The post of Christmas present

It's nearly the end of another banner year for humanity, which seems determined as ever to destroy the good things it has achieved. Here's hoping certain world 'leaders' have their Scrooge moments sooner rather than later.

One positive thing we can all do is bring a little more science into the world. And I don't just mean for the scientists you already know. Let's infect everyone we can find! Maybe your niece will one day detect a neutron star collision in the Early Cretaceous, or your small child's intuition for randomness will lead to more breakthroughs in quantum computing.

Build a seismic station

There's surely no better way to discover the wonder of waves than to build a seismometer. There are at least a couple of good options. I built a single component 10 Hz Raspberry Shake myself; it was easy to do and, once hooked up to Ethernet, the device puts itself online and starts streaming data immediately.

The Lego seismometer kit (above right) looks like a slightly cheaper option, and you might want to check that they can definitely ship in time for Xmas, but it's backed by the British Geological Survey so I think it's legit. And it looks very cool indeed.

Everyone needs a globe!

As I mentioned last year, I love globes. We have several at home and more at the office. I don't yet have a Moon globe, however, so I've got my eye on this Replogle edition, NASA approved apparently ("Yup, that's the moon alright!"), and not too pricey at about USD 85. 

They seem to be struggling to fill orders, but I can't mention globes without mentioning Little Planet Factory. These beautiful little 3D-printed worlds can be customized in all sorts of ways (clouds or no clouds, relief or smooth, etc), and look awesome in sets. 

The good news is that you can pick up LPF's little planets direct from Shapeways, a big 3D printing service provider. They aren't lacquered, but until LPF get back on track, they're the next best thing.

Geology as a lifestyle

Brenda Houston like minerals. A lot. She's made various photomicrographs into wallpaper and fabrics (below, left), and they are really quite awesome. Especially if you always wanted to live inside a geode

OK, some of them might make your house look a bit... Bond-villainy.

If you prefer the more classical imagery of geology, how about this Ancient Dorset duvet cover (USD 120) by De la Beche?

I love this tectonic pewter keychain (below, middle) — featuring articulated fault blocks, and tiny illustrations of various wave modes. And it's under USD 30.

A few months ago, Mark Tingay posted on Twitter about his meteorite-faced watch (below, right). Turns out it's a thing (of course it's a thing) and you can drop substantial sums of money on such space-time trinkets. Like $235,000.

Algorithmic puzzles and stuff

These are spectacular: randomly generated agate-like jigsaw puzzles. Every one is different! Even the shapes of the wooden pieces are generated with maths. They cost about USD 95, and come from Boston-based Nervous System. The same company has lots of other rock- and fossil-inspired stuff, like ammonity jewellery (from about USD 50) and some very cool coasters that look a bit like radiolarians (USD 48 for 4).

There's always books

You can't go wrong with books. These all just came out, and just might appeal to a geoscientist. And if these all sound a bit too much like reading for work, try the Atlas of Beer instead. Click on a book to open its page at Amazon.com.

The posts of Christmas past

If by any chance there aren't enough ideas here, or you are buying for a very large number of geoscientists, you'll have to dredge through the historical listicles of yesteryear — 2011, 2012, 2013, 2014, 2015, or 2016. You'll find everything there, from stocking stuffers to Triceratops skulls.

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The images in this post are all someone else's copyright and are used here under fair use guidelines. I'm hoping the owners are cool with people helping them sell stuff!

Not getting hacked

This kind of password is horrible for lots of reasons. The real solution to password madness is a password manager.

The end of the year is a great time to look around at your life and sort stuff out. One of the things you almost certainly need to sort out is your online security. Because if you haven't been hacked already (you probably have), you're just about to be.

Just look at some recent stories from the world of data security:

• Yesterday, it  emerged that Uber concealed a hack that exposed data of 57 million users
• Last month, we learned that when Yahoo said 1 billion accounts had been compromised in August 2013... it was wrong. It was 3 billion. In other words: all of their user accounts.
• On 29 July, hackers stole 143 million account details, seriously compromising hundreds of thousands of people.

There are plenty of others; Wired has been keeping track of them — read more here. Or check out Wikipedia's list.

Despite all this, I see hardly anyone using a password manager, and anecdotally I hear that hardly anyone uses two-factor authentication either. This tells me that at least 80% of smart people, inlcuding lots of my friends and relatives, are in daily peril. Oh no!

