2024-03-29T08:17:11Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:02001674
2023-01-16T05:06:50Z
488:489:490
Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes
Maeda, Yuta
Yamanaka, Yoshiko
Ito, Takeo
Horikawa, Shinichiro
open access
This article has been accepted for publication in [Geophysical Journal International] ©: [2020] [The Author(s)] Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Neural networks
fuzzy logic
Volcano monitoring
Volcano seismology
We propose a new algorithm, focusing on spatial amplitude patterns, to automatically detect volcano seismic events from continuous waveforms. Candidate seismic events are detected based on signal-to-noise ratios. The algorithm then utilizes supervised machine learning to classify the existing candidate events into true and false categories. The input learning data are the ratios of the number of time samples with amplitudes greater than the background noise level at 1 s intervals (large amplitude ratios) given at every station site, and a manual classification table in which ‘true’ or ‘false’ flags are assigned to candidate events. A two-step approach is implemented in our procedure. First, using the large amplitude ratios at all stations, a neural network model representing a continuous spatial distribution of large amplitude probabilities is investigated at 1 s intervals. Second, several features are extracted from these spatial distributions, and a relation between the features and classification to true and false events is learned by a support vector machine. This two-step approach is essential to account for temporal loss of data, or station installation, movement, or removal. We evaluated the algorithm using data from Mt. Ontake, Japan, during the first ten days of a dense observation trial in the summit region (2017 November 1–10). Results showed a classification accuracy of more than 97 per cent.
Online Published: 22 December 2020
Oxford University Press
2021-12-06
2021-04
eng
journal article
VoR
http://hdl.handle.net/2237/0002001674
https://nagoya.repo.nii.ac.jp/records/2001674
https://doi.org/10.1093/gji/ggaa593
0956-540X
Geophysical Journal International
225
1
416
444
https://nagoya.repo.nii.ac.jp/record/2001674/files/ggaa593.pdf
application/pdf
9.8 MB
2021-12-06