Calibrate your seismic intuition

On Tuesday we announced our new web app, modelr.io. Why are we so excited about it? 

  • We love the idea that subsurface software can cost dollars, not 1000's of dollars. 
  • We love the idea of subsurface software being online, not on the desktop.
  • We love the idea that subsurface software can be open source. Here's our code!
  • We love the idea of subsurface software that doesn't need a manual to master.
  • We love the idea of subsurface software that runs on a tablet or a phone.
  • We see software as an important way to share knowledge and connect people.

OK, that's enough reasons. There are more. Those are the main ones.

The point is: we love these ideas. And we hope that you, dear reader, at least like some of them a bit. Because we really want to keep developing modelr. We think it can be awesome. Imagine 3D earth models, imagine full waveform modeling, imagine gravity and magnetic models. We get very excited when we think about all the possiblities. There's no better way to calibrate your seismic intuition than modeling, and modelr is a great place to start modeling. 

Here's a challenge: take 3 minutes and see if you can generate...

 A wedge model & tuning curve An AVA gather for a Class 4 sand    A stochastic AVA crossplot          

 modelr seismic wedge modelmodelr seismic avo modelmodelr stochastic avo  model

The most important thing nobody does

A couple of weeks ago, we told you we were up to something. Today, we're excited to announce modelr.io — a new seismic forward modeling tool for interpreters and the seismically inclined.

Modelr is a web app, so it runs in the browser, on any device. You don't need permission to try it, and there's never anything to install. No licenses, no dongles, no not being able to run it at home, or on the train.

Later this week, we'll look at some of the things Modelr can do. In the meantime, please have a play with it.
Just go to modelr.io and hit Demo, or click on the screenshot below. If you like what you see, then think about signing up — the more support we get, the faster we can make it into the awesome tool we believe it can be. And tell your friends!

If you're intrigued but unconvinced, sign up for occasional news about Modelr:

This will add you to the email list for the modeling tool. We never share user details with anyone. You can unsubscribe any time.

Relentlessly practical

This is one of my favourite knowledge sharing stories.

A farmer in my community had a problem with one of his cows — it was seriously unwell. He asked one of the old local farmers about the symptoms, and was told, “Oh yes, one of my herd had the same thing last summer. I gave her a cup of brandy and four aspirins every night for a week.” The young farmer went off and did this, but the poor cow got steadily worse and died. When he saw the old farmer next he told him, more than a little accusingly, “I did what you said, and the cow died anyway.” The old geezer looked into the distance and just said, “Yep, so did mine.”

Incomplete information can be less useful than no information. Yet incomplete information has somehow become our specialty in applied geoscience. How often do we share methods, results, or case studies without the critical details that would make it useful information? That is, not just marketing, or resumé padding. Inded, I heard this week that one large US operator will not approve a publication that does include these critical details! And we call ourselves scientists...

Completeness mandatory

Thankfully, Last month The Leading Edge — the magazine of the SEG — started a new tutorial column, edited by me. Well, I say 'edited', I'm just the person that pesters prospective authors until they give in and send me a manuscript. Tad Smith, Don Herron, and Jenny Kucera are the people that make it actually happen. But I get to take all the credit.

When I was asked about it, I suggested two things:

  1. Make each tutorial reproducible by publishing the code that makes the figures.
  2. Make the words, the data, and the code completely open and shareable. 

To my delight and, I admit, slight surprise, they said 'Sure!'. So the words are published under an open license (Creative Commons Attribution-ShareAlike, the same license for re-use that most of Wikipedia has), the tutorials use open data for everything, and the code is openly available and free to re-use. Complete transparency.

There's another interesting aspect to how the column is turning out. The first two episodes tell part of the story in IPython Notebook, a truly amazing executable writing environment that we've written about before. This enables you to seamlessly stich together text, code, and plots (left). If you know a bit of Python, or want to start learning it right now this second, go give wakari.io a try. It's pretty great. (If you really like it, come and learn more with us!).

