Machine learning meets seismic interpretation

Agile has been reverberating inside the machine learning echo chamber this past week at EAGE. The hackathon's theme was machine learning, Monday's workshop was all about machine learning. And Matt was also supposed to be co-chairing the session on Applications of machine learning for seismic interpretation with Victor Aare of Schlumberger, but thanks to a power-cut and subsequent rescheduling, he found himself double-booked so, lucky me, he invited me to sit in his stead. Here are my highlights, from the best seat in the house.

Before I begin, I must mention the ambivalence I feel towards the fact that 5 of the 7 talks featured the open-access F3 dataset. A round of applause is certainly due to dGB Earth Sciences for their long time stewardship of open data. On the other hand, in the sardonic words of my co-chair Victor Aarre, it would have been quite valid if the session was renamed The F3 machine learning session. Is it really the only quality attribute research dataset our industry can muster? Let's do better.

Using seismic texture attributes for salt classification

Ghassan AlRegib ruled the stage throughout the session with not one, not two, but three great talks on behalf of himself and his grad students at Georgia Institute of Technology (rather than being a show of bravado, this was a result of problems with visas). He showed some exciting developments in shallow learning methods for predicting facies in seismic data. In addition to GLCM attributes, he also introduced a couple of new (to me anyway) attributes for salt classification. Namely, textural gradient and a thing he called seismic saliency, a metric modeled after the human visual system describing the 'reaction' between relative objects in a 3D scene. 

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Twelve Seismic attributes used for multi-attribute salt-boundary classification. (a) is RMS Amplitude, (B) to (M) are TEXTURAL attributes. See abstract for details. This figure is copyright of Ghassan AlRegib and licensed CC-BY-SA by virtue of being generated from the F3 dataset of dGB and TNO.

Ghassan also won the speakers' lottery, in a way. Due to the previous day's power outage and subsequent reshuffle, the next speaker in the schedule was a no-show. As a result, Ghassan had an extra 20 minutes to answer questions. Now for most speakers that would be a public-speaking nightmare, but Ghassan hosted the onslaught of inquiring minds beautifully. If we hadn't had to move on to the next next talk, I'm sure he could have entertained questions all afternoon. I find it fascinating how unpredictable events like power outages can actually create the conditions for really effective engagement. 

Salt classification without using attributes (using deep learning)

Matt reported on Anders Waldeland's work a year ago, and it was interesting to see how his research has progressed, as he nears the completion of his thesis. 

Anders successfully demonstrated how convolutional neural networks (CNNs) can classify salt bodies in seismic datasets. So, is this a big deal? I think it is. Indeed, Anders's work seems like a breakthough in seismic interpretation, at least of salt bodies. To be clear, I don't think this means that it is time for seismic interpreters to pack up and go home. But maybe we can start looking forward to spending our time doing less tedious things than picking complex salt bodies.  

One slice of a 3d seismic volume with two CLASS LABELS: Salt (red) and Not SALT (GREEN). This is the training data. On the right: Extracted 3D salt body in the same dataset, coloured by elevation. Copyright of A Waldeland, used with permission.

He trained a CNN on one manually labeled slice of a 3D cube and used the network to automatically classify the full 3D salt body (on the right in the figure). Conventional algorithms for salt picking, such as that used by AlRegib (see above), typically rely on seismic attributes to define a feature space. This requires professional insight and judgment, and is prone to error and bias. Nicolas Audebert mentioned the same shortcoming in his talk in the workshop Matt wrote about last week. In contrast, the CNN algorithm works directly on the seismic data, learning the most discriminative filters on its own, no attributes needed. 

Intuition training

Machine learning isn't just useful for computing in the inverse direction such as with inversion, seismic interpretation, and so on. Johannes Amtmann showed us how machine learning can be useful for ranking the performance of different clustering methods using forward models. It was exciting to see: we need to get back into the habit of forward modeling, each and every one of us. Interpreters build synthetics to hone their seismic intuition. It's time to get insanely good at building forward models for machines, to help them hone theirs. 

