Using Deep Learning to understand variations in fault zone properties: distinguishing foreshocks from aftershocks

Fault zone properties can change significantly during the seismic cycle in response to stress changes, microcracking and wall rock damage. Lab experiments show consistent changes in elastic properties prior to and after lab earthquakes (EQ) and previous works show that machine learning/deep learning (ML/DL) techniques are successful for capturing such changes. Here, we apply DL techniques to assess whether similar changes occur during the seismic cycle of tectonic EQ. The main motivation is to generalize lab-based findings to tectonic faulting, to predict failure and identify precursors. The novelty is that we use EQ traces as probing signals to estimate the fault state.

We train DL model to distinguish foreshocks, aftershocks and time to failure of the Mw 6.5 2016 Norcia EQ in central Italy, October 30th 2016. We analyze a 25-second window of 3-component data around the P- and S-wave arrivals for events near the Norcia fault with M>0.5 and ±2 months before/after the Norcia mainshock. Normalized waveforms are used to train a Convolutional Neural Network (CNN). As a first task we divide events into two classes (foreshocks/aftershocks), and then refine the classification as a function of time-to-failure (TTF) for the mainshock. Our DL model perform very well for TTF classification into 2, 4, 8, or 9-classes for the 2 months before/after the mainshock. We explore a range of seismic ray paths near, through, and away from the Norcia mainshock fault zone. Model performance exceeds 90% for most stations. Waveform investigations show that wave amplitude is not the key factor; other waveform properties dictate model performance. Models derived from seismic spectra, rather than time-domain data, are equally good. We challenged the model in several ways to confirm the results. We found reduced performance in training the model with the wrong mainshock time and by omitting data immediately before/after the mainshock. Foreshock/aftershock identification is significantly degraded also by removing high frequencies (filtering seismic data above 25 Hz). We tested data from different years to understand seasonality at individual stations for the time period September to December and removed these effects. Comparing these seasonality effects defined from noise with our EQ results shows that foreshocks/aftershocks for the 2016 Norcia mainshock are well resolved. Training with data containing EQ offers a huge increase in classification performance over noise only, proving that EQ signals are the sole that enable assessing timing as a function of the fault status. To confirm our results and understand which stations are able to detect changes of fault properties we perform a further test cleaning the signals from the seasonality by confounding the DL with a shuffled noise (adversarial training).

We conclude that DL is able to recognize variations in the stress state and fracture during the seismic cycle. The model uses EQ-induced changes in seismic attenuation to distinguish foreshocks from aftershocks and time to failure. This is an important step in ongoing efforts to improve EQ prediction and precursor identification through the use of ML and DL.