| Item type |
itemtype_ver1(1) |
| 公開日 |
2021-12-06 |
| タイトル |
|
|
タイトル |
Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes |
|
言語 |
en |
| 著者 |
Maeda, Yuta
Yamanaka, Yoshiko
Ito, Takeo
Horikawa, Shinichiro
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
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. |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Neural networks |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
fuzzy logic |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Volcano monitoring |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Volcano seismology |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
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. |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Online Published: 22 December 2020 |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Oxford University Press |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1093/gji/ggaa593 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0956-540X |
| 書誌情報 |
en : Geophysical Journal International
巻 225,
号 1,
p. 416-444,
発行日 2021-04
|
| ファイル公開日 |
|
|
日付 |
2021-12-06 |
|
日付タイプ |
Available |
ggaa593.pdf (9.8 MB)
