19–23 Jun 2023
Hofburg Palace & Online
Europe/Vienna timezone

Automatic Earthquake Detection Capabilities of PhaseNet and EQTransformer Using the 3 April 2017 Botswana M6.5 Earthquake and its Aftershocks.

22 Jun 2023, 09:00


Board: 38
E-poster T3.5 Analysis of Seismic, Hydroacoustic and Infrasound Monitoring Data Lightning talks: P3.5, P5.1


Joseph Maritinkole (Botswana Geocience Institute)


PhaseNet and EQTransformer are two deep learning methods most commonly used for automatic earthquake detection and phase-picking. This work presents the performance of these algorithms using two weeks of data retrieved from the Botswana Seismological Network stations. Additional data used is obtained from the International Monitoring System network within a distance of 250 km from Botswana (e.g. Lobatse and Boshof). The results of PhaseNet and EQTransformer are recorded and compared with the results of the STA-LTA method. The accuracy, precision, recall, and F1 score are calculated for each method. Preliminary results show that the deep learning algorithms outperform conventional phase-picking methods in noisy data and where events overlap. Events that may be ambiguous for traditional methods are often clear outliers within the DNN model domain, as observed in this study.

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Joseph Maritinkole is a Data Analyst and Station Operator at the Botswana Geoscience Institute. He completed his undergraduate degree in Geophysics at the University of Botswana and is now a full-time Masters student at the University of Witwatersrand.

E-mail [email protected]
Oral preference format in-person

Primary authors

Joseph Maritinkole (Botswana Geocience Institute) Mr Onkgopotse Ntibinyane (CTBTO Preparatory Commission)

Presentation materials