Six books about seismic analysis

Last year, I did a round-up of six books about seismic interpretation. A raft of new geophysics books recently, mostly from Cambridge, prompts this look at six volumes on seismic analysis — the more quantitative side of interpretation. We seem to be a bit hopeless at full-blown book reviews, and I certainly haven't read all of these books from cover to cover, but I thought I could at least mention them, and give you my first impressions.

If you have read any of these books, I'd love to hear what you think of them! Please leave a comment. 

Observation: none of these volumes mention compressive sensing, borehole seismic, microseismic, tight gas, or source rock plays. So I guess we can look forward to another batch in a year or two, when Cambridge realizes that people will probably buy anything with 3 or more of those words in the title. Even at $75 a go.

Quantitative Seismic Interpretation

Per Avseth, Tapan Mukerji and Gary Mavko (2005). Cambridge University Press, 408 pages, ISBN 978-0-521-15135-1. List price USD 91, $81.90 at, £45.79 at

You have this book, right?

Every seismic interpreter that's thinking about rock properties, AVO, inversion, or anything beyond pure basin-scale geological interpretation needs this book. And the MATLAB scripts.

Rock Physics Handbook

Gary Mavko, Tapan Mukerji & Jack Dvorkin (2009). Cambridge University Press, 511 pages, ISBN 978-0-521-19910-0. List price USD 100, $92.41 at, £40.50 at

If QSI is the book for quantitative interpreters, this is the book for people helping those interpreters. It's the Aki & Richards of rock physics. So if you like sums, and QSI left you feeling unsatisifed, buy this too. It also has lots of MATLAB scripts.

Seismic Reflections of Rock Properties

Jack Dvorkin, Mario Gutierrez & Dario Grana (2014). Cambridge University Press, 365 pages, ISBN 978-0-521-89919-2. List price USD 75, $67.50 at, £40.50 at

This book seems to be a companion to The Rock Physics Handbook. It feels quite academic, though it doesn't contain too much maths. Instead, it's more like a systematic catalog of log models — exploring the full range of seismic responses to rock properies.

Practical Seismic Data Analysis

Hua-Wei Zhou (2014). Cambridge University Press, 496 pages, ISBN 978-0-521-19910-0. List price USD 75, $67.50 at, £40.50 at

Zhou is a professor at the University of Houston. His book leans towards imaging and velocity analysis — it's not really about interpretation. If you're into signal processing and tomography, this is the book for you. Mostly black and white, the book has lots of exercises (no solutions though).

Seismic Amplitude: An Interpreter's Handbook

Rob Simm & Mike Bacon (2014). Cambridge University Press, 279 pages, ISBN 978-1-107-01150-2 (hardback). List price USD 80, $72 at, £40.50 at

Simm is a legend in quantitative interpretation and the similarly lauded Bacon is at Ikon, the pre-eminent rock physics company. These guys know their stuff, and they've filled this superbly illustrated book with the essentials. It belongs on every interpreter's desk.

Seismic Data Analysis Techniques...

Enwenode Onajite (2013). Elsevier. 256 pages, ISBN 978-0124200234. List price USD 130, $113.40 at £74.91 at

This is the only book of the collection I don't have. From the preview I'd say it's aimed at undergraduates. It starts with a petroleum geology primer, then covers seismic acquisition, and seems to focus on processing, with a little on interpretation. The figures look rather weak, compared to the other books here. Not recommended, not at this price.

NOTE These prices are Amazon's discounted prices and are subject to change. The links contain a tag that gets us commission, but does not change the price to you. You can almost certainly buy these books elsewhere. 


The event that connects like the web

Last week, Matt, Ben, and I attended SciPy 2014, the 13th annual scientific computing with Python conference. On a superficial level, it was just another conference. But there were other elements, brought forth by the organizers and participants (definitely not just attendees) and slowly revealed over the week. Together, the community created the conditions for a truly remarkable experience.

Immutable accessibility

By design, the experience starts before the event, and continues after it is over. Before each of the four half-day tutorials I attended, the instructors posted their teaching materials, code, and setup instructions. Most oral presentations did the same. Most code and content was served through GitHub or Bitbucket and instructions were posted using Mozilla's Etherpad. Ultimately the tools don't matter — it's the intention that is important. Instructors and speakers plan to connect.

Enhancing the being there

Beyond talks and posters, here are some examples of other events that were executed with engagement in mind:

  • Keynote presentations. If a keynote is truly key, design the schedule so that everyone can show up — they're a great way to start the day on a high note.
  • Birds of a Feather sessions are better than a panel discussion or Q&A. Run around with a microphone, and record notes in Etherpad.
  • Lightning talks at the end the day. Anyone can request 5 minutes on a show & tell. It was the first time I've heard applause erupt in the middle of a talk — and it happened several times.
  • Developer sprints take an hour to teach newbies how to become active members of your community or your project. Then spend two-days showing them how you work.

