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Towards advancing the earthquake forecasting by machine learning of satellite data.
Xiong, Pan; Tong, Lei; Zhang, Kun; Shen, Xuhui; Battiston, Roberto; Ouzounov, Dimitar; Iuppa, Roberto; Crookes, Danny; Long, Cheng; Zhou, Huiyu.
Afiliación
  • Xiong P; Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, United Kingdom.
  • Tong L; School of Informatics, University of Leicester, Leicester, United Kingdom.
  • Zhang K; School of Electrical Engineering, Nantong University, Nantong, China.
  • Shen X; National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China. Electronic address: shenxh@seis.ac.cn.
  • Battiston R; Department of Physics, University of Trento, Trento, Italy; National Institute for Nuclear Physics, the Trento Institute for Fundamental Physics and Applications, Trento, Italy.
  • Ouzounov D; Center of Excellence in Earth Systems Modeling & Observations, Chapman University, Orange, CA, USA.
  • Iuppa R; Department of Physics, University of Trento, Trento, Italy; National Institute for Nuclear Physics, the Trento Institute for Fundamental Physics and Applications, Trento, Italy.
  • Crookes D; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, United Kingdom.
  • Long C; School of Computer Science and Engineering, Nanyang Technological University, Singapore.
  • Zhou H; School of Informatics, University of Leicester, Leicester, United Kingdom.
Sci Total Environ ; 771: 145256, 2021 Jun 01.
Article en En | MEDLINE | ID: mdl-33736153
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Total Environ Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Total Environ Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido