Application of deep learning-based neural networks using theoretical seismograms as training data for locating earthquakes in the Hakone volcanic region, Japan

• 資料種別: 記事
• タイトル: Application of deep learning-based neural networks using theoretical seismograms as training data for locating earthquakes in the Hakone volcanic region, Japan
• 著者・編者: Daisuke Sugiyama; Seiji Tsuboi; Yohei Yukutake
• 出版事項: 日本 : Springer Nature
• 出版年月日等: 2021-06-25
• 出版年（W3CDTF）: 2021-06-25
• タイトル（掲載誌）: EPS : Earth, Planets and Space
• 巻号年月日等（掲載誌）: 73(135)
• 掲載巻: 73(135)
• ISSN（掲載誌）: 1880-5981
• ISSN-L（掲載誌）: 1343-8832
• 本文の言語コード: eng
• DOI: 10.1186/s40623-021-01461-w; https://doi.org/10.1186/s40623-021-01461-w
• 国立国会図書館永続的識別子: info:ndljp/pid/11740893; https://dl.ndl.go.jp/pid/11740893
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他; https://dl.ndl.go.jp/collections/B00035
• 収集根拠: オンライン資料収集制度
• 受理日（W3CDTF）: 2021-10-05T00:13:24+09:00
• 保存日（W3CDTF）: 2021-10-01
• 記録形式（IMT）: application/pdf
• オンライン閲覧公開範囲: 国立国会図書館内限定公開
• デジタル化資料送信: 図書館・個人送信対象外
• 遠隔複写可否（NDL）: 可
• 掲載誌（URI）: https://earth-planets-space.springeropen.com/articles
• 掲載誌（国立国会図書館永続的識別子）: info:ndljp/pid/11667897; https://dl.ndl.go.jp/pid/11667897
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

• 要約等: コレクション : 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他
• DOI: 10.1186/s40623-021-01461-w; https://doi.org/10.1186/s40623-021-01461-w; 10.21203/rs.3.rs-295787/v1; https://doi.org/10.21203/rs.3.rs-295787/v1
• オンライン閲覧公開範囲: インターネット公開
• 関連情報: Additional file 1 of Application of deep learning-based neural networks using theoretical seismograms as training data for locating earthquakes in the Hakone volcanic region, Japan; https://cir.nii.ac.jp/crid/1882835443041352192
• 関連情報（URI）: http://id.ndl.go.jp/digimeta/11740893; https://dl.ndl.go.jp/pid/11740893; https://link.springer.com/content/pdf/10.1186/s40623-021-01461-w.pdf; https://link.springer.com/article/10.1186/s40623-021-01461-w/fulltext.html
• 参照: Reappraisal of volcanic seismicity at the Kirishima volcano using machine learning; https://cir.nii.ac.jp/crid/1360584340714499456; Neural phase picker trained on the Japan meteorological agency unified earthquake catalog; https://cir.nii.ac.jp/crid/1360588379370845824; Recent advances in earthquake seismology using machine learning; https://cir.nii.ac.jp/crid/1360865815689043456; Discriminating seismic events using 1D and 2D CNNs : applications to volcanic and tectonic datasets; https://cir.nii.ac.jp/crid/2050870367085226240
• 参照: Spectral-element simulations of global seismic wave propagation-II. Three-dimensional models, oceans, rotation and self-gravitation; https://cir.nii.ac.jp/crid/1360011142931119872; Spectral-element simulations of global seismic wave propagation-I. Validation; https://cir.nii.ac.jp/crid/1360011142934664192; 2d and 3D elastic wave propagation by a pseudo-spectral domain decomposition method; https://cir.nii.ac.jp/crid/1360011145591265152; Forward and adjoint simulations of seismic wave propagation on emerging large-scale GPU architectures; https://cir.nii.ac.jp/crid/1360011146337194624; Robust and Fast Probabilistic Source Parameter Estimation from Near‐Field Displacement Waveforms Using Pattern Recognition; https://cir.nii.ac.jp/crid/1360013172316970880; Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning; https://cir.nii.ac.jp/crid/1360013173261249536; Identification and picking of<i>S</i>phase using an artificial neural network; https://cir.nii.ac.jp/crid/1360013188509507200; The spectral-element method in