Modern illuminations

The illuminated manuscripts of the Middle Ages blended words and images, continuing traditions established by the Ancient Egyptians. Words and pictures go together: one without the other is a rather flat experience, like silent cinema, or eating fine food with a cold. This is why I like comic books so much. 

One of the opening sessions on Day 1 at the recent ScienceOnline conference was an hour with sketchnoter and überdoodler Perrin Ireland of Alphachimp Studio. She basically gave away all her secrets for purposeful scientific doodling. Tips like building a canon of fonts, practising icons and dividing lines, and honing an eye for the deft use of colour. 

The result... well, I had a lot of fun scribing talks. Two of them I managed to get to a point we might call al dente, or maybe half baked. The first from a session on open notebook science, something that interests me quite a bit: 

If it looks like you have to really listen and concentrate to produce one of these, that's because you do. I did miss bits, though, as I fretted over important things like what kind of robot to draw. And you might have noticed that I can't draw people. Yeah, I noticed that too. It didn't stop me adding them to the next one, from a session on the semantic web:

I'm not alone in my happiness at finding this sketchy new world. Perrin has given her perspective, and Michele Arduengo has written a lovely post about learning to draw science, and you can see many of the other efforts in this awesome Flickr gallery—the scratchings of amateurs like me sit half-convincingly alongside the professional pieces, and together I think they're rather wonderful.

Amenhotep image from Flickr user wallyg, licensed BY-NC-ND. All Flickr slideshow images are copyright of their respective creators, and may be subject to restrictions. All my work is licensed CC-BY.

The filtered earth

Ground-based image (top left) vs Hubble's image. Click for a larger view. One of the reasons for launching the Hubble Space Telescope in 1990 was to eliminate the filter of the atmosphere that affects earth-bound observations of the night sky. The results speak for themselves: more than 10 000 peer-reviewed papers using Hubble data, around 98% of which have citations (only 70% of all astronomy papers are cited). There are plenty of other filters at work on Hubble's data: the optical system, the electronics of image capture and communication, space weather, and even the experience and perceptive power of the human observer. But it's clear: eliminating one filter changed the way we see the cosmos.

What is a filter? Mathematically, it's a subset of a larger set. In optics, it's a wavelength-selection device. In general, it's a thing or process which removes part of the input, leaving some output which may or may not be useful. For example, in seismic processing we apply filters which we hope remove noise, leaving signal for the interpreter. But if the filters are not under our control, if we don't even know what they are, then the relationship between output and input is not clear.

Imagine you fit a green filter to your petrographic microscope. You can't tell the difference between the scene on the left and the one on the right—they have the same amount and distribution of green. Indeed, without the benefit of geological knowledge, the range of possible inputs is infinite. If you could only see a monochrome view, and you didn't know what the filter was, or even if there was one, it's easy to see that the situation would be even worse. 

Like astronomy, the goal of geoscience is to glimpse the objective reality via our subjective observations. All we can do is collect, analyse and interpret filtered data, the sifted ghost of the reality we tried to observe. This is the best we can do. 

What do our filters look like? In the case of seismic reflection data, the filters are mostly familiar: 

• the design determines the spatial and temporal resolution you can achieve
• the source system and near-surface conditions determine the wavelet
• the boundaries and interval properties of the earth filter the wavelet
• the recording system and conditions affect the image resolution and fidelity
• the processing flow can destroy or enhance every aspect of the data
• the data loading process can be a filter, though it should not be
• the display and interpretation methods control what the interpreter sees
• the experience and insight of the interpreter decides what comes out of the entire process

Every other piece of data you touch, from wireline logs to point-count analyses, and from pressure plots to production volumes, is a filtered expression of the earth. Do you know your filters? Try making a list—it might surprise you how long it is. Then ask yourself if you can do anything about any of them, and imagine what you might see if you could. 

Hubble image is public domain. Photomicrograph from Flickr user Nagem R., licensed CC-BY-NC-SA. 

News of the week

Some news from the last fortnight or so. Things seem to be getting going again after the winter break. If you see anything you think our readers would be interested in, please get in touch. 