After reading this post, I hope you do two things:

• Start using a password manager. If you only do one thing, do this.
• Turn on two-factor authentication for your most vulnerable accounts.

Start using a password manager

Please, right now, download and install LastPass on every device and in every browser you use. It's awesome:

• It stores all your passwords! This way, they can all be different, and each one can be highly secure.
• It generates secure, random passwords for new accounts you create. 
• It scores you on the security level of your passwords, and lets you easily change insecure ones.
• The free version is awesome, and the premium version is only $2/month.

There are other password managers, of course, but I've used this one for years and it's excellent. Once you're set up, you can start changing passwords that are insecure, or re-used on multiple sites... or which are at Uber, Yahoo, or Equifax.

One surprise from using LastPass is being able to count the number of accounts I have created around the web over the years. I have 473 accounts stored in LastPass! That's 473 places to get hacked... how many places are you exposed?

The one catch: you need a bulletproof key for your password manager. Best advice: use a long pass-phrase instead.

The obligatory password cartoon, by xkcd and licensed CC-BY-NC

Two-factor authentication

Sure, it's belt and braces — but you don't want your security trousers to fall down, right? 

Er, anyway, the point is that even with a secure password, your password can still be stolen and your account compromised. But it's much, much harder if you use two-factor authentication, aka 2FA. This requires you to enter a code — from a hardware key or an app, or received via SMS — as well as your password. If you use an app, it introduces still another layer of security, because your phone should be locked.

I use Google's Authenticator app, and I like it. There's a little bit of hassle the first time you set it up, but after that it's plain sailing. I have 2FA turned on for all my 'high risk' accounts: Google, Twitter, Facebook, Apple, AWS, my credit card processor, my accounting software, my bank, my domain name provider, GitHub, and of course LastPass. Indeed, LastPass even lets me specify that logins must originate in Canada. 

What else can you do?

There are some other easy things you can do to make yourself less hackable:

• Install updates on your phones, tablets, and other computers. Keep browsers and operating systems up to date.
• Be on high alert for phishing attempts. Don't follow links to sites like your bank or social media sites — type them into your browser if possible. Be very suspicious of anyone contacting you, especially banks.
• Don't use USB sticks. The cloud is much safer — I use Dropbox myself, it's awesome.

For more tips, check out this excellent article from Motherboard on not getting hacked.

x lines of Python: Let's play golf!

Normally in the x lines of Python series, I'm trying to do something useful in as few lines of code as possible, but — and this is important — without sacrificing clarity. Code golf, on the other hand, tries solely to minimize the number of characters used, and to heck with clarity. This might, and probably will, result in rather obfuscated code.

So today in x lines, we set x = 1 and see what kind of geophysics we can express. Follow along in the accompanying notebook if you like.

A Ricker wavelet

One of the basic building blocks of signal processing and therefore geophysics, the Ricker wavelet is a compact, pulse-like signal, often employed as a source in simulation of seismic and ground-penetrating radar problems. Here's the equation for the Ricker wavelet:

$$ A = (1-2 \pi^2 f^2 t^2) e^{-\pi^2 f^2 t^2} $$

where \(A\) is the amplitude at time \(t\), and \(f\) is the centre frequency of the wavelet. Here's one way to translate this into Python, more or less as expressed on SubSurfWiki:

import numpy as np 
def ricker(length, dt, f):
    """Ricker wavelet at frequency f Hz, length and dt in seconds.
    """
    t = np.arange(-length/2, length/2, dt)
    y = (1.0 - 2.0*(np.pi**2)*(f**2)*(t**2)) * np.exp(-(np.pi**2)*(f**2)*(t**2))
    return t, y

That is alredy pretty terse at 261 characters, but there are lots of obvious ways, and some non-obvious ways, to reduce it. We can get rid of the docstring (the long comment explaining what the function does) for a start. And use the shortest possible variable names. Then we can exploit the redundancy in the repeated appearance of \(\pi^2f^2t^2\)... eventually, we get to:

def r(l,d,f):import numpy as n;t=n.arange(-l/2,l/2,d);k=(n.pi*f*t)**2;return t,(1-2*k)/n.exp(k)

This weighs in at just 95 characters. Not a bad reduction from 261, and it's even not too hard to read. In the notebook accompanying this post, I check its output against the version in our geophysics package bruges, and it's legit:

The 95-character Ricker wavelet in green, with the points computed by the function in BRuges.

