Machines can read too

The energy industry has a lot of catching up to do. Humanity is faced with difficult, pressing problems in energy production and usage, yet our industry remains as secretive and proprietary as ever. One rich source of innovation we are seriously under-utilizing is the Internet. You have probably heard of it.

Machine experience design

Web sites are just the front-end of the web. Humans have particular needs when they read web pages — attractive design, clear navigation, etc. These needs are researched and described by the rapidly growing field of user experience design, often called UX. (Yes, the ways in which your intranet pages need fixing are well understood, just not by your IT department!)

But the web has a back-end too. Rather than being for human readers, the back-end is for machines. Just like human readers, machines—other computers—also have particular needs: structured data, and a way to make queries. Why do machines need to read the web? Because the web is full of data, and data makes the world go round. 

So website administrators need to think about machine experience design too. As well as providing beautiful web pages for humans to read, they should provide widely-accepted machine-readable format such as JSON or XML, and a way to make queries.

What can we do with the machine-readable web?

The beauty of the machine-readable web, sometimes called the semantic web, or Web 3.0, is that developers can build meta-services on it. For example, a website like that finds the best flights, wherever they are. Or a service that provides charts, given some data or a function. Or a mobile app that knows where to get the oil price. 

In the machine-readable web, you could do things like:

  • Write a program to analyse bibliographic data from SEG, SPE and AAPG.
  • Build a mobile app to grab log mnemonics info from SLB's, HAL's, and BHI's catalogs.
  • Grab course info from AAPG, PetroSkills, and Nautilus to help people find training they need.

Most wikis have a public application programming interface, giving direct, machine-friendly access to the wiki's database. Here are two views of one wiki page — click on the images to see the pages:

At SEG last year, I suggested to a course provider that they might consider offering machine access to their course catalog—so that developers can build services that use their course information and thus send them more students. They said, "Don't worry, we're building better search tools for our users." Sigh.

In this industry, everyone wants to own their own portal, and tends to be selfish about their data and their users. The problem is that you don't know who your users are, or rather who they could be. You don't know what they will want to do with your data. If you let them, they might create unimagined value for you—as does for airlines with reasonable prices, good schedules, and in-flight Wi-Fi. 

I can't wait for the Internet revolution to hit this industry. I just hope I'm still alive.


Dream geoscience courses

MOOCs mean it's never been easier to learn something new.This is an appeal for opinions. Please share your experiences and points of view in the comments.

Are you planning to take any technical courses this year? Are you satisfied with the range of courses offered by your company, or the technical societies, or the commercial training houses (PetroSkills, Nautilus, and so on)? And how do you choose which ones to take — do you just pick what you fancy, seek recommendations, or simply aim for field classes at low latitudes?

At the end of 2012, several geobloggers wrote about courses they'd like to take. Some of them sounded excellent to me too... which of these would you take a week off work for?

Here's my own list, complete with instructors. It includes some of the same themes...

  • Programming for geoscientists (learn to program!) — Eric Jones
  • Solving hard problems about the earth — hm, that's a tough one... Bill Goodway?
  • Communicating rocks online — Brian Romans or Maitri Erwin
  • Data-driven graphics in geoscience — the figure editor at Nature Geoscience
  • Mathematics clinic for geoscientists — Brian Russell
  • Becoming a GIS ninja — er, a GIS ninja
  • Working for yourself — needs multiple points of view
What do you think? What's your dream course? Who would teach it?

Making images or making prospects?

Well-rounded geophysicists will have experience in each of the following three areas: acquisition, processing, and interpretation. Generally speaking, these three areas make up the seismic method, each requiring highly specified knowledge and tools. Historically, energy companies used to control the entire spectrum, owning the technology, the know-how and the risk, but that is no longer the case. Now, service companies do the acquisition and the processing. Interpretation is largely hosted within E & P companies, the ones who buy land and drill wells. Not only has it become unreasonable for a single geophysicist to be proficient across the board, but organizational structures constrain any particular technical viewpoint. 

Aligning with the industry's strategy, if you are a geophysicist, you likely fall into one of two camps: those who make images, or those who make prospects. One set of people to make the data, one set of people to do the interpretation.

This seems very un-scientific to me.

Where does science fit in?

Science, the standard approach of rational inquiry and accruing knowledge, is largely vacant from the applied geophysical business landscape. But, when science is used as a model, making images and making prospects are inseperable.

Can applied geophysics use scientific behaviour as a central anchor across disciplines?

There is a significant amount of science that is needed in the way that we produce observations, in the way that we make images. But the business landscape built on linear procedures leaves no wiggle room for additional testing and refinement. How do processors get better if they don't hear about their results? As a way of compensating, processing has deflected away from being a science of questioning, testing, and analysis, and moved more towards, well,... a process.