Shale education

Penn State University have put together an interactive infographic on the Marcellus Shale development in Pennsylvania. My first impression was that it was pro-industry. On reflection, I think it's quite objective, if idealized. As an industry, we need to get away from claims like "fracking fluid is 99% water" and "shale gas developments cover only 0.05% of the state". They may be true, but they don't give the whole story. Attractive, solid websites like this can be part of fixing this.

New technology

This week all the technlogy news has come from the Consumer Electronics Show in Las Vegas. It's mostly about tablets this year, it seems. Seems reasonable—we have been seeing them everywhere recently, even in the workplace. Indeed, the rumour is that Schlumberger is buying lots of iPads for field staff.

So what's new in tech? Well, one company has conjured up a 10-finger multi-touch display, bringing the famous Minority Report dream a step closer. I want one of these augmented reality monocles. Maybe we will no longer have to choose between paper and digital!

Geophysical magic?

A tiny press story piqued our interest. Who can resist the lure of Quantum Resonance Interferometry? Well, apparently some people can, because ViaLogy has yet to turn a profit, but we were intrigued. What is QRI? ViaLogy's website is not the most enlightening source of information—they really need some pictures!—but they seem to be inferring signal from subtle changes in noise. In our opinion, a little more openness might build trust and help their business. 

New things to read

Sometimes we check out the new and forthcoming books in Amazon. Notwithstanding their nonsensical prices, a few caught our eye this week:

Detect and Deter: Can Countries Verify the Nuclear Test Ban? Dahlman, et al, December 2011, Springer, 281 pages, $129. I've been interested in nuclear test monitoring since reading about the seismic insights of Tukey, Bogert, and others at Bell Labs in the 1960s. There's geophysics, nuclear physics and politics in here.

Deepwater Petroleum Exploration & Production: A Nontechnical Guide Leffler, et al, October 2011, Pennwell, 275 pages, $79. This is the second edition of this book by ex-Shell engineer Bill Leffler, aimed at a broad industry audience. There are new chapters on geoscience, according to the blurb.

Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties Tiab and Donaldson, November 2011, Gulf Professional Publishing, 971 pages, $180. A five-star book at Amazon, this outrageously priced book is now in its third edition.

This regular news feature is for information only. We aren't connected with any of these organizations, and don't necessarily endorse their products or services. Low-res images of book and website considered fair use.

What do you mean by average?

I may need some help here. The truth is, while I can tell you what averages are, I can't rigorously explain when to use a particular one. I'll give it a shot, but if you disagree I am happy to be edificated. 

When we compute an average we are measuring the central tendency: a single quantity to represent the dataset. The trouble is, our data can have different distributions, different dimensionality, or different type (to use a computer science term): we may be dealing with lognormal distributions, or rates, or classes. To cope with this, we have different averages. 

Arithmetic mean

Everyone's friend, the plain old mean. The trouble is that it is, statistically speaking, not robust. This means that it's an estimator that is unduly affected by outliers, especially large ones. What are outliers? Data points that depart from some assumption of predictability in your data, from whatever model you have of what your data 'should' look like. Notwithstanding that your model might be wrong! Lots of distributions have important outliers. In exploration, the largest realizations in a gas prospect are critical to know about, even though they're unlikely.

Geometric mean

Like the arithmetic mean, this is one of the classical Pythagorean means. It is always equal to or smaller than the arithmetic mean. It has a simple geometric visualization: the geometric mean of a and b is the side of a square having the same area as the rectangle with sides a and b. Clearly, it is only meaningfully defined for positive numbers. When might you use it? For quantities with exponential distributions — permeability, say. And this is the only mean to use for data that have been normalized to some reference value. 

Harmonic mean

The third and final Pythagorean mean, always equal to or smaller than the geometric mean. It's sometimes (by 'sometimes' I mean 'never') called the subcontrary mean. It tends towards the smaller values in a dataset; if those small numbers are outliers, this is a bug not a feature. Use it for rates: if you drive 10 km at 60 km/hr (10 minutes), then 10 km at 120 km/hr (5 minutes), then your average speed over the 20 km is 80 km/hr, not the 90 km/hr the arithmetic mean might have led you to believe. 

