Figure 1: Panel (a) shows an oblique view of one of the models used for studying asperities synchronization. The megathrust embeds two asperities of equal size and friction (modified from [3]). Panel (b) shows incremental maps of surface displacement associated with a synchronized asperities rupture. Time since the beginning of the gelquake is reported in each panel.
How do you think Machine Learning can help understanding faulting and earthquakes?
Machine Learning (ML) is providing a great input for seismic hazard assessment and for understanding faulting, starting from laboratory experiments. Rouet-Leduc and coworkers in 2018 showed that in a relatively simple laboratory fault analogue, ML can predict the earthquake timing. The prediction is accomplished by analysing and interpreting a series of characteristics, or features, hidden in acoustic emissions generated by a fault gouge material undergone a direct shear, which represents tectonic loading in nature.
The work of Rouet-Leduc started a new wave of studies aimed at understanding and predicting fault seismic behaviour using ML. We can say that three main research branches of applied ML for seismic hazard are active at the moment: 1) laboratory geodesy, 2) laboratory seismology, and 3) field studies. The latter is showing the great potential of ML to detect and characterize earthquakes, precursors to earthquakes (foreshocks and slow slip), and aftershocks..
So ML has great potential in the broad field of seismic hazard, but what contribution does ML give to your research in particular?
I think I’ve been lucky to be in the right place and at the right time when my friends FG and JB asked: “why don’t you use ML in your experiments, Fabio?”. This is the way I started playing with ML algorithms. In the beginning, I asked ML to predict the time to fault failure in a relatively simple experiment. I then used the intuition that the displacement field I was measuring with my video camera was equivalent to a dense, homogeneously spaced geodetic network, which is why gelquakes are nowadays also known as laboratory-geodesy experiments [2]. And with the help of a group of colleagues, we demonstrated that ML predicts the timing and size of analogue earthquakes by deciphering the spatially and temporally complex surface deformation history (Figure 2). I’m very happy to note that this paper was within the top downloaded papers in 2018-2019 in the journal Geophysical Research Letters.