Toward a Polarity Focal Mechanism Estimation via Deep Learning for small to moderate Italian earthquakes

One of the main tasks in seismology is the source characterization after an earthquake, in particular the estimate of the orientation of the fault on which an earthquake occurs and the direction of the slip. Currently, most seismological observatories compute moment tensor solutions for earthquakes above a certain magnitude threshold, but, for small to moderate earthquakes (i.e. aftershock sequences), or for large but close in time events, focal mechanism by first arrival polarities are often the only source information available (Sarao et al., 2021).

Focal mechanisms are important to better define the activated faults, to help in understanding the seismotectonic process, to improve the predicted ground shakings for early warning, the tsunami alert and the seismic hazard assessment. For these purposes, it becomes essential to produce and disseminate an estimate of the earthquake source parameters even for small events. Recently, machine learning techniques have gained significant attention and usage in various fields, including seismology where these algorithms have emerged as powerful tools in providing new insight into the earthquakes data analysis such as the prediction of the seismic wave's first arrivals polarities which can be used to compute focal mechanisms.

We present here a workflow developed to obtain earthquake focal mechanisms starting from the first p-wave polarities estimated through the method proposed by Ross et al (2018).

Our procedure consists of two stages: in the first stage, we use a combination of the available INGV web services (Bono et al., 2021) and the ObsPy functions to download the earthquake hypocentral location. We recover the waveforms recorded by the stations in the 0 -120 km distance range, and we create an input file with the appropriate information required for the prediction of the polarities for each waveform. We then use the convolutional neural network (CNN) proposed in Ross et al (2018) to obtain the polarities for each waveform, which can be UP, DOWN, or UNKNOWN. The second stage of the developed procedure aims to use the polarities that have been predicted to determine the focal mechanisms of the selected earthquakes. To do this, we use a modern Python implementation of HASH code (originally proposed in Fortran by Hardebeck et al. 2002, 2003) called SKASH (Skoumal et al. submitted). Finally, we present an application of this procedure to the September 2023, Marradi (Central Italy), seismic sequence that has been characterized by a magnitude Mw 4.9 mainshock followed by over 70 aftershocks in the magnitude range 2 - 3.4. Here, we focused on the estimation of the focal mechanism for events down to M 2.0. The application of the presented workflow permits to gain useful information about the kinematics of the earthquakes in the sequence, obtaining thus a more precise characterization of the activated structures.