Median average

The median is the central value in the sorted data. In some ways, it's the archetypal average: the middle, with 50% of values being greater and 50% being smaller. If there is an even number of data points, then its the arithmetic mean of the middle two. In a probability distribution, the median is often called the P50. In a positively skewed distribution (the most common one in petroleum geoscience), it is larger than the mode and smaller than the mean:

Mode average

The mode, or most likely, is the most frequent result in the data. We often use it for what are called nominal data: classes or names, rather than the cardinal numbers we've been discussing up to now. For example, the name Smith is not the 'average' name in the US, as such, since most people are called something else. But you might say it's the central tendency of names. One of the commonest applications of the mode is in a simple voting system: the person with the most votes wins. If you are averaging data like facies or waveform classes, say, then the mode is the only average that makes sense. 

Honourable mentions

Most geophysicists know about the root mean square, or quadratic mean, because it's a measure of magnitude independent of sign, so works on sinusoids varying around zero, for example. 

Finally, the weighted mean is worth a mention. Sometimes this one seems intuitive: if you want to average two datasets, but they have different populations, for example. If you have a mean porosity of 19% from a set of 90 samples, and another mean of 11% from a set of 10 similar samples, then it's clear you can't simply take their arithmetic average — you have to weight them first: (0.9 × 0.21) + (0.1 × 0.14) = 0.20. But other times, it's not so obvious you need the weighted sum, like when you care about the perception of the data points. 

Are there other averages you use? Do you see misuse and abuse of averages? Have you ever been caught out? I'm almost certain I have, but it's too late now...

There is an even longer version of this article in the wiki. I just couldn't bring myself to post it all here. 

Update on 2012-08-08 12:32 by Matt Hall

Geo-stats nut @fraserrc just reminded me that I'd missed Swanson's mean in my honourable mentions. How could I? It's a handy back-of-the-envelope estimator of the mean for a moderately skewed (usually lognormal) distribution, given P90, P50 and P10 values. It's easy, even for petroleum geologists:

It was published by retired Exxon geologist Roy Swanson, with some friends from the University of Aberdeen, and popularized by Peter Rose's courses and excellent book Risk Analysis and Management of Petroleum Exploration Ventures.

Reference

Hurst, A, G Brown, and R Swanson (2000). Swanson's 30–40–30 rule. AAPG Bulletin 84 (12), p 1883–1891. DOI 10.1306/​8626C70D-173B-11D7-8645000102C1865D

How to keep up with Agile*

I mentioned the other day that there are a few ways to keep up with this blog. I thought I'd list some of them out, in case you have not yet found one you like. 

The easiest thing for many is probably to get the email updates. They go out early in the morning the day after we put up a new post. We do not use your email address for anything else and would certainly never share it. To get these, just enter your email address in the box to the right →

If you already get them, don't worry, nothing has changed.

For many diehard blog readers, the only way is the RSS feed. You can access this from the link in the box on the right too. Just copy the URL of the feed [http://feeds.feedburner.com/agilegeoscience] into an RSS reader, sometimes called an aggregator. There are dozens — here's a list. Lots of people like Google Reader. Some people don't.

Visit our Twitter account to see what it's all about — no account requiredEvery new post is tweeted by the Twitter account @agilegeo. This is more or less all this Twitter account does, at least for now, so it's high signal-to-noise (if you consider our posts and comments signal, that is). These tweets also post to our Facebook page, so you can Like us to see the new posts in your Facebook feed.

We've started playing with Google+, but it's quite different from Facebook and Twitter, so is taking some getting used to. If you use Google+, follow Agile, me or Evan to get a smattering there. And Evan and I usually post about new writing in our LinkedIn profiles too, if you know us personally.

Lastly, there's always the trusty bookmark. Just remember to hit it occasionally. 

Thank you for reading! Seriously. Thank you.

The blog post

People sometimes eye Evan and I with suspicion when they ask about what we do. Even after a whole year of Agile, I admit I am sometimes at a loss for a snappy answer. In a nutshell, I'd say:

We solve geoscience problems for geoscientists. We like fast and useful solutions, not perfect or expensive solutions—we don't believe in perfect or expensive solutions. We love the things you might not have time for: data, technology, and documentation.

