A revolution in seismic acquisition?

We're in warm, sunny Calgary for the GeoConvention 2013. The conference feels like it's really embracing geophysics this year — in the past it's always felt more geological somehow. Even the exhibition floor felt dominated by geophysics. Someone we spoke to speculated that companies were holding their geological cards close to their chests, but the service companies are still happy to talk about (ahem, promote) their geophysical advances.

Are you at the conference? What do you think? Let us know in the comments.

We caught about 15 talks of the 100 or so on offer today. A few of them ignited the old whines about half-cocked proofs of efficacy. Why is it still acceptable to say that a particular seismic volume or inversion result is 'higher resolution' or 'more geological' with nothing more than a couple of sections or timeslices as evidence?

People are excited about designing seismic acquisition expressly for wavefield reconstruction. In a whole session devoted to the subject, for example, Mauricio Sacchi showed how randomization helps with regularization in processing, allowing us to either get better image quality, or to lower cost. It feels like the start of a new wave of innovation in acquisition, which has more than its fair share of recent innovation: multi-component, wide azimuth, dual-sensor, simultaneous source...

Is it a revolution? Or just the fallacy of new things looking revolutionary... until the next new thing? It's intriguing to the non-specialist. People are talking about 'beyond Nyquist' again, but this time without inducing howls of derision. We just spent an hour talking about it, and we think there's something deep going on... we're just not sure how to articulate it yet.

Unsolved problems

We were at the conference today, but really we are focused on the session we're hosting tomorrow morning. Along with a roomful of adventurous conference-goers (you're invited too!), looking for the most pressing questions in subsurface science. We start at 8 a.m. in Telus 101/102 on the main floor of the north building.

Backwards and forwards reasoning

Most people, if you describe a train of events to them will tell you what the result will be. There will be few people however, that if you told them a result, would be able to evolve from their own consciousness what the steps were that led to that result. This is what I mean when I talk about reasoning backward.
— Sherlock Holmes, A Study in Scarlet, Sir Arthur Conan Doyle (1887)

Reasoning backwards is the process of solving an inverse problem — estimating a physical system from indirect data. Straight-up reasoning, which we call the forward problem, is a kind of data collection: empiricism. It obeys a natural causality by which we relate model parameters to the data that we observe.

Modeling a measurement

Where are you headed? Every subsurface problem can be expressed as the arrow between two or more such panels.Inverse problems exists for two reasons. We are incapable of measuring what we are actually interested in, and it is impossible to measure a subject in enough detail, and in all aspects that matter. If, for instance, I ask you to determine my weight, you will be troubled if the only tool I allow is a ruler. Even if you are incredibly accurate with your tool, at best, you can construct only an estimation of the desired quantity. This estimation of reality is what we call a model. The process of estimation is called inversion.

Measuring a model

Forward problems are ways in which we acquire information about natural phenomena. Given a model (me, say), it is easy to measure some property (my height, say) accurately and precisely. But given my height as the starting point, it is impossible to estimate the me from which it came. This is an example of an ill-posed problem. In this case, there is an infinite number of models that share my measurements, though each model is described by one exact solution. 

Solving forward problems are nessecary to determine if a model fits a set of observations. So you'd expect it to be performed as a routine compliment to interpretation; a way to validate our assumptions, and train our intuition.  

The math of reasoning

Forward and inverse problems can be cast in this seemingly simple equation.

,

where d is a vector containing N observations (the data), m is a vector of M model parameters (the model), and G is a N × M matrix operator that connects the two. The structure of G changes depending on the problem, but it is where 'the experiment' goes. Given a set of model parameters m, the forward problem is to predict the data d produced by the experiment. This is as simple as plugging values into a system of equations. The inverse problem is much more difficult: given a set of observations d, estimate the model parameters m.

I think interpreters should describe their work within the Gm = d framework. Doing so would safeguard against mixing up observations, which should be objective, and interpretations, which contain assumptions. Know the difference between m and d. Express it with an arrow on a diagram if you like, to make it clear which direction you are heading in.

Illustrations for this post were created using data from the Marmousi synthetic seismic data set. The blue seismic trace and its corresponding velocity profile is at location no. 250.

How to get paid big bucks

Yesterday I asked 'What is inversion?' and started looking at problems in geoscience as either forward problems or inverse problems. So what are some examples of inverse problems in geoscience? Reversing our forward problem examples:

• Given a suite of sedimentological observations, provide the depositional environment. This is a hard problem, because different environments can produce similar-looking facies. It is ill-conditioned, because small changes in the input (e.g. the presence of glaucony, or Cylindrichnus) produces large changes in the interpretation.
• Given a seismic trace, produce an impedance log. Without a wavelet, we cannot uniquely deduce the impedance log — there are infinitely many combinations of log and wavelet that will give rise to the same seismic trace. This is the challenge of seismic inversion. 