Record all the things

SciPy is not a conference, it's a hypermedia stream that connects networks across organizational boundaries. And it happens in real time — I overheard several people remarking in astonishment that the video of so-and-so's talk earlier that same morning was already posted online. My trained habit of frantic note-taking was redundant, freeing my concentration for more active listening. Instructors and presenters published their media online, and the majority of presenters pulled up interactive iPython notebooks in the browser and executed code on the fly. 

As an example of this, here's Karl Schleicher of Sergey Fomel's group at UT, talking about reproducing the results from a classic paper in The Leading Edge, Spitz (1999)

We need this

On Friday evening Matt remarked to one of the sponsors, "This is the closest thing I have seen to what a conference should be". I think what he meant by that is that it should be about connecting. It should be about pushing our work out to the largest possible scope. It should be open by default, and designed to support ideas and conversations long after it is over. Just like all the things that the web is for as well.

Our question: Can we help SEG, AAPG, or EAGE deliver this to our community? Or do we have to go and build it? 


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!


SciPy will eat the world... in a good way

We're at the SciPy 2014 conference in Austin, the big giant meetup for everyone into scientific Python.

One surprising thing so far is the breadth of science and computing in play, from astronomy to zoology, and from AI to zero-based indexing. It shouldn't have been surprising, as hints at the variety:

There's really nothing you can't do in the scientific Python ecosystem, but this isn't why SciPy will soon be everywhere in science, including geophysics and even geology. I think the reason is IPython Notebook, and new web-friendly ways to present data, directly from the computing environment to the web — where anyone can see it, share it, interact with it, and even build on it in their own work.

Teaching STEM

In Tuesday's keynote, Lorena Barba, an uber-prof of engineering at The George Washington University, called IPython Notebook the killer app for teaching in the STEM fields. She has built two amazing courses in Notebook: 12 Steps to Navier–Stokes and AeroPython (right), and more are on the way. Soon, perhaps through Jupyter CoLaboratory (launching in alpha today), perhaps with the help of tools like Bokeh or mpld3, the web versions of these notebooks will be live and interactive. Python is already the new star of teaching computer science, web-friendly super-powers will continue to push this.

Let's be extra clear: if you are teaching geophysics using a proprietary tool like MATLAB, you are doing your students a disservice if you don't at least think hard about moving to Python. (There's a parallel argument for OpedTect over Petrel, but let's not get into that now.)

Reproducible and presentable

Can you imagine a day when geoscientists wield these data analysis tools with the same facility that they wield other interpretation software? With the same facility that scientists in other disciplines are already wielding them? I can, and I get excited thinking about how much easier it will be to collaborate with colleagues, document our workflows (for others and for our future selves), and write presentations and papers for others to read, interact with, and adapt for their own work.

To whet your appetite, here's the sort of thing I mean (not interactive, but here's the code)...

If you agree that it's needed, I want to ask: What traditions or skill gaps are in the way of this happening? How can our community of scientists and engineers drive this change? If you disagree, I'd love to hear why.


Looking forward to SciPy 2014

This week the Agile crew is at the SciPy conference in Austin, Texas. SciPy is a scientific library for the Python programming language, and the eponymous conference is the annual meetup for the physicists, astonomers, economists — and even the geophysicists! — that develop and use SciPy.

What is SciPy?

Python is an awesome high-level programming language. It's awesome because...

  • Python is free and open source.
  • Python is easy to learn and quite versatile.
  • Python has hundreds of great open source extensions, called libraries.
  • The Python ecosystem is actively developed by programmers at Google, Enthought, Continuum, and elsewhere.
  • Python has a huge and talkative user community, so finding help is easy.

All of these factors make it ideal for crunching and visualizing scientific data. The most important of these is NumPy, which provides efficient linear algebra operations — essential for handling big vectors and matrices. SciPy builds on NumPy to provide signal processing, statistics, and optimization. There are other packages in the same ecosystem for plotting, data management, and so on.

If you follow this blog, you know we have been getting into code lately. We think that languages like Python, GNU Octave, and R (a stastical language) are a core competency for geoscientists. That's why we want to help geoscientists learn Python, and why we organize hackathons, and why we keep going on about it on the blog.

What's going on in Austin?

Technical organizers Katy Huff and Serge Rey have put together a fantastic schedule including 2 days of tutorials (already underway), 3 days of technical talks and posters, and 2 days of sprints (focused coding sessions). Interspersed throughout the talk days are 'Birds of a Feather' meetups for various special-interest groups, and more social gatherings. It's exactly what a scientific conference should be: active learning, content, social, hacking, and unstructured discussion.

Here are some of the things I'm most looking forward to:

If you're interested in hearing about what's going on in this corner of the geophysical and scientific computing world, tune in this week to read more. We'll be posting regularly to the blog, or you can follow along on the #SciPy2014 Twitter hashtag.