Above all, we love to help people. And that's what the blog is for: we want to be useful, mostly relevant, perhaps interesting, occasionally insightful. And we live on the edge of the continent and don't want to fall off, small and forgotten, into the North Atlantic. For us, the blog is a portal to Houston, Calgary, Aberdeen, Perth, and the rest of our world.

Is it worth it? Well, that depends how you measure 'worth it'. I reckon we spend 8 to 16 hours on an average of 3 weekly posts to the blog, so it's a substantial investment for us. A lot of it ends up in the wiki, or in a paper, or elsewhere; it's definitely a good catalyst for thinking, making useful stuff, and starting conversations. I don't think the blog has generated business purely on its own yet, but it has helped keep our profile up, and made us easier to find. 

Who reads it? We don't know for sure, but we have some clues. Our website has been visited almost exactly 30 000 times this year. We currently get about 800 visits a week, from about 550 unique visitors (shown in the chart above). Of those, about 30% are in the US, 20% are in Canada, 9% in the UK, then it's Australia, Germany, India, and Norway. The list contains 136 countries. This last fact alone fills us with joy, even if it's wrong by a factor of two.

How do the readers find us? About 140 people subscribe to our feed by email, which means they get an email alert the morning after we publish a post. Each week, only about 20 people come to us via Google, with search terms like seismic rock physics, agile geophysics, and tight gas vs shale gas. Since we announce new posts on LinkedIn, Twitter, and Facebook (and now Google+ too), we get visitors from those sources too: they send about 24%, 18%, and 6% of our traffic respectively (G+ has too little data). The average visitor looks at 2.2 pages and stays for 3 mins and 2 seconds. But hey, 3 minutes is a long time on the Internet. Right?

If you were looking for some juicy geoscience, not this navel gazing, then check out our recent Greatest Hits, and have an amazing New Year! See you in 2012.

Blog traffic data are summarized from Google Analytics and are for interest only—the data are prone to all sorts of errors and artifacts. What's more, I do not have data for the first 6 weeks or so of traffic. Pinches of salt all round.

I is for integrated trace

A zero-phase wavelet has peaks and troughs that line up with interfaces, and has side-lobe events not associated with physical boundaries. Because of this, we see that seismic amplitude is only, at best, a proxy for earth's material contrasts (as shown below by the impedance log) and can be difficult to interpret. The largest positive amplitude corresponds to a downward increase in impedance, and the largest negative amplitude corresponds to a downward decrease in impedance.

Now consider the integral of the seismic trace. In the illustration, I have coloured the positive amplitude values blue, and the negative amplitude values red, for each time sample. The integral is literally the sample-by-sample cumulative sum of amplitudes. Notice how the shape of the trace integral now looks similar to the impedance log (far left). The inflections correlate to the bed boundaries; the integration has done a 90 degree phase rotation of the data. The integrated trace looks more like the geologic contrasts. To think of it another way, if the derivative of impedance is reflectivity, then the derivative of the integrated trace is the seismic trace.  

In the final column on the right, the integrated trace has been scaled so that the relative variations approximately match the absolute variations of the actual acoustic impedance log. This curve is merely a squeeze and bulkshift of the integrated trace, to align with the impedance of the background lithology. In practice, scaling seismic measurements to geologically realistic ranges requires the knowledge of rock properties from nearby well logs. The trace on the far right is a rudimentary geology-from-seismic transformation of the data. Although the general shape of the 3-layer model is reconstructed, there are some complications. The first and third layer is too soft, the middle layer is too hard (and wobbly). The appearance of a high impedance doublet is because the seismic is band-limited. 

It is important to note that a trace integral does not yield a seismic estimate of impedance, it is only a proxy. Consider it a starting point for seismic inversion, not a substitute for it. In oil sands, for instance, Matt showed how the integrated trace gives a considerably more robust estimate of impedance for reservoir characterization compared to a more time consuming and expensive seismic inversion process.

Integrated trace is not meant to be the final product in a reservoir characterization workflow, but it is a seismic attribute that you should be working with anytime you are are trying to do inversion. It should be a starting point, a sanity check, because it is fast to run, easy to understand, completely deterministic (no guess work). If it is not available on your standard interpretation software, Geocraft is one place where you can do it.

