A while back, I wrote about machine learning safety measures. I was thinking about how easy it is to accidentally make terrible models (e.g. training a support vector machine on unscaled data), or misuse good models (e.g. forgetting to scale data before making a prediction). I suggested that one solution might be to make tools that help spot these kinds of mistakes: “[We should build] software to support good practice. Many of the problems I’m talking about are quite easy to catch, or at least warn about, during the training and evaluation process. Unscaled features, class imbalance, correlate ..read more

After almost 12 years of consulting, teaching, writing, and hacking, it’s time for Agile* to close its laptops for the last time. We’ll be shutting down at the end of September. When I quit my job and moved to Nova Scotia in 2010, I had no idea if Agile* was going to work at all. I knew there was a chance I’d be looking for work within a year, possibly even having to move back to Calgary, or on to somewhere else to find it. But with Evan — and later Ben, Justin, Kara, Tracy, Diego, Martin and Rob — we did more than just survive. We built a solid business providing services to governments, star ..read more

If you use Python on a regular basis, you might have noticed that Pandas is eating everything, not just things made out of bamboo. But one thing Pandas is not useful for is 2-D or 3-D (or more-D) seismic data. Its DataFrames are implicitly two-dimensional tables, and shine when the columns represent all sorts of different quantities, e.g. different well logs. But, the really powerful thing about a DataFrame is its index. Instead of having row 0, row 1, etc, we can use an index that makes sense for the data. For example, we can use depth for the index and have row 13.1400 and row 13.2924, etc ..read more

Do you have an idea for an app? Or maybe a useful bit of code you want to share with others, but you’re not sure where to start? Lots of people come to our Geocomputing class — which is for outright beginners — saying, "I want to build an app". Most of them are thinking of a mobile or desktop app, but most beginners don't know much about the alternatives. Getting useful software into other people’s hands doesn’t necessarily mean making a desktop application. Alternatives include programming libraries, command line tools, and web applications with or without public machine interfacecs (so-calle ..read more

There’s a teaching method originating in medicine known as “see one, do one, teach one”. I like it because it underscores hands-on practice and knowledge sharing as essential steps in developing a craft — and it works. Today, I want to urge you to take a challenge, then make one for others. First, what’s the challenge? A couple of years ago, inspired by the annual Advent of Code challenges, we introduced the kata, a set of coding challenges especially for geoscientists. For a long time we sent them to students in our Geocomputing class, to encourage them to keep coding. Now we just tell every ..read more

One of the wonderful, but also baffling, things about machine learning is that there are so many ways to do it. At some very high level, most of them do something like this (highlighting some jargon): The human settles on a task (“Predict lithology”) and finds a bunch of data relevant to that task (say, some well logs A, B, and C). Then the human has to come up with some known instances or examples where these well log data go with those lithology labels. Stuff the logs into an equation. Not an equation like A + B + C, because there’s nothing to tweak in that equation. The equation needs p ..read more

There are several really nice comparisons between various algorithms in the Scikit-Learn documentation. The most famous, and useful, one is probably the classifier comparison: A comparison of classification algorithms. Each row is a different dataset; each column (except the first) is a different classifier, each trying to separate the blue and red points. The accuracy score of each classifier is show in the lower right corner of each plot. There’s so much to look at in this one plot! There’s also a very nice clustering algorithm comparison, and this anomaly detection comparison. As usual wit ..read more

As December rolls around again, thoughts turn to the Advent of Code, I mean Christmas, Jul, Hanukkah, Kwanzaa, Ōmisoka, Newtonmas, Solstice, Dongzhi, or whatever you like to celebrate at this time of year. The end of 2021 is arguably sufficient cause for celebration on its own. Just don’t let your guard down in 2022! Now, wherever you are, light the fire, chill out in the shade, pour yourself a glass of what you fancy, and check out this list of nerdtastic gifts for your favourite geoscientist, retired geoscientist, or geoscientist-to-be. Actual rock When giving to a geologist, you can’t go wr ..read more

In the recent How deep are the presents? post, I mentioned that I got a telescope this year — and I encouraged you to get one, because I kind of wish I’d got mine years ago. Since the observing conditions aren’t great tonight and I’m indoors anyway, I thought I’d elaborate a bit. Not Hubble The fun might not be obvious to all. Superficially, the experience is terrible — you read about some interesting object, noting its spectacular appearance in the obligatory Hubble photo, only to spend 45 minutes hunting for it before realizing it must be that dim grey smudge you tried to wipe off ..read more

Brian Romans (a geology professor at Virginia Tech) asked a great question in the Software Underground’s Slack earlier this month: “I was teaching my Seismic Stratigrapher course the other day and a student asked me about the origin of ‘side lobes’ on the Ricker wavelet. I didn’t have a great answer [...] what is a succinct explanation for the side lobes?” Questions like this are fantastic because they really aren’t easy to answer. There’s usually a breadcrumb trail of concepts that lead to an answer, but the trail might be difficult to navigate, and some of those breadcrumbs will lead to more ..read more