Read the first tutorial: Hall, M. (2014). Smoothing surfaces and attributes. The Leading Edge, 33(2), 128–129. doi: 10.1190/tle33020128.1. A version of it is also on SEG Wiki, and you can read the IPython Notebook at nbviewer.org.

Do you fancy authoring something for this column? Wonderful — please do! Here are the author instructions. If you have an idea for something, please drop me a line, let's talk about how to make it relentlessly practical.

A long weekend of Atlantic geology

The Atlantic Geoscience Society Colloquium was hosted by Acadia University in Wolfville, Nova Scotia, this past weekend. It was the 50th Anniversay meeting, and attracted a crowd of about 175 geoscientists. A few members were able to reflect and tell stories first-hand of the first meeting in 1964.

It depends which way you slice it

Nova Scotia is one of the best places for John Waldron to study deformed sedimentary rocks of continental margins and orogenic belts. Being the anniversary, John traced the timeline of tectonic hypotheses over the last 50 years. From his kinematic measurements of Nova Scotia rocks, John showed the complexity of transtensional tectonics. It is easy to be fooled: you will see contraction features in one direction, and extension structures in another direction. It all depends which way you slice it. John is a leader in visualizing geometric complexity; just look at this animation of piecing together a coal mine in Stellarton. Oh, and he has a cut and fold exercise so that you can make your own Grand Canyon! 

The application of the Law of the Sea

In September 2012 the Bedford Institute of Oceanography acquired some multibeam bathymetric data and applied geomorphology equations to extend Canada's boundaries in the Atlantic Ocean. Calvin Campbell described the cruise as like puttering from Halifax to Victoria and back at 20 km per hour, sending a chirp out once a minute, each time waiting for it to go out 20 kilometres and come back.

The United Nation's Convention on the Law of the Sea (UNCLOS) was established to define the rights and responsibilities of nations in their use of the world's oceans, establishing guidelines for businesses, the environment, and the management of marine natural resources. A country is automatically entitled to any natural resources found within a 200 nautical mile limit of its coastlines, but can claim a little bit more if they can prove they have sedimentary basins beyond that. 

Practicing the tools of the trade

Taylor Campbell, applied a post-stack seismic inversion workflow to the Penobscot 3D survey and wells. Compared to other software talks I have seen in industry, Taylor's was a quality piece of integrated technical work. This is even more commendable considering she is an undergraduate student at Dalhousie. My only criticism, which I shared with her after the talk was over, was that the work lacked a probing question. It would have served as an anchor for the work, and I think is one of the critical distinctions between scientific pursuits and engineering.

Image courtesy of Justin Drummond, 2014, personal communication, from his expanded abstract presented at GSA 2013.

Practicing rational inquiry

Justin Drummond's work, on the other hand, started with a nugget of curiosity: How did the biogeochemical cycling of phosphorite change during the Neoproterozoic? Justin's anchoring question came first, only then could he think about the methods, technologies and tools he needed to employ, applying sedimentology, sequence stratigraphy, and petrology to investigate phosphorite accumulation in the Sete Lagoas Formation. He won the award for Best Graduate Student presentation at the conference.

It is hard to know if he won because his work was so good, or if it was because of his impressive vocabulary. He put me in mind of what Rex Murphy would sound like if he were a geologist.

The UNCLOS illustration is licensed CC-BY-SA, by Wikipedia users historicair and MJSmit.

Atlantic geology hits Wikipedia

WikiProject Geology is one of the gathering places for geoscientists in Wikipedia.Regular readers of this blog know that we're committed to open scientific communication, and that we're champions of wikis as one of the venues for that communication, and that we want to see more funky stuff happen at conferences. In this spirit, we hosted a Wikipedia editing session at the Atlantic Geoscience Society Colloquium in Wolfville, Nova Scotia, this past weekend. 