The sure-fire way to build knowledge and decrease uncertainty, is through experimentation and testing. In this sense this notion of selling 'solutions', is incompatible with scientific behavior. Science doesn't claim to give solutions, science doesn't claim to give answers, but it does promise to address uncertainty; to tell you what you know.

In studying the earth, we have to accept a lack of clarity in our data, but we must not accept mistakes, errors, or mediocrity due to shortcomings in our shared methodologies.

We need a new balance. We need more connectors across these organizational and disciplinary divides. That's where value will be made as industry encounters increasingly tougher problems. Will you be a connector? Will you be a subscriber to science?

Hall, M (2012). Do you know what you think you know? CSEG Recorder 37 (2), February 2012, p 26–30. Free to download from CSEG. 


Filters that distort vision

Almost two weeks ago, I had LASIK vision correction surgery. Although the recovery took longer than average, I am seeing better than I ever did before with glasses or contacts. Better than 20/20. Here's why.

Low order and high order refractive errors

Most people (like me) who have (had) poor vision fall short of pristine correction because lenses only correct low order refractive errors. Still, any correction gives a dramatic improvement to the naked eye; further refinements may be negligible or imperceptible. Higher order aberrations, caused by small scale structural irregularities of the cornea, can still affect one's refractive power by up to 20%, and they can only be corrected using customized surgical methods.

It occurs to me that researchers in optometry, astronomy, and seismology face a common challenge: how to accurately measure and subsequently correct for structural deformations in refractive media, and the abberrations in wavefronts caused by such higher-order irregularities. 

The filter is the physical model

Before surgery, a wavefront imaging camera was used to make detailed topographic maps of my corneas, and estimate point spread functions for each eye. The point spread function is a 2D convolution operator that fuzzies the otherwise clear. It shows how a ray is scattered and smeared across the retina. Above all, it is a filter that represents the physical eye.

Point spread function (similar to mine prior to LASIK) representing refractive errors of the cornea (top two rows), and corrected vision (bottom row). Point spread functions are filters that distort both the visual and seismic realms. The seismic example is a segment of inline 25, Blake Ridge 3D seismic survey, available from the Open Seismic Repository (OSR).Observations in optics and seismology alike are only models of the physical system, models that are constrained by the filters. We don't care about the filters per se, but they do get in the way of the underlying system. Luckily, the behaviour of any observation can be expressed as a combination of filters. In this way, knowing the nature of reality literally means quantifying the filters that cause distortion. Change the filter, change the view. Describe the filter, describe the system. 

The seismic experiment yields a filtered earth; a smeared reality. Seismic data processing is the analysis and subsequent removal of the filters that distort geological vision. 

This image was made using the custom filter manipulation tool in FIJI. The seismic data is available from OpendTect's Open Seismic Repository.


5 ways to kickstart an interpretation project

Last Friday, teams around the world started receiving external hard drives containing this year's datasets for the AAPG's Imperial Barrel Award (IBA for short). I competed in the IBA in 2008 when I was a graduate student at the University of Alberta. We were coached by the awesome Dr Murray Gingras (@MurrayGingras), we won the Canadian division, and we placed 4th in the global finals. I was the only geophysical specialist on the team alongside four geology graduate students.

Five things to do

Whether you are a staff geoscientist, a contractor, or competitor, it can help to do these things first:

  1. Make a data availability map (preferably in QGIS or ArcGIS). A graphic and geospatial representation of what you have been given.
  2. Make well scorecards: as a means to demonstrate not only that you have wells, but what information you have within the wells.
  3. Make tables, diagrams, maps of data quality and confidence. Indicate if you have doubts about data origins, data quality, interpretability, etc.
  4. Background search: The key word is search, not research. Use Mendeley to organize, tag, and search through the array of literature
  5. Use Time-Scale Creator to make your own stratigraphic column. You can manipulate the vector graphic, and make it your own. Much better than copying an old published figure. But use it for reference.

All of these things can be done before assigning roles, before saying who needs to do what. All of this needs to be done before the geoscience and the prospecting can happen. To skirt around it is missing the real work, and being complacent. Instead of being a hammer looking for a nail, lay out your materials, get a sense of what you can build. This will enable educated conversations about how you can spend your geoscientific manpower, division of labour, resources, time, etc.

Read more, then go apply it 

In addition to these tips for launching out of the blocks, I have also selected and categorized blog posts that I think might be most relevant and useful. We hope they are helpful to all geoscientists, but especially for students. Visit the Agile blog highlights list on SubSurfWiki.

I wish a happy and exciting IBA competition to all participants, and their supporting university departments. If you are competing, say hi in the comments and tell us where you hail from.