Work

Anomaly Detection Based on 1DCNN Self-Attention Networks for Seismic Electric Signals

Year: 2025

Type: article

Abstract: The application of deep learning to seismic electric signal (SES) anomaly detection remains underexplored in geophysics. This study introduces the integration of a 1D convolutional neural network (1DCNN) with a self-attention mechanism to automate SES analysis in a station in a certain place in China. Utilizing physics-informed data augmentation, our framework adapts to real-world interference scenarios, including subway operations and tidal fluctuations. The model achieves an F1-score of 0.9797 on a 7-year dataset, demonstrating superior robustness and precision compared to traditional manual interpretation. This work establishes a practical deep learning solution for real-time geoelectric anomaly monitoring, offering a transformative tool for earthquake early warning systems.

Source: Computers

DOI: 10.3390/computers14070263

Authors: Wei Li, Huaqin Gu, Yanlin Wen, Wenzhou Zhao, Zhaobin Wang

Institution: Lanzhou University

Language: English

Topic: Earthquake Detection and Analysis

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,172