There were so many fascinating problems being worked on in this session. It was one of the best half-day sessions of technical content I've ever witnessed at a subsurface conference. Thanks and well done to everyone who presented.

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References

• H Di, M Shafiq and G AlRegib (2017). Multi-attribute K-means Cluster Analysis for Salt Boundary Detection.
• H Di and G AlRegib (2017). Seismic Multi-attribute Classification for Salt Boundary Detection - A Comparison.
• Y Alaudah and G AlRegib,  (2017). A Weakly-Supervised Approach to Seismic Structure Labeling.
• A Waldeland and A Solberg (2017). Salt Classification Using Deep Learning.
• J Amtmann et al. (2017). Testing of Clustering Algorithms on Different 3D Seismic Models.

Machine learning and analytics in geoscience

We're at EAGE in Paris. I'm sitting in a corner of the exhibition because the power is out in the main hall, so all the talks for the afternoon have been postponed. The poor EAGE team must be beside themselves, I feel for them. (Note to future event organizers: white boards!)

Yesterday Diego, Evan, and I — along with lots of hackathon participants — were at the Data Science for Geosciences workshop, an all-day machine learning fest. The session was chaired by Cyril Agut (Total), Marianne Cuif-Sjostrand (Total), Florence Delprat-Jannaud (IFPEN), and Noalwenn Dubos-Sallée (IFPEN), and they had assembled a good programme, with quite a bit of variety.

Michel Lutz, Group Data Officer at Total, and adjunct at École des Mines de Saint-Étienne, gave a talk entitled, Data science & application to geosciences: an introduction. It was high-level but thoughtful, and such glimpses into large companies are always interesting. The company seems to have a mature data science strategy, and a well-developed technology stack. Henri Blondelle (AgileDD) asked about open data at the end, and Michel somewhat sidestepped on specifics, but at least conceded that the company could do more in open source code, if not data.

Infrastructure, big data, and IoT

Next we heard a set of talks about the infrastructure aspect of big (really big) data.

Alan Smith of Luchelan told the group about some negative experiences with Hadoop and seismic data (though it didn't seem to me that his problems were insoluble since I know of several projects that use it), and the realization that sometimes you just need fast infrastructure and custom software.

Hadi Jamali-Rad of Shell followed with an IoT story from the field. He had deployed a large number of wireless seismic sensors around a village in Holland, then tested various aspects of the communication system to answer questions like, what's the packet loss rate when you collect data from the nodes? What about from a balloon stationed over the site?

Duncan Irving of Teradata asked, Why aren't we [in geoscience] doing live analytics on 100PB of live data like eBay? His hypothesis is that IT organizations in oil and gas failed to keep up with key developments in data analytics, so now there's a crisis of sorts and we need to change how we handle our processes and culture around big data. 

Machine learning

We shifted gears a bit after lunch. I started with a characteristically meta talk about how I think our community can help ensure that our research and practice in this domain leads to good places as soon as possible. I'll record it and post it soon.

Nicolas Audebert of ONERA/IRISA presented a nice application of a 3D convolutional neural network (CNN) to the segmentation and classification of hyperspectral aerial photography. His images have between about 100 and 400 channels, and he finds that CNNs reduce error rates by up to about 50% (compared to an SVM) on noisy or complex images. 

Henri Blondelle of Agile Data Decisions talked about his experience of the CDA's unstructured data challenge of 2016. About 80% of the dataset is unstructured (e.g. folders of PDFs and TIFFs), and Henri's vision is to transform 80% of that into structured data, using tools like AgileDD's IQC to do OCR and heuristic labeling. 

Irina Emelyanova of CSIRO provided another case study: unsupervised e-facies prediction using various types of clustering, from K-means to some interesting variants of self-organizing maps. It was refreshing to see someone revealing a lot of the details of their implementation.

