, I've wanted to be able to reproduce this workflow. It's one of the frustrations of the non-programming geophysicist that such reproduction is so hard (or expensive!). So hard that you may never quite manage it. Indeed, it took until this year, when Evan implemented the workflow in MATLAB, for a geothermal project. Phew!

But now we're moving to SciPy for our scientific programming, so Evan was looking at building the workflow again... until Paul de Groot told me he was building texture attributes into OpendTect, dGB's awesome, free, open source seismic interpretation tool. And this morning, the news came: OpendTect 4.4.0e is out, and it has Haralick textures! Happy Christmas, indeed. Thank you, dGB.

Parameters

There are 4 parameters to set, other than selecting an attribute. Choose a time gate and a kernel size, and the number of grey levels to reduce the image to (either 16 or 32 — more options might be nice here). You also have to choose the dynamic range of the data — don't go too wide with only 16 grey levels, or you'll throw almost all your data into one or two levels. Only the time gate and kernel size affect the run time substantially, and you'll want them to be big enough to capture your textures. 

Reference
West, B, S May, J Eastwood, and C Rossen (2002). Interactive seismic faces classification using textural attributes and neural networks. The Leading Edge, October 2002. DOI: 10.1190/1.1518444

The seismic dataest is the F3 offshore Netherlands volume from the Open Seismic Repository, licensed CC-BY-SA.

Update on Thursday, December 27, 2012 at 2:42 PM by Matt Hall

It turns out there are a couple of bugs in this early implementation (thanks Paul and Raman at dGB!). It doesn't run on timeslices, and you can't write the volume out to disk yet. But dGB has a rapid development cycle and gets new stuff out quickly via the new installation manager, so we'll continue playing with it and exploring the parameters. In the meantime, there are always workarounds!

Geothermal facies from seismic

Here is a condensed video of the talk I gave at the SEG IQ Earth Forum in Colorado. Much like the tea-towel mock-ups I blogged about in July, this method illuminates physical features in seismic by exposing hidden signals and textures. 

This approach is useful for photographs of rocks and core, for satellite photography, or any geophysical data set, when there is more information to be had than rectangular and isolated arrangements of pixel values.

Click to download slides with notes!Interpretation has become an empty word in geoscience. Like so many other buzzwords, instead of being descriptive and specific jargon, it seems that everyone has their own definition or (mis)use of the word. If interpretation is the art and process of making mindful leaps between unknowns in data, I say, let's quantify to the best of our ability the data we have. Your interpretation should be iteratable, it should be systematic, and it should be cast as an algorithm. It should be verifiable, it should be reproducible. In a word, scientific.  

You can download a copy of the presentation with speaking notes, and access the clustering and texture codes on GitHub. 

Fabric facies

I set out to detect patterns in images, with the conviction that they are diagnostic of more telling physical properties of the media. Tea towel textures can indicate absorbency, durability, state of wear and tear, etc. Seismic textures can indicate things like depositional environment, degree of deformation, lithologic content, and so on:

Facies: A rock or stratified body distinguished from others by its appearance or composition.

Facies are clusters distinguishable in all visual media. Geophysicists shouldn't be afraid of using the word normally reserved by geologists—seismic facies. In the seismic case, instead of lithology, grain size, bedding patterns, and so on, we are using attributes such as amplitude, energy, coherency, and Haralick textures for classification.

The brain is good at pattern recognition and picking out subtleties. I can assign facies to the input data (A), based on on hues (B), or patterns (C). I can also count objects (D), interpret boundaries (E), and identify poorly resolved regions of an image (F) caused by shadows or noise. I can even painstakingly draw the pockmarks or divets in the right hand teatowel (G). All of these elements can be simultaneously held in the mind of the viewer and comprise what we naturally perceive as the properties of visual media. Isolating, extracting, and illustrating these visual features by hand remains tedious.

I am not interested in robot vision so computers can replace geophysical interpreters, but I am interested in how image classification can be used to facilitate, enrich, and expedite the interpretive process. You can probably already think of attributes we can use to coincide with this human interpretation from the examples I gave in a previous post.

