Work

Which is better: deep-learning or manual seismic arrival-time picking?

Year: 2025

Type: article

Abstract: Earthquake locations and catalogs from routine earthquake monitoring are typically based on manually reviewed arrival-time picks from classical, rule-based automatic pickers. High-performance, deep-learning (DL) pickers can replace this standard approach, rapidly delivering much larger and complete catalogs. A transition to routine monitoring based on DL picks requires that resulting catalogs include all events identified by current procedures with locations of the same or higher quality. Here we verify these requirements by comparing the performance of DL and manual picking for earthquake relocation and tomographic inversion. We form a reference catalog with a subset of INGV bulletin events and picks from the 2016 Central Italy sequence. This catalog is re-picked using the DL picker PhaseNet trained on the Northern California Earthquake Data Center dataset and on the INSTANCE Italian dataset. We use these three pick sets for high-precision, nonlinear earthquake relocation and for 3D tomographic inversion and relocation. Relative to the high-precision relocations using routine picks, those using DL picks show improved organization and clustering, and, in a ground-truth test, smaller hypocenter separation for event pairs with more similar waveforms. The tomographic inversions show statistically better convergence and more organized relocations using the DL picks than with the rou-2 S. Cianetti et al. tine picks. We conclude that DL based monitoring can rapidly produce more consistent picks and higher quality catalogs than standard procedures, while freeing analyst time for improved quality control, assessment, interpretation, and dissemination of information on seismic activity, especially during significant seismic sequences.

Source: Geophysical Journal International

DOI: 10.1093/gji/ggaf494

Authors: Spina Cianetti, Anthony Lomax, Alberto Michelini, Carlo Giunchi

Institutions: INGV Sezione di Pisa, INGV Osservatorio Nazionale Terremoti

Language: English

Topic: Seismology and Earthquake Studies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,416

Funder: Istituto Nazionale di Geofisica e Vulcanologia