Jan Limbeck, a research scientist at Shell wrapped up the session with an overview of Shell's activities around big data and machine learning, as they prepare for exabytes. He mentioned the Mauricio Araya-Polo et al. paper on deep learning in seismic shot gathers in the special March issue of The Leading Edge — clearly it's easiest to talk about things they've already published. He also listed a lot of Shell's machine learning projects (frac optimization, knowledge graphs, reservoir simulation, etc), but there's no way to know what state they are in or what their chances of success are. 

As well as all the 9 talks, there were 13 posters, about a third of which were on infrastructure stuff, with the rest providing more case studies. Unfortunately, I didn't get the chance to look at them in any detail, but I appreciated the organizers making time for discussion around the posters. If they'd also allowed more physical space for the discussion it could have been awesome.

Analytics!

After hearing about Mentimeter from Chris Jackson I took the opportunity to try it out on the audience. Here are the results, I think they are fairly self-explanatory... 

I also threw in the mindmap I drew at the end as a sort of summary. The vertical axis represents something like'abstraction' or 'time' (in a workflow sense) and I think each layer depends somewhat on those beneath it. It probably makes sense to no-one but me.

Breakout!

It seems clear that 2017 is the breakout year for machine learning in petroleum geoscience, and in petroleum in general. If your company or institution has not yet gone beyond "watching" or "thinking about" data science and machine learning, then it is falling behind by a little more every day, and it has been for at least a year. Now's the time to choose if you want to be part of what happens next, or a victim of it.

Looking forward to EAGE

Evan, Diego and I are flying to Paris today for the EAGE Conference and Exhibition. It's exciting. We're excited. 

But the excitement starts before the conference. The Subsurface Hackathon is this weekend!

My diary

Even the hackathon excitement starts before the weekend, because tomorrow, Friday, we're running the hacker's bootcamp — a sort of short course appetizer for the hackathon. We have about 25 geoscientists coming to the Booster TOTAL (an event space at TOTAL's La Défense offices) to get some hands-on practice with Python and the latest in machine learning tools. It's especially exciting because we'll also have engineers from NVIDIA on hand to help with the coaching. The idea is to help people hit the ground running when the hackathon starts on Saturday.

After that, on Saturday and Sunday,  it's the hackathon itself. We have no fewer than 60 geoscientists and engineers registered for this breakout event. They're coming to the Booster to work on a wide array of machine learning ideas for the subsurface. It's going to be epic. You can read all about what happens next week, I promise. 

Then on Monday it's the Data Science for Geoscience workshop, at which I'm giving a keynote. Since I'm far from possessing expertise, I'm using it as a chance to get people jazzed about helping make the coming AI revolution in geoscience a positive experience. I'm really looking forward to it.

The conference itself starts on Tuesday. In the afternoon I'm co-chairing a session on machine learning (have you spotted the theme yet?) in seismic interpretation, along with Victor Aare of Schlumberger. It will be awesome to see what kind of progress our community is making in this field — it's fun to imagine what seismic interpretation might be like in a few years. There are so many fascinating problems to work on! Here are the talks in that session:

• H Di, M Shafiq and G AlRegib (Georgia Institute of Technology), Multi-attribute K-means Cluster Analysis for Salt Boundary Detection
• H Hashemi et al. (Institute of Geophysics, University of Tehran), Clustering Seismic Datasets for Optimized Facies Analysis Using a SSCSOM Technique
• S Hadiloo et al. (Research Institute of Applied Sciences, ACECR), Seismic Facies Analysis by ANFIS and Fuzzy Clustering Methods to Extract Channel Patterns
• A Waldeland and A Solberg (University of Oslo), Salt Classification Using Deep Learning
• H Di & G AlRegib (Georgia Institute of Technology), Seismic Multi-attribute Classification for Salt Boundary Detection - A Comparison
• P Xu, W Lu and B Wang (Tsinghua University), Multi-Attribute Classification Based On Sparse Autoencoder - A Gas Chimney Detection Example
• Y Alaudah and G AlRegib (Georgia Institute of Technology), Weakly Supervised Seismic Structure Labeling via Orthogonal Non-Negative Matrix Factorization
• J Amtmann et al. (University of Leoben/ Geo5 GmbH), Testing of Clustering Algorithms on Different 3D Seismic Models
• R Noemani Rad and G Gharabeigli (NIOC Research & Technology Directorate), Half Graben Evolution in the Kopet Dagh Fold-and-thrust Belt - Sedimentation and Pale Current History