Identifying absorbency

Let's set an arbitrary goal of classifying the ability to soak up water, or absorbency. Surely a property of interest to anyone studying porous media. Because absorbency is a media-property, not an optical property (like colour) or a boundary property (like edges), it makes sense to use texture classification. From the input image, I can count 5 different materials, each with a distinct pattern. The least tractable might be the rightmost fabric which has alternating waffle-dimple segments, troublesome shadows and contours, and patterns at two different scales. The test of success is seeing how this texture classification compares to the standard approach of visual inspection and manual picking. 

I landed on using 7 classes for this problem. Two for the white tea-towels, two for the green tea-towel, one for the blue, and one that seems to be detecting shadows (shown in dark grey). Interestingly, the counter top on the far left falls into the same texture class as the green tea-towel. Evidence that texture alone isn't a foolproof proxy for absorbency. To improve the classification, I would need to allow more classes (likely 8 or 9). 

It seems to me that the manual picks match the classification quite well. The picks lack detail, as with any interpretation, but they also lack noise. On the contrary, there are some locations where the classification has failed. It stuggles in low-light and over-exposed regions. 

If you are asking, "is one approach better than the other?", you are asking the wrong question. These are not mutually exclusive approaches. The ideal scenario is one which uses these methods in concert for detecting geologic features in the fabric of seismic data. 

Fabric textures

Beyond the traditional, well-studied attributes that I referred to last time, are a large family of metrics from image processing and robot vision. The idea is to imitate the simple pattern recognition rules our brains intuitively and continuously apply when we look at seismic data: how do the data look? How smooth or irregular are the reflections? If you thought the adjectives I used for my tea towels were ambiguous, I assure you seismic will be much more cryptic.

In three-dimensional data, texture is harder to see, difficult to draw, and impossible to put on a map. So when language fails us, discard words altogether and use numbers instead. While some attributes describe the data at a particular place (as we might describe a photographic pixel as 'red', 'bright', 'saturated'), other attributes describe the character of the data in a small region or kernel ('speckled', 'stripy', 'blurry').

Texture by numbers

I converted the colour image from the previous post to a greyscale image with 256 levels (a bit-depth of 8) to match this notion of scalar seismic data samples in space. The geek speak is that I am computing local grey-level co-occurence matrices (or GLCMs) in a moving window around the image, and then evaluating some statistics of the local GLCM for each point in the image. These statistics are commonly called Haralick textures. Choosing the best kernel size will depend on the scale of the patterns. The Haralick textures are not particularly illustrative when viewed on their own but they can be used for data clustering and classification, which will be the topic of my next post.

• Step 1: Reduce the image to 256 grey-levels
• Step 2: For every pixel, compute a co-occurrence matrix from a p by q kernel (p, q = 15 for my tea towel photo)
• Step 3: For every pixel, compute the Haralick textures (Contrast, Correlation, Energy, Homogeneity) from the GLCM

Textures in seismic data

Here are a few tiles of seismic textures that I have loosely labeled as "high-amplitude continous", "high-amplitude discontinuous", "low-amplitude continuous", etc. You certainly might choose different words to describe them, but each has a unique and objective set of Haralick textures. I have explicitly represented the value of each's texture as a color; using cyan for contrast, magenta for correlation, yellow for energy, and black for homogeneity. Thus, the four Haralick textures span the CMYK color space. Merging these components back together into a single color gives you a sense of the degree of difference across the tiles. For instance, the high-amplitude continuous tile, is characterized by high contrast and high energy, but low correlation, relative to the low-amplitude continuous tile. Their textures are similar, so obviously, they map to similar color values in CMYK color space. Whether or not they are truly discernable is the challenge we offer to data clustering; be it employed by visual inspection or computational force.

Further reading:
Gao, D., 2003, Volume texture extraction for 3D seismic visualization and interpretation, Geophysics, 64, No. 4, 1294-1302
Haralick, R., Shanmugam, K., and Dinstein, I., 1973, Textural features for image classification: IEEE Tran. Systems, Man, and Cybernetics, SMC-3, 610-621.
Mryka Hall-Beyer has a great tutorial at http://www.fp.ucalgary.ca/mhallbey/tutorial.htm for learning more about GLCMs.
Images in this post were made using MATLAB, FIJI and Inkscape.