How machine learning is helping seismologists understand earthquakes and predict them more efficiently

Knowable Magazine examined how seismologists are using machine learning techniques to predict earthquakes with better accuracy and understand how they occur:

One of the most common uses of machine learning in seismology is measuring the arrival time of seismic waves at a particular location, a process known as phase picking. Earthquakes generate two kinds of seismic waves, known as P and S waves, that affect the ground in different ways and show up as different types of squiggles on a seismogram. In the past, a seismologist would analyze data arriving from seismic sensors and hand-select what they gauged to be the start of P waves or S waves on those seismogram plots. Picking the starts of those waves accurately and precisely is important for understanding factors such as where exactly the earthquake hit. But phase picking is very time consuming.

In the past few years, seismologists have been using machine learning algorithms to pick seismic phases much faster than a human can. There are a number of automated methods that can do phase picking, but machine learning algorithms, which have been trained on huge volumes of data on past quakes, can identify a wide variety of signals from different types of tremors in a way that was not possible before. The practice is now so standard that the term “machine learning” is no longer stated in the titles of research papers, says Mousavi. “By default, everybody knows.”

The way AI is used as a blanket term for everything that isn’t generative AI does a lot of its subfields a disservice. Machine learning underpins a lot of what we see in those fields and it has a lot of usefulness in areas such as seismology. Earthquakes devastate and if there’s a way to forecast them better and save lives, I’m all for it.