Random forest parameterization for earthquake catalog generation
Visualitza/Obre
10.1007/978-3-030-64583-0_22
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/335328
Tipus de documentText en actes de congrés
Data publicació2020
EditorSpringer
Condicions d'accésAccés obert
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ProjecteMATHROCKS - Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics (EC-H2020-777778)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
ChEESE - Centre of Excellence for Exascale in Solid Earth (EC-H2020-823844)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
ChEESE - Centre of Excellence for Exascale in Solid Earth (EC-H2020-823844)
Abstract
An earthquake is the vibration pattern of the Earth’s crust induced by the sliding of geological faults. They are usually recorded for later studies. However, strong earthquakes are rare, small-magnitude events may pass unnoticed and monitoring networks are limited in number and efficiency. Thus, earthquake catalog are incomplete and scarce, and researchers have developed simulators of such catalogs. In this work, we start from synthetic catalogs generated with the TREMOL-3D software. TREMOL-3D is a stochastic-based method to produce earthquake catalogs with different statistical patterns, depending on certain input parameters that mimics physical parameters. When an appropriate set of parameters are used, TREMOL-3D could generate synthetic catalogs with similar statistical properties observed in real catalogs. However, because of the size of the parameter space, a manual searching becomes unbearable. Therefore, aiming at increasing the efficiency of the parameter search, we here implement a Machine Learning approach based on Random Forest classification, for an automatic parameter screening. It has been implemented using the machine learning Python’s library SciKit Learn.
CitacióLlácer, D. [et al.]. Random forest parameterization for earthquake catalog generation. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020: Siena, Italy, July 19-23, 2020: revised selected papers, part I". Berlín: Springer, 2020, p. 233-243. ISBN 978-3-030-64583-0. DOI 10.1007/978-3-030-64583-0_22.
ISBN978-3-030-64583-0
Versió de l'editorhttps://link.springer.com/chapter/10.1007/978-3-030-64583-0_22
Col·leccions
- 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 [82]
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.954]
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LOD_2020_paper_35_camera_ready.pdf | 752,2Kb | Visualitza/Obre |