What else can we do?

In the notebook for this post, I run through some more algorithms for which I have unit-tested examples in bruges:

• The Ormsby wavelet, reduced from 1545 characters in bruges to 168 characters. (Not bad, considering it took me 111 characters just to express the mathematical equation in the wiki!)
• The 4-term Aki-Richards equation, weighing in at only 179 characters (9.8% of the 1828 characters in bruges).
• The exact Zoeppritz solution for a PP reflection, at 386 characters.

To give you some idea of why we don't normally code like this, here's what the Aki–Richards solution looks like:

def r(a,c,e,b,d,f,t):import numpy as n;w=f-e;x=f+e;y=d+c;p=n.pi*t/180;s=n.sin(p);return w/x-(y/a)**2*w/x*s**2+(b-a)/(b+a)/n.cos((p+n.arcsin(b/a*s))/2)**2-(y/a)**2*(2*(d-c)/y)*s**2

A bit hard to debug! But there is still some point to all this — I've found I've had to really understand Python's order of mathematical operations, and find different ways of doing familiar things. Playing code golf also makes you think differently about repetition and redundancy. All good food for developing the programming brain.

Do have a play with the notebook, which you can even run in Microsoft Azure, right in your browser! Give it a try. (You'll need an account to do this. Create one for free.)

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Many thanks to Jesper Dramsch and Ari Hartikainen for helping get my head into the right frame of mind for this silliness!

A new blog, and a new course

There's a great new geoscience blog on the Internet — I urge you to add it to your blog-reading app or news reader or list of links or whatever it is you use to keep track of these things. It's called Geology and Python, and it contains exactly what you'd expect it to contain!

The author, Bruno Ruas de Pinho, has nine posts up so far, all excellent. The range of topics is quite broad:

• Calculating the Hoek–Brown failure criterion
• Interpolation with machine learning (right)
• Stereonets and rose diagrams

In each post, Bruno takes some geoscience challenge — nothing too huge, but the problems aren't trivial either — and then methodically steps through solving the problem in Python. He's clearly got a good quantitative brain, having recently graduated in geological engineering from the Federal University of Pelotas, aka UFPel, Brazil, and he is now available for hire. (He seems to be pretty sharp, so if you're doing anything with computers and geoscience, you should snag him.)

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A new course for Calgary

We've run lots of Introduction to Python courses before, usually with the name Creative Geocomputing. Now we're adding a new dimension, combining a crash introduction to Python with a crash introduction to machine learning. It's ambitious, for sure, but the idea is not to turn you into a programmer. We aim to:

• Help you set up your computer to run Python, virtual environments, and Jupyter Notebooks.
• Get you started with downloading and running other people's packages and notebooks.
• Verse you in the basics of Python and machine learning so you can start to explore.
• Set you off with ideas and things to figure out for that pet project you've always wanted to code up.
• Introduce you to other Calgarians who love playing with code and rocks.

We do all this wielding geoscientific data — it's all well logs and maps and seismic data. There are no silly examples, and we don't shy away from so-called advanced things — what's the point in computers if you can't do some things that are really, really hard to do in your head?

Tickets are on sale now at Eventbrite, it's $750 for 2 days — including all the lunch and code you can eat.

Powered by Eventbrite

The Surmont Supermerge

In my recent Abstract horror post, I mentioned an interesting paper in passing, Durkin et al. (2017):

Paul R. Durkin, Ron L. Boyd, Stephen M. Hubbard, Albert W. Shultz, Michael D. Blum (2017). Three-Dimensional Reconstruction of Meander-Belt Evolution, Cretaceous Mcmurray Formation, Alberta Foreland Basin, Canada. Journal of Sedimentary Research 87 (10), p 1075–1099. doi: 10.2110/jsr.2017.59

I wanted to write about it, or rather about its dataset, because I spent about 3 years of my life working on the USD 75 million seismic volume featured in the paper. Not just on interpreting it, but also on acquiring and processing the data.

Let's start by feasting our eyes on a horizon slice, plus interpretation, of the Surmont 'Supermerge' 3D seismic volume:

Figure 1 from Durkin et al (2017), showing a stratal slice from 10 ms below the top of the McMurray Formation (left), and its interpretation (right). © 2017, SEPM (Society for Sedimentary Geology) and licensed CC-BY.

