A focus on building

We've got some big plans for modelr.io, our online forward modeling tool. They're so big, we're hiring! An exhilarating step for a small company. If you are handy with the JavaScript, or know someone who is, scroll down to read all about it!

Here are some of the cool things in Modelr's roadmap:

Interactive 1D models – to support fluid substitution, we need to handle physical properties of pore fluids as well as rocks. Our prototype (right) supports arbitrary layers, but eventually we'd like to allow uploading well logs too.

Exporting models – imagine creating an earth model of your would-be prospect, and sending it around to your asset team to strengthen it's prognosis. Modelr solves the forward problem, PickThis solves the inverse. We need to link them up. We also need SEG-Y export, so you can see your model next to your real data.

Models from sketches – Want to do a quick sketch of a geologic setting, and see what it would look like under the lens of seismic? At the hackathon last month, Matteo Niccoli and friends showed a path to this dream — sketch a picture, take a photo, and upload it to the the app with your phone (right). 

3D models Want to visualize how seismic amplitudes vary according to bed thickness? Build a 2D wedge model and you can analyze a tuning curve. Now, want to explore the same wedge spanning a range of physical properties? That's a job for a 3D wedge model. 

Seismic attributes – Seismic discontinuity attributes, like continuity, or curvature can be ineffective when viewed in cross-section; they're really meant to be shown in time-slices. There is a vast library of attributes and co-rendering technologies we want to provide.

If you get excited about building simple tools on the web for difficult tasks under the ground, we'd love to talk to you. We have an open position for a full-time web developer to help us carry this project forward. Check out the job posting.

The road to Modelr: my EuroSciPy poster

At EuroSciPy recently, I gave a poster-ized version of the talk I did at SciPy. Unlike most of the other presentations at EuroSciPy, my poster didn't cover a lot of the science (which is well understood), or the code (which is esoteric).

Instead it focused on the advantages of spreading software via web applications, rather than only via source code, and on the challenges that we overcame — well, that we're still overcoming — to get our Modelr tool out there. I wanted other programmer-scientists to think about running some of their code as a web app for others to enjoy, but to be aware of the effort involved in doing this.

I've written before about my dislike of posters, though I'm told they are an important component at, say, the AGU Fall Meeting. I admit I do quite like the process of making them, and — on advice from Colin Purrington's useful page — I left a space on the poster for people to write comments or leave sticky notes. As a result, I heard about Docker, a lead I'll certainly follow up,

What's new in modelr

This wasn't part of the poster, but I might as well take the chance to let you know what we've updated recently:

  • You can now add noise to models by specifying the signal:noise.
  • Instead of automatic scaling, you can choose your own gain.
  • The app now returns the elastic moduli of the rocks in the model.
  • You can choose a spatial cross-section view or a space–offset–frequency view.

All of these features are now available to subscribers for only $9/month. Amazing value :)


I've stored my poster on Figshare, a data storage site and part of Macmillan's Digital Science effort. What I love about Figshare, apart from the convenience of cloud-based storage and easy access for others, is that every item gets a digital object identifier or DOI. You've probably seen these on journal articles. They're a bit like other persistent and unique IDs for publications, such as ISBNs for books, but the idea is to provide more interactivity by making it easily linkable: you can get to any object with a DOI by prepending it with "http://dx.doi.org/".


Hall, M (2014). The road to modelr: building a commercial web app on an open source foundation. EuroSciPy, Cambridge, UK, August 29–30, 2014. Poster presentation. DOI:10.6084/m9.figshare.1151653

Graphics that repay careful study

The Visual Display of Quantitative Information by Edward Tufte (2nd ed., Graphics Press, 2001) celebrates communication through data graphics. The book provides a vocabulary and practical theory for data graphics, and Tufte pulls no punches — he suggests why some graphics are better than others, and even condemns failed ones as lost opportunities. The book outlines empirical measures of graphical performance, and describes the pursuit of graphic-making as one of sequential improvement through revision and editing. I see this book as a sort of moral authority on visualization, and as the reference book for developing graphical taste.

Through design, the graphic artist allows the viewer to enter into a transaction with the data. High performance graphics, according to Tufte, 'repay careful study'. They support discovery, probing questions, and a deeper narrative. These kinds of graphics take a lot of work, but they do a lot of work in return. In later books Tufte writes, 'To clarify, add detail.'

A stochastic AVO crossplot

Consider this graphic from the stochastic AVO modeling section of modelr. Its elements are constructed with code, and since it is a program, it is completely reproducible.

Let's dissect some of the conceptual high points. This graphic shows all the data simultaneously across 3 domains, one in each panel. The data points are sampled from probability density estimates of the physical model. It is a large dataset from many calculations of angle-dependent reflectivity at an interface. The data is revealed with a semi-transparent overlay, so that areas of certainty are visually opaque, and areas of uncertainty are harder to see.

At the same time, you can still see every data point that makes the graphic giving a broad overview (the range and additive intensity of the lines and points) as well as the finer structure. We place the two modeled dimensions with templates in the background, alongside the physical model histograms. We can see, for instance, how likely we are to see a phase reversal, or a Class 3 response subject to the physical probability estimates. The statistical and site-specific nature of subsurface modeling is represented in spirit. All the data has context, and all the data has uncertainty.