As typically happens with these funky sessions, it wasn't bursting at the seams: The Island of Misfit Toys is not overcrowded. There were only 7 of us: three Agilistas, another consultant, a professor, a government geologist, and a student. But it's not the numbers that matter (I hope), it's the spirit of the thing. We were a keen bunch and we got quite a bit done. Here are the articles we started or built upon:

The birth of the Atlantic Geoscience Society page gave the group an interesting insight into Wikipedia's quality control machine. Within 10 minutes of publishing it, the article was tagged for speedy deletion by an administrator. This sort of thing is always a bit off-putting to noobs, because Wikipedia editors can be a bit, er, brash, or at least impersonal. This is not that surprising when you consider that new pages are created at a rate of about one a minute some days. Just now I resurrected a stripped-down version of the article, and it has already been reviewed. Moral: don't let anyone tell you that Wikipedia is a free-for-all.

All of these pages are still (and always will be) works in progress. But we added 5 new pages and a substantial amount of material with our 28 or so hours of labour. Considering most of those who came had never edited a wiki before, I'm happy to call this a resounding success. 

Much of my notes from the event could be adapted to any geoscience wiki editing session — use them as a springboard to get some champions of open-access science together at your next gathering. If you'd like our help, get in touch.

Rock Hack 2014

We're hosting another hackathon! This time, we're inviting geologists in all their colourful guises to come and help dream up cool tools, find new datasets, and build useful stuff. Mark your calendar: 5 & 6 April, right before AAPG.

On 4 April there's the added fun of a Creative geocomputing course. So you can learn some skills, then put them into practice right away. More on the course next week.

What's a hackathon?

It's not as scary — or as illegal — as it sounds! And it's not just for coders. It's just a roomful of creative geologists and friendly programmers figuring out two things together:

  1. What tools would help us in our work?
  2. How can we build those tools?

So for example, we might think about problems like these:

  • A sequence stratigraphy calibration app to tie events to absolute geologic time
  • Wireline log 'attributes'
  • Automatic well-to-well correlation
  • Facies recognition from core
  • Automatic photomicrograph interpretation: grain size, porosity, sorting, and so on
  • A mobile app for finding and capturing data about outcrops
  • Sedimentation rate analysis, accounting for unconformities, compaction, and grain size

I bet you can think of something you'd like to build — add it to the list!

Still not sure? Check out what we did at the Geophysics Hackathon last autumn...

How do I sign up?

You can sign up for the creative geocomputing course at Eventbrite.

If you think Rock Hack sounds like a fun way to spend a weekend, please drop us a line or sign up at Hacker League. If you're not sure, please come anyway! We love visitors.

If you think you know someone who'd be up for it, let them know with the sharing buttons below.

The poster image is from an original work by Flickr user selkovjr.

January linkfest

Time for the quarterly linkfest! Got stories for next time? Contact us.

BP's new supercomputer, reportedly capable of about 2.2 petaflops, is about as fast as Total's Pangea machine in Paris, which booted up almost a year ago. These machines are pretty amazing — Pangea has over 110,000 cores, and 442 terabytes of memory — but BP claims to have bested that with 1 petabyte of RAM. Remarkable. 

Leo Uieda's open-source modeling tool Fatiando a Terra got an upgrade recently and hit version 0.2. Here's Leo himself demonstrating a forward seismic model:

I'm a geoscientst, get me out of here is a fun-sounding new educational program from the European Geosciences Union, which has recently been the very model of a progressive technical society (along with the AGU is another great example). It's based on the British outreach program, I'm a scientist, get me out of here, and if you're an EGU member (or want to be), I think you should go for it! The deadline: 17 March, St Patrick's Day.

Darren Wilkinson writes a great blog about some of the geekier aspects of geoscience. You should add it to your reader (I'm using The Old Reader to keep up with blogs since Google Reader was marched out of the building). He wrote recently about this cool tool — an iPad controller for desktop apps. I have yet to try it, but it seems a good fit for tools like ArcGIS, Adobe Illustrator.

Speaking of big software, check out Joe Kington's Python library for GeoProbe volumes — I wish I'd had this a few years ago. Brilliant.

And speaking of cool tools, check out this great new book by technology commentator and philosopher Kevin Kelly. Self-published and crowd-sourced... and drawn from his blog, which you can obviously read online if you don't like paper. 

