Relocating acoustic emission in rocks with unknown velocity structure with machine learning
Abstract
Inversion of hypocenters is the first and most fundamental step in the study of seismic activities. It requires solving the non-linear relation between the travel time and hypocenter location, which is heavily dependent on the knowledge of the medium properties, most importantly the velocity structure. In this study, we prove that machine learning using artificial neural networks (ANNs) can relocate hypocenters without a priori knowledge of the velocity structure. We train a generalized ANN model with acoustic emissions (AEs) created by breaking pencil leads at known locations on a laboratory fault, using the relative P-wave arrival time as the input and AE source locations as the output. The resultant ANN model accurately relocates AEs on the fault surface. This study suggests that ANN can provide an effective and accurate approach for relocating seismic events in a medium with unknown velocity structures.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.S43E0709Z
- Keywords:
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- 0555 Neural networks;
- fuzzy logic;
- machine learning;
- COMPUTATIONAL GEOPHYSICS;
- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 1914 Data mining;
- INFORMATICS;
- 1942 Machine learning;
- INFORMATICS