On Wednesday we'll be taking in some more talks and posters, then in the afternoon I'm reprising my keynote talk at IFPEN, a subsurface research institute in the Bois de Boulogne. I've never been there before, although I have met a few IFP scientists before. I'm looking forward to it very much. 

It all ends for us on Thursday. Evan and Diego fly home and I'm off to Cambridge (the old one in the fens, not the one in Massachusetts) for a few days with family (and bookshops). Until then, expect much blogging!

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Going to EAGE?

If you're reading this and would like to meet up with us at Agile or some of the Software Underground crowd — the friendliest bunch of coding geoscientists you could hope for — let's plan to meet at the end of the workshop, at the workshop location. Look for the Software Underground shirts.

Unweaving the rainbow

Last week at the Canada GeoConvention in Calgary I gave a slightly silly talk on colourmaps with Matteo Niccoli. It was the longest, funnest, and least fruitful piece of research I think I've ever embarked upon. And that's saying something.

Freeing data from figures

It all started at the Unsession we ran at the GeoConvention in 2013. We asked a roomful of geoscientists, 'What are the biggest unsolved problems in petroleum geoscience?'. The list we generated was topped by Free the data, and that one topic alone has inspired several projects, including this one. 

Our goal: recover digital data from any pseudocoloured scientific image, without prior knowledge of the colourmap.

I subsequently proferred this challenge at the 2015 Geophysics Hackathon in New Orleans, and a team from Colorado School of Mines took it on. Their first step was to plot a pseudocoloured image in (red, green blue) space, which reveals the colourmap and brings you tantalizingly close to retrieving the data. Or so it seems...

Here's our talk:

GeoConvention highlights

We were in Calgary last week at the Canada GeoConvention 2017. The quality of the talks seemed more variable than usual but, as usual, there were some gems in there too. Here are our highlights from the technical talks...

Filling in gaps

Mauricio Sacchi (University of Alberta) outlined a new reconstruction method for vector field data. In other words, filling in gaps in multi-compononent seismic records. I've got a soft spot for Mauricio's relaxed speaking style and the simplicity with which he presents linear algebra, but there are two other reasons that make this talk worthy of a shout out:

1. He didn't just show equations in his talk, he used pseudocode to show the algorithm.
2. He linked to his lab's seismic processing toolkit, SeismicJulia, on GitHub.

I am sure he'd be the first to admit that it is early days for for this library and it is very much under construction. But what isn't? All the more reason to showcase it openly. We all need a lot more of that.

Update on 2017-06-7 13:45 by Evan Bianco: Mauricio, has posted the slides from his talk. 

Learning about errors

Anton Birukov (University of Calgary & graduate intern at Nexen) gave a great talk in the induced seismicity session. It was a lovely mashing-together of three of our favourite topics: seismology, machine-learning, and uncertainty. Anton is researching how to improve microseismic and earthquake event detection by framing it as a machine-learning classification problem. He's using Monte Carlo methods to compute myriad synthetic seismic events by making small velocity variations, and then using those synthetic events to teach a model how to be more accurate about locating earthquakes.

Figure 2 from Anton Biryukov's abstract. An illustration of the signal classification concept. The signals originating from the locations on the grid (a) are then transformed into a feature space and labeled by the class containing the event origin. From Biryukov (2017). Event origin depth uncertainty - estimation and mitigation using waveform similarity. Canada GeoConvention, May 2017.

The bright lights of geothermal energy
Matt Hall

Two interesting sessions clashed on Wednesday afternoon. I started off in the Value of Geophysics panel discussion, but left after James Lamb's report from the mysterious Chief Geophysicists' Forum. I had long wondered what went on in that secretive organization; it turns out they mostly worry about how to make important people like your CEO think geophysics is awesome. But the large room was a little dark, and — in keeping with the conference in general — so was the mood.