If you're in Atlantic Canada, and coming to the Colloquium next weekend, you might like to know about the wikithon on Sunday 9 February. We'll be looking for articles relevant to geoscientists in Atlantic Canada to improve. Tim Sherry offers some inspiration. I would tell you about Evan's geocomputing course too... but it's sold out.

Heard about any cool geostuff lately? Let us know in the comments. 

6 questions about seismic interpretation

This interview is part of a series of conversations between Satinder Chopra and the authors of the book 52 Things You Should Know About Geophysics (Agile Libre, 2012). The first three appeared in the October 2013 issue of the CSEG Recorder, the Canadian applied geophysics magazine, which graciously agreed to publish them under a CC-BY license.


Satinder Chopra: Seismic data contain massive amounts of information, which has to be extracted using the right tools and knowhow, a task usually entrusted to the seismic interpreter. This would entail isolating the anomalous patterns on the wiggles and understanding the implied subsurface properties, etc. What do you think are the challenges for a seismic interpreter?

Evan Bianco: The challenge is to not lose anything in the abstraction.

The notion that we take terabytes of prestack data, migrate it into gigabyte-sized cubes, and reduce that further to digitized surfaces that are hundreds of kilobytes in size, sounds like a dangerous discarding of information. That's at least 6 orders of magnitude! The challenge for the interpreter, then, is to be darn sure that this is all you need out of your data, and if it isn't (and it probably isn't), knowing how to go back for more.

SC: How do you think some these challenges can be addressed?

EB: I have a big vision and a small vision. Both have to do with documentation and record keeping. If you imagine the entire seismic experiment upon a sort of conceptual mixing board, instead of as a linear sequence of steps, elements could be revisited and modified at any time. In theory nothing would be lost in translation. The connections between inputs and outputs could be maintained, even studied, all in place. In that view, the configuration of the mixing board itself becomes a comprehensive and complete history for the data — what's been done to it, and what has been extracted from it.

The smaller vision: there are plenty of data management solutions for geospatial information, but broadcasting the context that we bring to bear is a whole other challenge. Any tool that allows people to preserve the link between data and model should be used to transfer the implicit along with the explicit. Take auto-tracking a horizon as an example. It would be valuable if an interpreter could embed some context into an object while digitizing. Something that could later inform the geocellular modeler to proceed with caution or certainty.

SC: One of the important tasks that a seismic interpreter faces is the prediction about the location of the hydrocarbons in the subsurface.  Having come up with a hypothesis, how do you think this can be made more convincing and presented to fellow colleagues?

EB: Coming up with a hypothesis (that is, a model) is solving an inverse problem. So there is a lot of convincing power in completing the loop. If all you have done is the inverse problem, know that you could go further. There are a lot of service companies who are in the business of solving inverse problems, not so many completing the loop with the forward problem. It's the only way to test hypotheses without a drill bit, and gives a better handle on methodological and technological limitations.

SC: You mention "absolving us of responsibility" in your article.  Could you elaborate on this a little more? Do you think there is accountability of sorts practiced in our industry?

EB: I see accountability from a data-centric perspective. For example, think of all the ways that a digitized fault plane can be used. It could become a polygon cutting through a surface on map. It could be a wall within a geocellular model. It could be a node in a drilling prognosis. Now, if the fault is mis-picked by even one bin, this could show up hundreds of metres away, depending on the dip of the fault, compared to the prognosis. Practically speaking, accounting for mismatches like this is hard, and is usually done in an ad hoc way, if at all. What caused the error? Was it the migration or was it the picking? Or what about the error in the measurement of the drill-bit? I think accountability is loosely practised at best because we don't know how to reconcile all these competing errors.

Until data can have a memory, being accountable means being diligent with documentation. But it is time-consuming, and there aren’t as many standards as there are data formats.

SC: Declaring your work to be in progress could allow you to embrace iteration.  I like that. However, there is usually a finite time to complete a given interpretation task; but as more and more wells are drilled, the interpretation could be updated. Do you think this practice would suit small companies that need to ensure each new well is productive or they are doomed?

EB: The size of the company shouldn't have anything to do with it. Iteration is something that needs to happen after you get new information. The question is not, "do I need to iterate now that we have drilled a few more wells?", but "how does this new information change my previous work?" Perhaps the interpretation was too rigid — too precise — to begin with. If the interpreter sees her work as something that evolves towards a more complete picture, she needn't be afraid of changing her mind if new information proves us to be incorrect. Depth migration, for example, exemplifies this approach. Hopefully more conceptual and qualitative aspects of subsurface work can adopt it as well.

SC: The present day workflows for seismic interpretation for unconventional resources demand more than the usual practices followed for the conventional exploration and development.  Could you comment on how these are changing?

EB: With unconventionals, seismic interpreters are looking for different things. They aren't looking for reservoirs, they are looking for suitable locations to create reservoirs. Seismic technologies that estimate the state of stress will become increasingly important, and interpreters will need to work in close contact to geomechanics. Also, microseismic monitoring and time-lapse technologies tend to push interpreters into the thick of the operations, which allow them to study how the properties of the earth change according to operations. What a perfect place for iterative workflows.


You can read the other interviews and Evan's essay in the magazine, or buy the book! (You'll find it in Amazon's stores too.) It's a great introduction to who applied geophysicists are, and what sort of problems they work on. Read more about it. 

Join CSEG to catch more of these interviews as they come out. 

Save the samples

A long while ago I wrote about how to choose an image format, and then followed that up with a look at vector vs raster graphics. Today I wanted to revisit rasters (you might think of them as bitmaps, images, or photographs). Because a question that seems to come up a lot is 'what resolution should my images be?' 

Forget DPI

When writing for print, it is common to be asked for a certain number of dots per inch, or dpi (or, equivalently, pixels per inch or ppi). For example, I've been asked by journal editors for images 'at least 200 dpi'. However, image files do not have an inherent resolution — they only have pixels. The resolution depends on the reproduction size you choose. So, if your image is 800 pixels wide, and will be reproduced in a 2-inch-wide column of print, then the final image is 400 dpi, and adequate for any purpose. The same image, however, will look horrible at 4 dpi on a 16-foot-wide projection screen.

Rule of thumb: for an ordinary computer screen or projector, aim for enough pixels to give about 100 pixels per display inch. For print purposes, or for hi-res mobile devices, aim for about 300 ppi. If it really matters, or your printer is especially good, you are safer with 600 ppi.

The effect of reducing the number of pixels in an image is more obvious in images with a lot of edges. It's clear in the example that downsampling a sharp image (a to c) is much more obvious than downsampling the same image after smoothing it with a 25-pixel Gaussian filter (b to d). In this example, the top images have 512 × 512 samples, and the downsampled ones underneath have only 1% of the information, at 51 × 51 samples (downsampling is a type of lossy compression).

Careful with those screenshots

The other conundrum is how to get an image of, say, a seismic section or a map.

What could be easier than a quick grab of your window? Well, often it just doesn't cut it, especially for data. Remember that you're only grabbing the pixels on the screen — if your monitor is small (or perhaps you're using a non-HD projector), or the window is small, then there aren't many pixels to grab. If you can, try to avoid a screengrab by exporting an image from one of the application's menus.

For seismic data, you'd like to capture sample as a pixel. This is not possible for very long or deep lines, because they don't fit on your screen. Since CGM files are the devil's work, I've used SEGY2ASCII (USGS Open File 2005–1311) with good results, converting the result to a PGM file and loading into Gimp

Large seismic lines are hard to capture without decimating the data. Rockall Basin. Image: BGS + Virtual Seismic Atlas.If you have no choice, make the image as large as possible. For example, if you're grabbing a view from your browser, maximize the window, turn off the bookmarks and other junk, and get as many pixels as you can. If you're really stuck, grab two or more views and stitch them together in Gimp or Inkscape

When you've got the view you want, crop the window junk that no-one wants to see (frames, icons, menus, etc.) and save as a PNG. Then bring the image into a vector graphics editor, and add scales, colourbars, labels, annotation, and other details. My advice is to do this right away, before you forget. The number of times I've had to go and grab a screenshot again because I forgot the colourbar...

