Proposal for a system model for offline seismic event detection in Colombia
This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system proce...
- Autores:
-
Miranda, Julian
Flórez Abril, Angélica
Ospina, Gustavo
Gamboa, Ciro Alberto
Altuve, Miguel
FLOREZ-GONGORA, CARLOS
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/710
- Palabra clave:
- seismic event detection
detection model
seismology
classification
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
| Summary: | This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system. |
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