Feeling a little down, I went along to the Diversification of the Energy Industry session instead. The contrast was abrupt and profound. The bright room was totally packed with a conspicuously young audience numbering well over 100. The mood was hopeful, exuberant even. People were laughing, but not wistfully or ironically. I think I saw a rainbow over the stage.

If you missed this uplifting session but are interested in contributing to Canada's geothermal energy scene, which will certainly need geoscientists and reservoir engineers if it's going to get anywhere, there are plenty of ways to find out more or get involved. Start at cangea.ca and follow your nose.

We'll be writing more about the geothermal scene — and some of the other themes in this post — so stay tuned. 

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The Computer History Museum

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Mountain View, California, looking northeast over US 101 and San Francisco Bay. The Computer History Museum sits between the Googleplex and NASA Ames. Hangar 1, the giant airship hangar, is visible on the right of the image. Imagery and map data © Google, Landsat/Copernicus.

A few days ago I was lucky enough to have a client meeting in Santa Clara, California. I had not been to Silicon Valley before, and it was more than a little exciting to drive down US Route 101 past the offices of Google, Oracle and Amazon and basically every other tech company, marvelling at Intel’s factory and the hangars at NASA Ames, and seeing signs to places like Stanford, Mountain View, and Menlo Park.

I had a spare day before I flew home, and decided to visit Stanford’s legendary geophysics department, where there was a lecture that day. With an hour or so to kill, I thought I’d take in the Computer History Museum on the way… I never made it to Stanford.

The museum

The Computer History Museum was founded in 1996, building on an ambition of über-geek Gordon Bell. It sits in the heart of Mountain View, surrounded by the Googleplex, NASA Ames, and Microsoft. It’s a modern, airy building with the museum and a small café downstairs, and meeting facilities on the upper floor. It turns out to be an easy place to burn four hours.

I saw a lot of computers that day. You can see them too because much of the collection is in the online catalog. A few things that stood out for me were:

• The analog computers of the pre-digital era, especially the beautiful Nordsieck Differential Analyzer.
• The actual, fully working, room-sized IBM 1401. Picture below.
• A wall of home computers from the early 1980s, including a BBC Micro and a ZX Spectrum, both important machines to me. The display even evoked the smell of those machines... and the frustration of reading programs from audio tapes.
• An Enigma machine, an Apple I, and Google’s actual server (just one rack!) from 1999, complete with its sagging motherboards.

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No seismic

I had been hoping to read more about the early days of Texas Instruments, because it was spun out of a seismic company, Geophysical Service or GSI, and at least some of their early integrated circuit research was driven by the needs of seismic imaging. But I was surprised not to find a single mention of seismic processing in the place. We should help them fix this!

A replica of a 1968 game console. Nice finish!

1981 all over again.

You can't beat the aesthetic of early computers.

IBM's monolithic SAGE radar system. 

Gubbins.

Programming then was not like programming today.

An electronic analog computer, the Nordsieck Differential Analyzer (1950).

SEG machine learning contest: there's still time

Have you been looking for an excuse to find out what machine learning is all about? Or maybe learn a bit of Python programming language? If so, you need to check out Brendon Hall's tutorial in the October issue of The Leading Edge. Entitled, "Facies classification using machine learning", it's a walk-through of a basic statistical learning workflow, applied to a small dataset from the Hugoton gas field in Kansas, USA.

But it was also the launch of a strictly fun contest to see who can get the best prediction from the available data. The rules are spelled out in ther contest's README, but in a nutshell, you can use any reproducible workflow you like in Python, R, Julia or Lua, and you must disclose the complete workflow. The idea is that contestants can learn from each other.

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Left: crossplots and histograms of wireline log data, coloured by facies — the idea is to highlight possible data issues, such as highly correlated features. Right: true facies (left) and predicted facies (right) in a validation plot. See the rest of the paper for details.

What's it all about?

The task at hand is to predict sedimentological facies from well logs. Such log-derived facies are sometimes called e-facies. This is a familiar task to many development geoscientists, and there are many, many ways to go about it. In the article, Brendon trains a support vector machine to discriminate between facies. It does a fair job, but the accuracy of the result is less than 50%. The challenge of the contest is to do better.

Indeed, people have already done better; here are the current standings:

	Team	F1	Algorithm	Language	Solution
1	gccrowther	0.580	Random forest	Python	Notebook
2	LA_Team	0.568	DNN	Python	Notebook
3	gganssle	0.561	DNN	Lua	Notebook
4	MandMs	0.552	SVM	Python	Notebook
5	thanish	0.551	Random forest	R	Notebook
6	geoLEARN	0.530	Random forest	Python	Notebook
7	CannedGeo	0.512	SVM	Python	Notebook
8	BrendonHall	0.412	SVM	Python	Initial score in article

As you can see, DNNs (deep neural networks) are, in keeping with the amazing recent advances in the problem-solving capability of this technology, doing very well on this task. Of the 'shallow' methods, random forests are quite prominent, and indeed are a great first-stop for classification problems as they tend to do quite well with little tuning.

How do I enter?

There is still over 6 weeks to enter: you have until 31 January. There is a little overhead — you need to learn a bit about git and GitHub, there's some programming, and of course machine learning is a massive field to get up to speed on — but don't be discouraged. The very first entry was from Bryan Page, a self-described non-programmer who dusted off some basic skills to improve on Brendon's notebook. But you can run the notebook right here in mybinder.org (if it's up today — it's been a bit flaky lately) and a play around with a few parameters yourself.

The contest aspect is definitely low-key. There's no money on the line — just a goody bag of fun prizes and a shedload of kudos that will surely get the winners into some awesome geophysics parties. My hope is that it will encourage you (yes, you) to have fun playing with data and code, trying to do that magical thing: predict geology from geophysical data.

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Reference

Hall, B (2016). Facies classification using machine learning. The Leading Edge 35 (10), 906–909. doi: 10.1190/tle35100906.1. (This paper is open access: you don't have to be an SEG member to read it.)

Where is the ground?

This is the upper portion of a land seismic profile in Alaska. Can you pick a horizon where the ground surface is? Have a go at pickthis.io.

Pick the Ground surface at the top of the seismic section at pickthis.io.

Picking the ground surface on land-based seismic data is not straightforward. Picking the seafloor reflection on marine data, on the other hand, is usually a piece of cake, a warm-up pick. You can often auto-track the whole thing with a few seeds.

Seafloor reflection on Penobscot 3D survey, offshore Nova Scotia. from Matt's tutorial in the April 2016 The Leading Edge, The function of interpolation.

Why aren't interpreters more nervous that we don't know exactly where the surface of the earth is? I'm sure I'm not the only one that would like to have this information while interpreting. Wouldn't it be great if land seismic were more like marine?

Treacherously Jagged TopographY or Near-Surface processing ArtifactS?

If you're new to land-based seismic data, you might notice that there isn't a nice pickable event across the top of the section like we find in marine seismic data. Shot noise at the surface has been muted (deleted) in processing, and the low fold produces an unclean, jagged look at the top of the section. Additionally, the top of the section, time-zero — the seismic reference datum — usually floats somewhere above the land surface — and we can't know where that is unless it can be found in the file header, or looked up in the processing report.

The seismic reference datum, at a two-way time of zero seconds on seismic data, is typically set at mean sea level for offshore data. For land data, it is usually chosen to 'float' above the land surface.

Reframing the question

This challenge is a bit of a trick question. It begs the viewer to recognize that the seemingly simple task of mapping the ground level on a land seismic section is actually a rudimentary velocity modeling or depth conversion exercise in itself. Wouldn't it be nice to have the ground surface expressed as pickable seismic event? Shouldn't we have it always in our images? Baked into our data, so to speak, such that we've always got an unambiguous pick? In the next post, I'll illustrate what I mean and show what's involved in putting it in. 

