A Deep-Learning-Based Framework for Focal Mechanism Determination and Its Application to the 2022 Luding Earthquake Sequence

TL;DR

This paper introduces a deep learning framework that automates the determination of earthquake focal mechanisms using P-wave first-motion polarity data, enhancing regional seismic analysis.

Contribution

It presents a novel deep neural network approach for automatic polarity detection and applies it to real earthquake data, improving efficiency and accuracy in focal mechanism solutions.

Findings

Polarity detection recall of 97.4%

Polarity detection precision of 98.5%

Focal mechanisms consistent with regional fault structures

Abstract

P-wave first-motion polarity plays an important role in resolving focal mechanisms of small to moderate earthquakes (M <= 4.5). High-quality focal mechanism solutions for abundant small events can greatly improve our understanding of regional tectonics, fault geometries, and stress-field characteristics. In this study, we develop an automated focal mechanism determination framework that integrates deep neural networks with P-wave first-motion polarity observations, and apply it to the 2022 Luding earthquake sequence. The model is trained on 12 years (2009-2020) of manually annotated 100 Hz waveform records from the China National Seismic Network, achieving a polarity recall of 97.4 percent and a precision of 98.5 percent. After automatically determining the first-motion polarities, we invert focal mechanisms using the HASH method. The resulting focal mechanism solutions show high consistency with mapped fault structures in the Luding region, demonstrating the reliability and applicability of the proposed workflow.