2011 retrospective

The year is almost over so we thought we'd highlight some of our favourite and our most popular posts from the year. We've posted something like 150 or so missives to this blog this year — nothing to boast about, but when we look over our work we do feel like we've achieved something. We've made a lot of new friends and acquaintances, which has been the greatest part of it. We've also learnt a lot about geoscience, especially when we've posted things at the edges of our knowledge... luckily we don't mind learning in plain sight! 

Most popular

One of the eye-opening things about running a website is the incredible statistics available from Google Analytics. There's no personal information, of course, but where readers clicked from, what they read, and for how long, where they went next, what browser they use... and that doesn't scratch the surface. After the main page, the most popular stops are: 

• Downloads (people want stuff!)
• About Us
• Articles — various scribblings and talks
• Evan's awesome Rock Physics Cheatsheet
• Our reading list
• The apps page
• The cheatsheets in general

After the cheatsheet posts, the most-visited posts are:

• The scales of geoscience, about being helicoptery
• Shale vs tight, about the differences between shale gas and tight gas reservoirs
• What is unconventional?, about ways to define unconventional resources
• Are you a poet or a mathematician?, about the language of uncertainty

Most commented

I have not been very rigorous and filtered our own comments here—we try to respond to every comment. Except the comments about Paul Smith shoes and Breitling watches, which I delete immediately (if you don't have a blog, you are perhaps blissfully unaware of the tedious amount of robo-spam that blogs and wikis attract—lucky for you!). Apart from the Where on (Google) Earth game posts, some of the most commented posts were:

• Wherefore art thou, Expert?, about coping without them
• Geophysical stamps 3: Geophone, mainly because of my mistake!
• Why we should embrace openness, about being less secretive in appled geoscience
• The simultaneity funnel, about the process of invention

Most favourite

Evan and I have posts we loved to write and share. For what it's worth, here they are:

Evan	Matt
Species identification in the rock kingdom	Pseudogeophysics
The Rock Physics Workshop series	Things not to think
Shattering shale	Learn to program

That's it! It's almost the end of a hair-raising year for both Evan and I, and one of the most satisfying parts of it has been meeting and conversing with you, dear reader. Thank you for investing your attention in us now and then.

And have a wonderful Christmas, Newtonmas, or whatever you celebrate round your way. Cheers!

News of the week

The news is back! A few stories have caught our beady geoscientific eyes over the last couple of weeks... If you see anything you think we missed, drop us a line!

Spotfire is free*

*kind of. This is huge. One of the limits on adoption of the amazing Spotfire — the best tools we've ever used for data exploration, and a must-have tool for reservoir engineers — has been cost. But TIBCO is now offering Silver Spotfire, cloud-friendly versions for very reasonable dollars, starting at free! So if you have never tried it, now's your chance. It's very easy: install it, Ctrl-C a data table from MS Excel, and Ctrl-V into Spotfire, and you're away.

World's cheapest Lidar

Most geoscientists are happier holding a pencil than a mouse, so the news of gadgets like tablets coming to subsurface interpretation is always welcome. Though 3D interaction tools like gloves and wands, when they appeared about a decade ago, turned out to be utterly useless, perhaps Microsoft's Kinect can kill the mouse? For example, how about using a sandpit as an input device, like SandyStation?

If you're not sure about that, try this: a glaciologist at the University of California at Santa Cruz used a Kinect as a makeshift Lidar. Though it can only 'see' up to about 5 m, it's extremely fast, accurate, and cheap.

Six years of geo-floss

Geoscientific, free, libre, open source software, or geo-FLOSS, is, like, a 'thing'. The movement continues to grow and blossom at events like the awesome workshop we reported on in June, and the recently announced workshop at the 2012 EAGE Conference and Exhibition next June in Copenhagen. If you work on, use, care about, or are just curious about open source software in exploration geoscience, then we hope to see you there.

This regular news feature is for information only. We aren't connected with any of these organizations, and don't necessarily endorse their products or services. Image of Spotfire considered fair use.