The etiology of rivers

The Ordovician was a primitive time. No mammals. No birds. No flowers. Most geologists know this, right? How about this: No meandering rivers.

Recently several geo-bloggers wrote about geological surprises. This was on my shortlist. 

A couple of weeks ago, Evan posted the story of scale-free gravity deformation we heard from Adrian Park and his collaborators at the Atlantic Geological Society's annual Colloquium. My own favourite from the conference was Neil Davies' account of the evolution of river systems:

Davies, Neil & Martin Gibling (2011). Pennsylvanian emergence of anabranching fluvial deposits: the parallel rise of arborescent vegetation and fixed-channel floodplains.

Neil, a post-doctoral researcher at Dalhousie University in Nova Scotia, Canada, started with a literature review. He read dozens of case studies of fluvial geology from all over the world, noting the interpretation of river morphology (fluvotype?). What he found was, to me at least, surprising: there were no reported meandering rivers before the Devonian, and no anabranching rivers before the Carboniferous. 

The idea that rivers have evolved over time, becoming more diverse and complex, is fascinating. At first glance, rivers might seem to be independent of life and other manifestly time-bound phenomena. But if we have learned only one thing in the last couple of decades, it is that the earth's systems are much more intimately related than this, and that life leaves its fingerprint on everything on earth's surface. 

A little terminology: anastomosing, a term I was more familiar with, is not strictly the correct term for these many-branched, fixed-channel rivers. Sedimentologists prefers anabranching. Braided and meandering river types are perhaps more familiar. The fluviotypes I'm showing here might be thought of as end members — most rivers show all of these characteristics through time and space.

What is the cause of this evolution? Davies and Gibling discussed two parallel effects: bank stabilization by soil and roots, and river diversion, technically called avulsion, by fallen trees. The first idea is straightforward: plants colonize river banks and floodplains, thus changing their susceptibility to erosion. The second idea was new to me, but is also simple: as trees got taller, it became more and more likely that fallen trunks would, with time, make avulsion more likely. 

There is another river type we are familiar with in Canada: the string of beaver dams (like this example from near Fort McMurray, Alberta). I don't know for sure, but I bet these first appeared in the Eocene. I have heard that the beaver is second only to man in terms of the magnitude of its effect on the environment. As usual, I suspect that microbes were not considered in this assertion.

All of this makes me wonder: are there other examples of evolution expressing itself in geomorphology like this?

Many thanks to Neil and Martin for allowing us to share this story. Please forgive my deliberate vagueness with some of the details — this work is not yet published; I will post a link to their forthcoming paper when it is published. The science and the data are theirs, any errors or inconsistencies are mine alone. 

Update on 2011-08-23 11:40 by Matt Hall

Davies and Gibling went on to publish this research in Nature Geoscience in August 2011.

The BBC covered the story in their science pages. 

How to cheat

Yesterday I posted the rock physics cheatsheet, which is a condensed version of useful seismic reservoir characterization and rock mechanics concepts. It's cheat as in simplify, not cheat as in swindle. 

As Matt discussed on Friday, heuristics can be shortcuts to hone your intuition. Our minds search to use rules of thumb to visualise the invisible and to solve sticky problems. That's where the cheat sheet comes in. You might not find rock physics that intuitive, but let's take a look at the table to see how it reveals some deeper patterns.

The table of elastic parameters is setup based on the fundamental notion that, if you have any two elastic properties previously defined, you can compute all the others. This is a consequence of one of the oldest laws in classical mechanics: Newton's second law, F=ma. In particular one thing I find profound about seismic velocity is that it is wholly determined by a ratio of competing tensional (elastic) forces to inertial (density) forces. To me, it is not immediately obvious that speed, with units of m/s, results from the ratio of pressure to density. 

This simple little equation has had a profound impact on the utility of seismology to the oil and gas industry. It links extrinsic dynamic properties (V_P) to intrinsic rock properties (K, μ, ρ). The goal of course, is not just to investigate elastic properties for the sake of it, but to link elastic properties to reservoir and petrophysical properties. This is traditionally done using a rock physics template. The one I find easiest to understand is the V_P/V_S vs P-impedance template, an example of which is shown on the cheatsheet. You will see others in use, for instance Bill Goodway has pioneered the λρ vs μρ (LMR) template.

In an upcoming post we'll look to deepen the connection between Newtonian mechanics and reservoir characterization. 

Rock physics cheatsheet

Today, I introduce to you the rock physics cheatsheet. It contains useful information for people working on problems in seismic rock physics, inversion, and the mechanical properties of rocks. Admittedly, there are several equations, but I hope they are laid out in a simple and systematic way. This cheatsheet is the third instalment, following up from the geophysics cheatsheet and basic cheatsheet we posted earlier. 

To me, rock physics is the crucial link between earth science and engineering applications, and between reservoir properties and seismic signals. Rocks are, in fact, a lot like springs. Their intrinsic elastic parameters are what control the extrinsic seismic attributes that we collect using seismic waves. With this cheatsheet in hand you will be able to model fluid depletion in a time-lapse sense, and be able to explain to somebody that Young's modulus and brittleness are not the same thing.

So now with 3 cheatsheets at your fingertips, and only two spaces on the inside covers of you notebooks, you've got some rearranging to do! It's impossible to fit the world of seismic rock physics on a single page, so if you feel something is missing or want to discuss anything on this sheet, please leave a comment.