A decade ago, I was 'geophysics advisor' on Surmont, which is jointly operated by ConocoPhillips Canada, where I worked, and Total E&P Canada. My line manager was a Total employee; his managers were ex-Gulf Canada. It was a fantastic, high-functioning team, and working on this project had a profound effect on me as a geoscientist. 

The Surmont bitumen field

The dataset covers most of the Surmont lease, in the giant Athabasca Oil Sands play of northern Alberta, Canada. The Surmont field alone contains something like 25 billions barrels of bitumen in place. It's ridiculously massive — you'd be delighted to find 300 million bbl offshore. Given that it's expensive and carbon-intensive to produce bitumen with today's methods — steam-assisted gravity drainage (SAGD, "sag-dee") in Surmont's case — it's understandable that there's a great deal of debate about producing the oil sands. One factoid: you have to burn about 1 Mscf or 30 m³ of natural gas, costing about USD 10–15, to make enough steam to produce 1 bbl of bitumen.

Detail from Figure 12 from Durkin et al (2017), showing a seismic section through the McMurray Formation. Most of the abandoned channels are filled with mudstone (really a siltstone). The dipping heterolithic strata of the point bars, so obvious in horizon slices, are quite subtle in section. © 2017, SEPM (Society for Sedimentary Geology) and licensed CC-BY.

The field is a geoscience wonderland. Apart from the 600 km² of beautiful 3D seismic, there are now about 1500 wells, most of which are on the 3D. In places there are more than 20 wells per section (1 sq mile, 2.6 km², 640 acres). Most of the wells have a full suite of logs, including FMI in 2/3 wells and shear sonic as well in many cases, and about 550 wells now have core through the entire reservoir interval — about 65–75 m across most of Surmont. Let that sink in for a minute.

What's so awesome about the seismic?

OK, I'm a bit biased, because I planned the acquisition of several pieces of this survey. There are some challenges to collecting great data at Surmont. The reservoir is only about 500 m below the surface. Much of the pay sand can barely be called 'rock' because it's unconsolidated sand, and the reservoir 'fluid' is a quasi-solid with a viscosity of 1 million cP. The surface has some decent topography, and the near surface is glacial till, with plenty of boulders and gravel-filled channels. There are surface lakes and the area is covered in dense forest. In short, it's a geophysical challenge.

Nonetheless, we did collect great data; here's how:

• General information
• The ca. 600 km² Supermerge consists of a dozen 3Ds recorded over about a decade starting in 2001.
• The northern 60% or so of the dataset was recombined from field records into a single 3D volume, with pre- and post-stack time imaging.
• The merge was performed by CGG Veritas, cost nearly $2 million, and took about 18 months.
• Geometry
• Most of the surveys had a 20 m shot and receiver spacing, giving the volume a 10 m by 10 m natural bin size
• The original survey had parallel and coincident shot and receiver lines (Megabin); later surveys were orthogonal.
• We varied the line spacing between 80 m and 160 m to get trace density we needed in different areas.
• Sources
• Some surveys used 125 g dynamite at a depth of 6 m; others the IVI EnviroVibe sweeping 8–230 Hz.
• We used an airgun on some of the lakes, but the data was terrible so we stopped doing it.
• Receivers
• Most of the surveys were recorded into single-point 3C digital MEMS receivers planted on the surface.
• Bandwidth
• Most of the datasets have data from about 8–10 Hz to about 180–200 Hz (and have a 1 ms sample interval).

The planning of these surveys was quite a process. Because access in the muskeg is limited to 'freeze up' (late December until March), and often curtailed by wildlife concerns (moose and elk rutting), only about 6 weeks of shooting are possible each year. This means you have to plan ahead, then mobilize a fairly large crew with as many channels as possible. After acquisition, each volume spent about 6 months in processing — mostly at Veritas and then CGG Veritas, who did fantastic work on these datasets.

Kudos to ConocoPhillips and Total for letting people work on this dataset. And kudos to Paul Durkin for this fine piece of work, and for making it open access. I'm excited to see it in the open. I hope we see more papers based on Surmont, because it may be the world's finest subsurface dataset. I hope it is released some day, it would have huge impact.

