The correlation polarization method: A novel automatic technique for seismic event detection and first-break picking

2021 ◽  
Author(s):  
Maher Nasr ◽  
Bernard Giroux ◽  
J. Christian Dupuis
Keyword(s):  
Event Detection ◽  
Seismic Event ◽  
Polarization Method ◽  
Seismic Event Detection ◽  
Automatic Technique

Automatic seismic event detection using migration and stacking: a performance and parameter study in Hengill, southwest Iceland

2017 ◽  
Vol 209 (3) ◽  
pp. 1866-1877 ◽  
Author(s):  
F. Wagner ◽  
A. Tryggvason ◽  
R. Roberts ◽  
B. Lund ◽  
Ó. Gudmundsson
Keyword(s):  
Event Detection ◽  
Seismic Event ◽  
Parameter Study ◽  
Seismic Event Detection ◽  
A Performance

scholarly journals High-resolution seismic event detection using local similarity for Large-N arrays

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Zefeng Li ◽  
Zhigang Peng ◽  
Dan Hollis ◽  
Lijun Zhu ◽  
James McClellan
Keyword(s):  
High Resolution ◽  
Event Detection ◽  
Seismic Event ◽  
Local Similarity ◽  
Large N ◽  
Seismic Event Detection

scholarly journals Proposal for a System Model for Offline Seismic Event Detection in Colombia

2020 ◽  
Vol 12 (12) ◽  
pp. 231
Author(s):  
Julián Miranda ◽  
Angélica Flórez ◽  
Gustavo Ospina ◽  
Ciro Gamboa ◽  
Carlos Flórez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Event Detection ◽  
Seismic Event ◽  
Detection System ◽  
Sample Selection ◽  
P Wave ◽  
Machine Learning Techniques ◽  
Seismic Events ◽  
Seismic Event Detection ◽  
Events Detection

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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