Empowering Machine Learning Forecasting of Labquake Using Event‐Based Features and Clustering Characteristics
[1];
[1];
[2];
[1];
[3];
[4]
- Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Potsdam Germany
- Department of Earth Sciences Statewide California Earthquake Center University of Southern California Los Angeles CA USA
- Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Potsdam Germany, Institute of Earth and Environmental Sciences Universität Potsdam Potsdam Germany
- Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Potsdam Germany, Department of Earth Sciences Free University Berlin Berlin Germany
Abstract Following recent advances of machine learning (ML), we present a novel approach to extract spatiotemporal seismo‐mechanical features from Acoustic Emission (AE) catalogs to empower ML‐based forecasting. The AE data were recorded during laboratory stick‐slip experiments on granite samples cut by rough faults. Based on the features computed for a past time window, a random forest (RF) classifier is used to forecast the occurrence of a large magnitude event ( M AE > 3.5) in the next time window. Event‐based features allow us to associate informative time‐space characteristics to each feature and nearest‐neighbor clustering analysis enables us to separate background and clustered seismicity and train individual models. The results show that the separation of AEs enhances the forecasting accuracy from 73.2% for the entire catalog up to 82.1% and 89.0% if background and clustered events are used separately. The presented new approach may be upscaled for applications to forecast tectonic earthquakes.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- SC0016520
- OSTI ID:
- 2373331
- Journal Information:
- Journal of Geophysical Research: Machine Learning and Computation, Journal Name: Journal of Geophysical Research: Machine Learning and Computation Journal Issue: 2 Vol. 1; ISSN 2993-5210
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- Country unknown/Code not available
- Language:
- English
Similar Records
Intermittent Criticality Multi‐Scale Processes Leading to Large Slip Events on Rough Laboratory Faults
Learning Forecasts of Rare Stratospheric Transitions from Short Simulations