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References & bibliography

Paul R. Durkin, Ron L. Boyd, Stephen M. Hubbard, Albert W. Shultz, Michael D. Blum (2017). Three-Dimensional Reconstruction of Meander-Belt Evolution, Cretaceous Mcmurray Formation, Alberta Foreland Basin, Canada. Journal of Sedimentary Research 87 (10), p 1075–1099. doi: 10.2110/jsr.2017.59 (not live yet).

Hall, M (2007). Cost-effective, fit-for-purpose, lease-wide 3D seismic at Surmont. SEG Development and Production Forum, Edmonton, Canada, July 2007.

Hall, M (2009). Lithofacies prediction from seismic, one step at a time: An example from the McMurray Formation bitumen reservoir at Surmont. Canadian Society of Exploration Geophysicists National Convention, Calgary, Canada, May 2009. Oral paper.

Zhu, X, S Shaw, B Roy, M Hall, M Gurch, D Whitmore and P Anno (2008). Near-surface complexity masquerades as anisotropy. SEG Annual Convention, Las Vegas, USA, November 2008. Oral paper. doi: 10.1190/1.3063976.

Surmont SAGD Performance Review (2016), by ConocoPhillips and Total geoscientists and engineers. Submitted to AER, 258 pp. Available online [PDF] — and well worth looking at.

Trad, D, M Hall, and M Cotra (2008). Reshooting a survey by 5D interpolation. Canadian Society of Exploration Geophysicists National Convention, Calgary, Canada, May 2006. Oral paper. 

The abstract lead-time problem

On Tuesday I wrote about the generally low quality of abstracts submitted to conferences. In particular, their vagueness and consequent uninterestingness. Three academics pointed out to me that there's an obvious reason.

Brian Romans (Virginia Tech) —

One issue, among many, with conference abstracts is the lead time between abstract submission and presentation (if accepted). AAPG is particularly bad at this and it is, frankly, ridiculous. The conference is >6 months from now! A couple years ago, when it was in Calgary in June, abstracts were due ~9 months prior. This is absurd. It can lead to what you are calling vague abstracts because researchers are attempting to anticipate some of what they will do. People want to present their latest and greatest, and not just recycle the same-old, which leads to some of this anticipatory language.

Chris Jackson (Imperial College London) and Zane Jobe (Colorado School of Mines) both responded on Twitter —

Also, by having abstract deadlines so far in advance of a conference, it's no surprise the abstract is vague.

— Christopher Jackson (@seis_matters) October 31, 2017

I agree that many abstracts need more meat, but big problem is that submitting an abstract for a conference 8 months away requires "vague" https://t.co/IqFU3MXYaf

— off the shelf edge (@ZaneJobe) November 1, 2017

What's the problem?

As I explained last time, most abstracts aren't fun to read. And people seem to be saying that this overlong lead time is to blame. I think they're probably right. So much of my advice was useless: you can't be precise about non-existent science.

In this light, another problem occurs to me. Writing abstracts months in advance seems to me to potentially fuel confirmation bias, as we encourage people to set out their hypothetical stalls before they've done the work. (I know people tend to do this anyway, but let's not throw more flammable material at it.)

So now I'm worried that we don't just have boring abstracts, we may be doing bad science too.

Why is it this way?

I think the scholarly societies' official line might be, "Propose talks on completed work." But let's face it, that's not going to happen, and thank goodness because it would lead to even more boring conferences. Like PowerPoint-only presentations, committees powered by Robert's Rules, and terrible coffee, year-old research is no longer good enough.

What can we do about it?

If we can't trust abstracts, how can we select who gets to present at a conference? I can't think of a way that doesn't introduce all sorts of bias or other unfairness, or is horribly prone to gaming.

So maybe the problem isn't abstracts, it's talks.

Maybe we don't need to select anything. We just need to let the research community take over the process of telling people about their work, in whatever way they want.

In this alternate reality, the role of the technical society is not to maintain a bunch of clunky processes to 'manage' (but not manage) the community. Instead, their role is to create the conditions for members of the community to dynamically share and progress their work. Research don't need 6 months' lead time, or giant spreadsheets full of abstracts, or broken websites (yes, I'm looking at you, Scholar One). They need an awesome space, whiteboards, Wi-Fi, AV equipment, and good coffee.

In short, maybe this is one of the nudges we need to start talking seriously about unconferences.