seismology; https://cir.nii.ac.jp/crid/1360294647030919552; A magma‐hydrothermal system beneath Hakone volcano, central Japan, revealed by highly resolved velocity structures; https://cir.nii.ac.jp/crid/1360567179756104704; Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives; https://cir.nii.ac.jp/crid/1360576120931272448; Passive seismic imaging of subwavelength natural fractures: theory and 2-D synthetic and ultrasonic data tests; https://cir.nii.ac.jp/crid/1360576122550381312; High-frequency simulations of global seismic wave propagation using SPECFEM3D_GLOBE on 62K processors; https://cir.nii.ac.jp/crid/1360576122938618112; The spectral element method: An efficient tool to simulate the seismic response of 2D and 3D geological structures; https://cir.nii.ac.jp/crid/1360576285947927296; Learning Spatiotemporal Features with 3D Convolutional Networks; https://cir.nii.ac.jp/crid/1360855571089702400; Machine Learning Predicts Laboratory Earthquakes; https://cir.nii.ac.jp/crid/1360855571162705408; Probabilistic point source inversion of strong‐motion data in 3‐D media using pattern recognition: A case study for the 2008<i>M</i>_<i>w</i>5.4 Chino Hills earthquake; https://cir.nii.ac.jp/crid/1360857597352045312; A framework for fast probabilistic centroid-moment-tensor determination—inversion of regional static displacement measurements; https://cir.nii.ac.jp/crid/1360857597631399296; Solving probabilistic inverse problems rapidly with prior samples; https://cir.nii.ac.jp/crid/1360857598104911232; Artificial neural network-based seismic detector; https://cir.nii.ac.jp/crid/1360857613593548416; Discrimination of Seismic Signals from Earthquakes and Tectonic Tremor by Applying a Convolutional Neural Network to Running Spectral Images; https://cir.nii.ac.jp/crid/1361413117941286400; Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset; https://cir.nii.ac.jp/crid/1361418519211379072; Object Recognition with Gradient-Based Learning; https://cir.nii.ac.jp/crid/1361418519509543040; Original approach for the localisation of objects in images; https://cir.nii.ac.jp/crid/1361699994587026432; Broadband modeling of the 2002 Denali fault earthquake on the Earth Simulator; https://cir.nii.ac.jp/crid/1361981471253364096; A 1.8 trillion degrees-of-freedom, 1.24 petaflops global seismic wave simulation on the K computer; https://cir.nii.ac.jp/crid/1362544418637883904; Generalized Seismic Phase Detection with Deep Learning; https://cir.nii.ac.jp/crid/1362825893263312768; Taking the Human Out of the Loop: A Review of Bayesian Optimization; https://cir.nii.ac.jp/crid/1363107370262763904; Implementation of a Multistation Approach for Automated Event Classification at Piton de la Fournaise Volcano; https://cir.nii.ac.jp/crid/1363388844961564544; Long-term recurrent convolutional networks for visual recognition and description; https://cir.nii.ac.jp/crid/1363388845051516160; Going deeper with convolutions; https://cir.nii.ac.jp/crid/1363951794000700160; Convolutional neural network for earthquake detection and location; https://cir.nii.ac.jp/crid/1363951794885739648; 近地地震のマグニチュード; https://cir.nii.ac.jp/crid/1390001204304597248; Deep Convolutional Neural Network for Cloud Coverage Estimation from Snapshot Camera Images; https://cir.nii.ac.jp/crid/1390282680200069888
• 参照（URI）: https://cir.nii.ac.jp/crid/1370576118714912773
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp; Crossref; https://www.crossref.org; 科学研究費助成事業データベース; https://kaken.nii.ac.jp/ja
• 書誌ID（NDLBibID）: 11740893; http://id.ndl.go.jp/bib/11740893