Rules for graphics that work

Tufte summarizes that excellent data graphics should:

  • Show all the data.
  • Provoke the viewer into thinking about meaning.
  • Avoid distorting what the data have to say.
  • Present many numbers in a small space.
  • Make large data sets coherent.
  • Encourage the eye to compare different pieces of the data.
  • Reveal the data at several levels of detail, from a broad overview to the fine structure.
  • Serve a reasonably clear purpose: description, exploration, tabulation, or decoration.
  • Be closely integrated with the statistical and verbal descriptions of a data set.

The data density, or data-to-ink ratio, looks reasonably high in my crossplot, but it could like still be optimized. What would you remove? What would you add? What elements need revision?

Geophysics at SciPy 2014

Wednesday was geophysics day at SciPy 2014, the conference for scientific Python in Austin. We had a mini-symposium in the afternoon, with 4 talks and 2 lightning talks about posters.

All the talks

Here's what went on in the session...

The talks should all be online eventually. For now, you can watch my talk and Joe's (awesome) talk right here...

And also...

There have been so many other highlights at this amazing conference that I can't resist sharing a couple of the non-geophysical gems...

Last thing... If you use the scientific Python stack in your work, please consider giving as generously as you can to the NumFOCUS Foundation. Support open source!

Cross sections into seismic sections

We've added to the core functionality of modelr. Instead of creating an arbitrarily shaped wedge (which is plenty useful in its own right), users can now create a synthetic seismogram out of any geology they can think of, or extract from their data.

Turn a geologic-section into an earth model

We implemented a color picker within an image processing scheme, so that each unique colour gets mapped to an editable rock type. Users can create and manage their own rock property catalog, and save models as templates to share and re-use. You can use as many or as few colours as you like, and you'll never run out of rocks.

To give an example, let's use the stratigraphic diagram that Bruce Hart used in making synthetic seismic forward models in his recent Whither seismic stratigraphy article. There are 7 unique colours, so we can generate an earth model by assigning a rock to each of the colours in the image.

If you can imagine it, you can draw it. If you can draw it, you can model it.

Modeling as an interactive experience

We've exposed parameters in the interface and so you can interact with the multidimensional seismic data space. Why is this important? Well, modeling shouldn't be a one-shot deal. It's an iterative process. A feedback cycle where you turn knobs, pull levers, and learn about the behaviour of a physical system; in this case it is the interplay between geologic units and seismic waves. 

A model isn't just a single image, but a swath of possibilities teased out by varying a multitude of inputs. With modelr, the seismic experiment can be manipulated, so that the gamut of geologic variability can be explored. That process is how we train our ability to see geology in seismic.

Hart's paper doesn't specifically mention the rock properties used, so it's difficult to match amplitudes, but you can see here how modelr stands up next to Hart's images for high (75 Hz) and low (25 Hz) frequency Ricker wavelets.

There are some cosmetic differences too... I've used fewer wiggle traces to make it easier to see the seismic waveforms. And I think Bruce forgot the blue strata on his 25 Hz model. But I like this display, with the earth model in the background, and the wiggle traces on top — geology and seismic blended in the same graphical space, as they are in the real world, albeit briefly.

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Seismic models: Hart, BS (2013). Whither seismic stratigraphy? Interpretation, volume 1 (1). The image is copyright of SEG and AAPG.

Getting started with Modelr

Let's take a closer look at modelr.io, our new modeling tool. Just like real seismic experiments, there are four components:

  • Make a framework. Define the geometries of rock layers.
  • Make an earth. Assign a set of rock properties to each layer.
  • Make a kernel. Define the seismic survey.
  • Make a plot. Set the output parameters.

Modelr takes care of the physics of wave propagation and reflection, so you don't have to stick with normal incidence acoustic impedance models if you don't want to. You can explore the full range of possibilities.

3 ways to slice a wedge

To the uninitiated, the classic 3-layer wedge model may seem ridiculously trivial. Surely the earth looks more complicated than that! But we can leverage such geometric simplicity to systematically study how seismic waveforms change across spatial and non-spatial dimensions. 

Spatial domain. In cross-section (right), a seismic wedge model lets you analyse the resolving power of a given wavelet. In this display the onset of tuning is marked by the vertical red line, and the thickness at which maximum tuning occurs is shown in blue. Reflection profiles can be shown for any incidence angle, or range of incidence angles (offset stack).

Amplitude versus angle (AVA) domain. Maybe you are working on a seismic inversion problem so you might want to see what a CDP angle gather looks like above and below tuning thickness. Will a tuned AVA response change your quantitative analysis? This 3-layer model looks like a two-layer AVA gather except our original wavelet looks like it has undergone a 90 degree phase rotation. Looks can be deceiving. 

Amplitude versus frequency domain. If you are trying to design a seismic source for your next survey, and you want to ensure you've got sufficient bandwidth to resolve a thin bed, you can compute a frequency gather — right, bottom — and explore a swath of wavelets with regard to critical thickness in your prospect. The tuning frequency (blue) and resolving frequency (red) are revealed in this domain as well. 

Wedges are tools for seismic waveform classification. We aren't just interested in digitizing peaks and troughs, but the subtle interplay of amplitude tuning, and apparent phase rotation variations across the range of angles and bandwidths in the seismic experiment. We need to know what we can expect from the data, from our supposed geology. 

In a nutshell, all seismic models are about illustrating the band-limited nature of seismic data on specific geologic scenarios. They help us calibrate our intuition when bandwidth causes ambiguity in interpretation. Which is nearly all of the time.

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:

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