To solve these problems, we must use induction — a fancy name for informed guesswork. For example, we can use judgement about likely wavelets, or the expected geology, to constrain the geophysical problem and reduce the number of possibilities. This, as they say, is why we're paid the big bucks. Indeed, perhaps we can generalize: people who are paid big bucks are solving inverse problems...

• How do we balance the budget?
• What combination of chemicals might cure pancreatic cancer?
• What musical score would best complement this screenplay?
• How do I act to portray a grief-stricken war veteran who loves ballet?

What was the last inverse problem you solved?

Update on Wednesday, April 17, 2013 at 6:48 PM by Matt Hall

Moar inversion! Don't miss the previous post in this mini-series, or the next one — where things get a bit more mathy. 

What is inversion?

Inverse problems are at the heart of geoscience. But I only hear hardcore geophysicists talk about them. Maybe this is because they're hard problems to solve, requiring mathematical rigour and computational clout. But the language is useful, and the realization that some problems are just damn hard — unsolvable, even — is actually kind of liberating. 

Forwards first

Before worrying about inverse problems, it helps to understand what a forward problem is. A forward problem starts with plenty of inputs, and asks for a straightforward, algorithmic, computable output. For example:

• What is 4 × 5?
• Given a depositional environment, what sedimentological features do we expect?
• Given an impedance log and a wavelet, compute a synthetic seismogram.

These problems are solved by deductive reasoning, and have outcomes that are no less certain than the inputs.

Can you do it backwards?

You can guess what an inverse problem looks like. Computing 4 × 5 was pretty easy, even for a geophysicist, but it's not only difficult to do it backwards, it's impossible:

20 = what × what

You can solve it easily enough, but solutions are, to use the jargon, non-unique: 2 × 10, 7.2 × 1.666..., 6.3662 × π — you get the idea. One way to deal with such under-determined systems of equations is to know about, or guess, some constraints. For example, perhaps our system — our model — only includes integers. That narrows it down to three solutions. If we also know that the integers are less than 10, there can be only one solution.

Non-uniqueness is a characteristic of ill-posed problems. Ill-posedness is a dead giveaway of an inverse problem. Proposed by Jacques Hadamard, the concept is the opposite of well-posedness, which has three criteria:

• A solution exists.
• The solution is unique.
• The solution is well-conditioned, which means it doesn't change disproportionately when the input changes. 

Notice the way the example problem was presented: one equation, two unknowns. There is already a priori knowledge about the system: there are two numbers, and the operator is multiplication. In geoscience, since the earth is not a computer, we depend on such knowledge about the nature of the system — what the variables are, how they interact, etc. We are always working with a model of nature.

Tomorrow, I'll look at some specific examples of inverse problems, and Evan will continue the conversation next week.

Update on Friday, April 12, 2013 at 8:17 AM by Matt Hall

Don't miss the first follow-up to this article, How to get paid big bucks. 

Must-read geophysics blogs

Tuesday's must-read list was all about traditional publishing channels. Today, it's all about new media.

If you're anything like me before Agile, you don't read a lot of blogs. At least, not ones about geophysics. But they do exist! Get these in your browser favourites, or use a reader like Google Reader (anywhere) or Flipboard (on iPad).

Seismos

Chris Liner, a geophysics professor at the University of Arkansas, recently moved from the University of Houston. He's been writing Seismos, a parallel universe to his occasional Leading Edge column, since 2008.

MyCarta

Matteo Niccoli (@My_Carta on Twitter) is an exploration geoscientist in Stavanger, Norway, and he recently moved from Calgary, Canada. He's had MyCarta: Geophysics, visualization, image processing and planetary science, since 2011. This blog is a must-read for MATLAB hackers and image processing nuts. Matteo was one of our 52 Things authors.

GeoMika

Mika McKinnon (@mikamckinnon), a geophysicist in British Columbia, Canada, has been writing GeoMika: Fluid dynamics, diasters, geophysics, and fieldwork since 2008. She's also into education outreach and the maker-hacker scene.

The Way of the Geophysicist

Jesper Dramsch (@JesperDramsch), a geophysicist in Hamburg, Germany has written the wonderfully personal and philosophical The Way of The Geophysicist since 2011. His tales of internships at Fugro and Schlumberger provide great insights for students.

VatulBlog

Maitri Erwin (@maitri), an exploration geoscientist in Texas, USA. She has been blogging since 2001 (surely some kind of record), and both she and her unique VatulBlog: From Kuwait to Katrina and beyond defy categorization. Maitri was also one of our 52 Things authors. 

There are other blogs on topics around seismology and exploration geophysics — shout outs go to Hypocentre in the UK, the Laboratoire d'imagerie et acquisition des mesures géophysiques in Quebec, occasional seismicky posts from sedimentologists like @zzsylvester, and the panoply of bloggery at the AGU. Stick those in your reader!