In the meantime, I challenge you to pick where you think the (currently absent) ground surface is on this profile, so in the next post we can see how well you did.

x lines of Python: AVO plot

Amplitude vs offset (or, more properly, angle) analysis is a core component of quantitative interpretation. The AVO method is based on the fact that the reflectivity of a geological interface does not depend only on the acoustic rock properties (velocity and density) on both sides of the interface, but also on the angle of the incident ray. Happily, this angular reflectivity encodes elastic rock property information. Long story short: AVO is awesome.

As you may know, I'm a big fan of forward modeling — predicting the seismic response of an earth model. So let's model the response the interface between a very simple model of only two rock layers. And we'll do it in only a few lines of Python. The workflow is straightforward:

1. Define the properties of a model shale; this will be the upper layer.
2. Define a model sandstone with brine in its pores; this will be the lower layer.
3. Define a gas-saturated sand for comparison with the wet sand. 
4. Define a range of angles to calculate the response at.
5. Calculate the brine sand's response at the interface, given the rock properties and the angle range.
6. For comparison, calculate the gas sand's response with the same parameters.
7. Plot the brine case.
8. Plot the gas case.
9. Add a legend to the plot.

That's it — nine lines! Here's the result:

Once we have rock properties, the key bit is in the middle:

    θ = range(0, 31)
    shuey = bruges.reflection.shuey2(vp0, vs0, ρ0, vp1, vs1, ρ1, θ)

shuey2 is one of the many functions in bruges — here it provides the two-term Shuey approximation, but it contains lots of other useful equations. Virtually everything else in our AVO plotting routine is just accounting and plotting.

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As in all these posts, you can follow along with the code in the Jupyter Notebook. You can view this on GitHub, or run it yourself in the increasingly flaky MyBinder (which is down at the time of writing... I'm working on an alternative).

What would you like to see in x lines of Python? Requests welcome!

What's that funny noise?

Seismic reflections are strange noises. Around 50 Hz, narrow band, very quiet, and difficult to interpret. It is possible to convert seismic traces (active or passive) into audible sound with a shift in pitch and a time stretch.

Made by the legendary Emory Cook, who recorded everything from steel bands to racing cars to ionospheric noises to this treatment of Hugo Benioff's earthquake recordings. Epic.

Curiously the audification thing has never really caught on in exploration geophysics — a bit surprising, given the fascination with spectral decomposition over the last 15 years or so. And especially so when you consider that our hearing has a dynamic range of about 100 dB, which is comparable to, indeed slightly greater than, our vision (about 90 dB).

Paolo Dell'Aversana of ENI wants to change that. Rather than listening to 'raw' seismic, he's sending it to a MIDI interface and listening to it as a piano roll. Just try to imagine playing seismic on a piano for a second, then listen to his weird and wonderful results — at 9:45 in this EAGE video:

In this EAGE E-Lecture Paolo Dell'Aversana discusses how digital music technology can support geophysical data analysis and interpretation. If you've read any of Dell'Aversana's articles, you'll know he has one of the most creative minds in exploration geophysics. Skip to 9:45 for the crazy seismic piano roll.

On the subject of weird sounds, one of my favourite Wikipedia pages is List of unexplained sounds. I especially love the eerie recordings of mysterious underwater noises, like this one called Upsweep:

No-one knows what makes that noise! My money's on a volcanic vent, but that doesn't explain the seasonality. Maybe we should do a hackathon on these unexaplained sounds some time. If you know of any others — I'd love tohear about them.

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If you enjoy strange infrasound as much as I do, I recommend following these two scientists on Twitter:

• Milton Garces, aka Infrasound Hunter and director of the Infrasound LAb at the University of Hawaii.

• Stephen Gibbons, NORSAR seismologist who monitors nuclear testing with seismology.

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If you really like strange noises, don't forget to check out the Undersampled Radio podcast!