← Click to download the PDF (1.5MB)

Update on 2012-03-01 13:52 by Evan Bianco

Shortly after posting the rock physics cheatsheet, I got a request to quote the equations for the elastic parameters as a function of P-wave slowness, and S-wave slowness; typically acquired from a dipole sonic logging tool. These logs are commonly called "delta-t" (DTP and DTS) and have units of or  . Slowness is the inverse of velocity.

Cast in this form, the equations are not as compact, and therefore wouldn't fit very easily on the cheatsheet. But you will find these useful if wish to compute elastic properties directly from dipole sonic logs.

 Click to download the PDF (230KB) →

Update on 2012-10-15 21:27 by Evan Bianco

I have posted a new version of the rock physics cheatsheet. Click on the image of the cheatsheet above to download version 2, or go to the Download page.

What's new? I corrected a few typos, and tidied up some of the typography. There was a typo in the first Hashin–Shtrikman equation, and there was a typo in the formula for V_P as a function of Young's modulus and Poisson's ratio (first row of the elastic properties table). Finally, we made some minor improvements to the layout.

Confirmation

The first principle is that you must not fool yourself — and you are the easiest person to fool. So you have to be very careful about that. After you've not fooled yourself, it's easy not to fool other scientists.
Richard Feynman, 1974

Suppose that I have done a seismic inversion and have a new seismic attribute volume that predicts Poisson's ratio (a rock property that can help predict fluid type). According to my well calibration and my forward modelling, low Poisson's ratio means Gas. This is my hypothesis; I need to test it.

So here's a game: I have some new wells, represented by double-sided cards. Which cards do I need to turn over to prove the hypothesis that all the cards with Low PR on one side have Gas on the other? Take a moment to look at the four cards and decide which you will flip:

In the course of evolution, our brains have developed heuristics, rules of thumb, for dealing with problems like this one. Our intuition is made of heuristics: we're wary of the outsider with the thick accent; we balk at a garden hose in the grass that could have been a snake. We are programmed to see faces in the topography of Mars (left). The rules are useful to us in urgent matters of survival, letting us take the least risky course of urgent action. But I think they're limiting and misleading when rational decisions are required.

That's why most people, even educated people, get this problem wrong. As scientists we should be especially wary of this, but the fact is that we all tend to seek information that confirms our hypotheses, rather than trying to disprove them. In the problem above, the cards to flip are the Low PR card (of course, it had better have Gas on the other side), and the Water card, because it had better not say Low PR. Most people select the Gas card, but it is not required because its reverse cannot prove our disprove our hypothesis: we don't care if High PR also means Gas sometimes (or even all the time).

Think of a hypothesis you have about the data you are working on right now. Can you think of a test that might disprove it? Would you get funding for a test like this? 

This post is a version of part of my article The rational geoscientist, The Leading Edge, May 2010. I recently read this post on the OpenScience Project blog, and it got me thinking about this again. The image of Mars was created by NASA and the JPL, and is in the public domain.  

Where on (Google) Earth #272

I got WoGE #271 by the well-established lucky guess method. Some people mightn't think this is a method sensu stricto, but I will take what I can get. So I unabashedly declare victory and bring you number 272, fresh out of the oven; the time is 1600 AST, 2000 GMT.

Where on (Google) Earth is the best way to get a repetitive strain injury since interpreting seismic data. If you are new to the game, it is easy to play. The winner is the first person to examine the picture below, find the location (name, link, or lat-long), and give a brief explanation of its geological interest. Please post your answer in the comments. And thanks to the Schott Rule, which I am invoking, newbies have a slight edge: previous winners must wait one earth hour for each previous win before playing.

So: where and what on Google's green earth is this?

Rotten writing's rubbish, right?

Marked-up copy — effective copy editing is a useful skill for all scientists that writeI love teaching. I get a buzz from it. I don't know that I'm great at it, but I want to be great. As a student, I think I was quite reflective—both of my parents were teachers—and one of the great things about teaching is that you finally get to put your money where your mouth is. Every time you berated a teacher's boringness (behind their backs, obviously), or whined about how pointless an essay or lab exercise was (to your buddies), is now held up as a vivid and uncomfortable challenge. 

So late last year I got in touch with the Canadian Society of Petroleum Geologists (CSPG) and the US Society of Exploration Geophysicists (SEG) and offered a one-day short course. They both said they'd been wanting to offer something like it and, if enough people sign up for it, the course will run at least twice this year:

• at the CSPG convention in Calgary in May [course outline]
• at the SEG convention in San Antonio in September [course outline]

My worry is this: writing is like driving—most people think they're pretty good at it. But my course isn't just about style, it's also about tools, publishing, and getting things done. My two goals for the day are:

Get more people writing. Especially people from industry, who often excuse themselves from the global scientific community. 'I don't have the time' or 'My work's not interesting enough' are the things I hear. And maybe I'm a shallow, superficial kind of person, but I'm not so worried about high-brow, highly specialized, technical writing. There's plenty of that. I just want to see more grass-roots experience, stories, tutorials, field trip reports, how-to's, and what-I-did-at-the-weekend's. More community, in less traditional media.

Get people thinking about good style. Style has two aspects: the qualitative (what we might call interestingness) and the quantitative (correctness).  I don't claim to be the world's greatest writer myself, but I know what gets me good feedback in my work, and I have an eye for detail (did you notice the extra space back there? I did). I think there are two insidious notions out there about writing: science is serious business, and 'nit-picky' detail is not all that important. Both of these notions are nonsense.

If you were to take a writing skills course like this, what would you want to do or see? If you've done a course like this before and loved it (or not!), what can I learn from it? 

Apologies to Jon Agee for the title; his poem Rotten Writing, in his book Orangutan Tongs was the inspiration.