From labquakes to megathrusts: scaling deep learning based pickers over 15 orders of magnitude

Shi, Peidong, Meier, Men-Andrin, Villiger, Linus, Tuinstra, Katinka, Selvaurai, Paul Antony, Lanza, Federica, Yuan, Sanyi, Obermann, Anne, Mesimeri, Maria, Munchmeyer, Jannes, Bianchi, Patrick and Weimer, Stefan 2024. From labquakes to megathrusts: scaling deep learning based pickers over 15 orders of magnitude. Journal of Geophysical Research: Machine Learning and Computation 11 (4) , e2024JH000220. 10.1029/2024JH000220

PDF - Published Version Available under License Creative Commons Attribution. Download (4MB)

Abstract

The application of machine learning techniques in seismology has greatly advanced seismological analysis, especially for earthquake detection and seismic phase picking. However, machine learning approaches still face challenges in generalizing to data sets that differ from their original training setting. Previous studies focused on retraining or transfer-learning models for these scenarios, but require high-quality labeled data sets. This paper demonstrates a new approach for augmenting already trained models without the need for additional training data. We propose four strategies—rescaling, model aggregation, shifting, and filtering—to enhance the performance of pre-trained models on out-of-distribution data sets. We further devise various methodologies to ensemble the individual predictions from these strategies to obtain a final unified prediction result featuring prediction robustness and detection sensitivity. We develop an open-source Python module quakephase that implements these methods and can flexibly process input continuous seismic data of any sampling rate. With quakephase and pre-trained ML models from SeisBench, we perform systematic benchmark tests on data recorded by different types of instruments, ranging from acoustic emission sensors to distributed acoustic sensing, and collected at different scales, spanning from laboratory acoustic emission events to major tectonic earthquakes. Our tests highlight that rescaling is essential for dealing with small-magnitude seismic events recorded at high sampling rates as well as larger magnitude events having long coda and remote events with long wave trains. Our results demonstrate that the proposed methods are effective in augmenting pre-trained models for out-of-distribution data sets, especially in scenarios with limited labeled data for transfer learning.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Earth and Environmental Sciences
Publisher:	Wiley
ISSN:	2993-5210
Date of First Compliant Deposit:	4 December 2024
Date of Acceptance:	3 September 2024
Last Modified:	09 Dec 2024 12:40
URI:	https://orca.cardiff.ac.uk/id/eprint/174100

Actions (repository staff only)

Edit Item