Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake

S Cianetti,G Saccorotti,D Keir,D Piccinini,S Gaviano,C Giunchi,R Bruni

doi:10.1029/2021jb023405

Abstract

AbstractThe increase of available seismic data prompts the need for automatic processing procedures to fully exploit them. A good example is aftershock sequences recorded by temporary seismic networks, whose thorough analysis is challenging because of the high seismicity rate and station density. Here, we test the performance of two recent Deep Learning algorithms, the Generalized Phase Detection and Earthquake Transformer, for automatic seismic phases identification. We use data from the December 2019 Mugello basin (Northern Apennines, Italy) swarm, recorded on 13 permanent and nine temporary stations, applying these automatic procedures under different network configurations. As a benchmark, we use a catalog of 279 manually repicked earthquakes reported by the Italian National Seismic Network. Due to the ability of deep learning techniques to identify earthquakes under poor signal‐to‐noise‐ratio (SNR) conditions, we obtain: (a) a factor 3 increase in the number of locations with respect to INGV bulletin and (b) a factor 4 increase when stations from the temporary network are added. Comparison between deep learning and manually picked arrival times shows a mean difference of 0.02–0.04 s and a variance in the range 0.02–0.07 s. The improvement in magnitude completeness is ∼0.5 units. The deep learning algorithms were originally trained using data sets from different regions of the world: our results indicate that these can be successfully applied in our case, without any significant modification. Deep learning algorithms are efficient and accurate tools for data reprocessing in order to better understand the space‐time evolution of earthquake sequences.

Highlights

Over the last few decades, the techniques used to automatically detect and locate earthquakes have dramatically evolved with the improvement of computing power
The deep learning algorithms used in this application were originally trained on data collected from different regions of the world: Our results indicate their capability to apply the knowledge gained in different contexts to totally unseen data
Two exceptions are constituted by stations CRCL and MOCL, which reported a huge number of detections, of which only 10% and 4%, respectively, are successfully associated and located

Summary

Introduction

Over the last few decades, the techniques used to automatically detect and locate earthquakes have dramatically evolved with the improvement of computing power. Amongst the first implemented and still most widely used techniques to automatically pick seismograms is comparing the short term average (STA) to a longer term average (LTA) of the seismic signal (or of a characteristic function of it) in sliding windows through the continuous data stream to pick the arrival of a seismic phase (Allen, 1978, 1982; Baer & Kradolfer, 1987) Another class of methods are those based on autoregressive models, which go beyond the bare analysis of the time and frequency domain variations of the seismic signal and instead try to fit the statistical properties of noise and signal segments of the waveforms.

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