Epicentral region estimation using convolutional neural networks
Visualitza/Obre
10.1007/978-3-030-95467-3_39
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/363035
Tipus de documentText en actes de congrés
Data publicació2022
EditorSpringer Nature
Condicions d'accésAccés obert
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ProjecteUPC-COMPUTACION DE ALTAS PRESTACIONES VIII (AEI-PID2019-107255GB-C22)
MATHROCKS - Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics (EC-H2020-777778)
MATHROCKS - Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics (EC-H2020-777778)
Abstract
Recent works have assessed the capability of deep neural networks of estimating the epicentral source region of a seismic event from a single-station three-channel signal. In all the cases, the geographical partitioning is performed by automatic tessellation algorithms such as the Voronoi decomposition. This paper evaluates the hypothesis that the source region estimation accuracy is significantly increased if the geographical partitioning is performed considering the regional geological characteristics such as the tectonic plate boundaries. Also, it raises the transformation of the training data to increase the accuracy of the predictive model based on a Projected Coordinate Reference (PCR) System.
A deep convolutional neural network (CNN) is applied over the data recorded by the broadband stations of the Venezuelan Foundation of Seismological Research (FUNVISIS) in the region of 9.5 to 11.5ºN and 67.0 to 69.0ºW between April 2018 and April 2019. In order to estimate the epicentral source region of a detected event, several geographical tessellations provided by seismologists from the area are employed. These tessellations, with different number of partitions, consider the fault systems of the study region (San Sebastián, La Victoria and Morón fault systems). The results are compared to the ones obtained with automatic partitioning performed by the k-means algorithm.
CitacióCruz, S. [et al.]. Epicentral region estimation using convolutional neural networks. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 7th International Conference, LOD 2021: Grasmere, UK, October 4-8, 2021: revised selected papers, part I". Springer Nature, 2022, p. 541-552. ISBN 978-3-030-95467-3. DOI 10.1007/978-3-030-95467-3_39.
ISBN978-3-030-95467-3
Versió de l'editorhttps://link.springer.com/chapter/10.1007/978-3-030-95467-3_39
Col·leccions
- Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos [285]
- VIRTUOS - Virtualisation and Operating Systems - Ponències/Comunicacions de congressos [14]
- CAP - Grup de Computació d'Altes Prestacions - Ponències/Comunicacions de congressos [784]
- Computer Applications in Science & Engineering - Ponències/Comunicacions de congressos [81]
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.948]
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LOD_2021_Camera-Ready_74+(3).pdf | 3,408Mb | Visualitza/Obre |