The Lenna image is from Hall, M (2006). Resolution and uncertainty in spectral decomposition. First Break 24, December 2006, p 43-47.

What is the Gabor uncertainty principle?

This post is adapted from the introduction to my article Hall, M (2006), Resolution and uncertainty in spectral decomposition. First Break 24, December 2006. DOI: 10.3997/1365-2397.2006027. I'm planning to delve into this a bit, partly as a way to get up to speed on signal processing in Python. Stay tuned.


Spectral decomposition is a powerful way to get more from seismic reflection data, unweaving the seismic rainbow.There are lots of ways of doing it — short-time Fourier transform, S transform, wavelet transforms, and so on. If you hang around spectral decomposition bods, you'll hear frequent mention of the ‘resolution’ of the various techniques. Perhaps surprisingly, Heisenberg’s uncertainty principle is sometimes cited as a basis for one technique having better resolution than another. Cool! But... what on earth has quantum theory got to do with it?

A property of nature

Heisenberg’s uncertainty principle is a consequence of the classical Cauchy–Schwartz inequality and is one of the cornerstones of quantum theory. Here’s how he put it:

At the instant of time when the position is determined, that is, at the instant when the photon is scattered by the electron, the electron undergoes a discontinuous change in momen- tum. This change is the greater the smaller the wavelength of the light employed, i.e. the more exact the determination of the position. At the instant at which the position of the electron is known, its momentum therefore can be known only up to magnitudes which correspond to that discontinuous change; thus, the more precisely the position is determined, the less precisely the momentum is known, and conversely. — Heisenberg (1927), p 174-5.

The most important thing about the uncertainty principle is that, while it was originally expressed in terms of observation and measurement, it is not a consequence of any limitations of our measuring equipment or the mathematics we use to describe our results. The uncertainty principle does not limit what we can know, it describes the way things actually are: an electron does not possess arbitrarily precise position and momentum simultaneously. This troubling insight is the heart of the so-called Copenhagen Interpretation of quantum theory, which Einstein was so famously upset by (and wrong about).

Dennis Gabor (1946), inventor of the hologram, was the first to realize that the uncertainty principle applies to signals. Thanks to wave-particle duality, signals turn out to be exactly analogous to quantum systems. As a result, the exact time and frequency of a signal can never be known simultaneously: a signal cannot plot as a point on the time-frequency plane. Crucially, this uncertainty is a property of signals, not a limitation of mathematics.

Getting quantitative

You know we like the numbers. Heisenberg’s uncertainty principle is usually written in terms of the standard deviation of position σx, the standard deviation of momentum σp, and the Planck constant h:

In other words, the product of the uncertainties of position and momentum is small, but not zero. For signals, we don't need Planck’s constant to scale the relationship to quantum dimensions, but the form is the same. If the standard deviations of the time and frequency estimates are σt and σf respectively, then we can write Gabor’s uncertainty principle thus:

So the product of the standard deviations of time, in milliseconds, and frequency, in Hertz, must be at least 80 ms.Hz, or millicycles. (A millicycle is a sort of bicycle, but with 1000 wheels.)

The bottom line

Signals do not have arbitrarily precise time and frequency localization. It doesn’t matter how you compute a spectrum, if you want time information, you must pay for it with frequency information. Specifically, the product of time uncertainty and frequency uncertainty must be at least 1/4π. So how certain is your decomposition?

References

Heisenberg, W (1927). Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik, Zeitschrift für Physik 43, 172–198. English translation: Quantum Theory and Measurement, J. Wheeler and H. Zurek (1983). Princeton University Press, Princeton.

Gabor, D (1946). Theory of communication. Journal of the Institute of Electrical Engineering 93, 429–457.

The image of Werner Heisenberg in 1927, at the age of 25, is public domain as far as I can tell. The low res image of First Break is fair use. The bird hologram is form a photograph licensed CC-BY by Flickr user Dominic Alves