A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning

Hamzeh Mohammadigheymasi; Nasrin Tavakolizadeh; Luís Matias; S Mostafa Mousavi; Yahya Moradichaloshtori; Seyed Jalaleddin Mousavirad; Rui Fernandes

doi:10.1016/j.dib.2023.108969

A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning

Data Brief. 2023 Feb 11:47:108969. doi: 10.1016/j.dib.2023.108969. eCollection 2023 Apr.

Authors

Hamzeh Mohammadigheymasi¹, Nasrin Tavakolizadeh², Luís Matias³, S Mostafa Mousavi⁴, Yahya Moradichaloshtori⁵, Seyed Jalaleddin Mousavirad², Rui Fernandes¹

Affiliations

¹ Instituto Dom Luiz (IDL), Universidade da Beira Interior, Covilha, 6201-001, Portugal.
² Departamento de Informatica, Universidade da Beira Interior, Covilha, 6201-001, Portugal.
³ Department of Geophysics, Stanford University, Stanford, CA 94305-2215, United States.
⁴ Instituto Dom Luiz, Faculdade de Ciencias, Universidade de Lisboa, Lisboa, 1749-016, Portugal.
⁵ Institute of Geophysics, University of Tehran, Tehran, 14359-44411, Iran.

Abstract

The Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquakes consisting of supporting data, waveforms (including P and S arrival phases), and earthquake bulletin are presented. The bulletin includes the 559 arrival times (292 P and 267 S phases) and waveforms of the 73 local earthquakes in SEISAN format. The supporting data encompasses the preliminary crustal velocity models obtained from the joint inversion analysis of the detected hypocentral parameters. These parameters comprised of a 6- layer model of the crustal velocity (Vp and Vp/Vs ratio), incident time sequence, and statistical analysis of the detected earthquakes and hypocentral parameters analyzed and relocated by the updated crustal velocity and graphic representation of them a 3D live figure enlighting the seismogenic depth of the region. This dataset has a unique appeal for earth science specialists to analyze and reprocess the detected waveforms and characterize the seismogenic sources and active faults in Ghana. The metadata and waveforms have been deposited at the Mendeley Data repository [1].

Keywords: Deep learning; Earthquake waveforms; Live Matlab figures; Seismic catalog.