Citation

Mohammadigheymasi H, Tavakolizadeh N, Matias L, Mousavi SM, Moradichaloshtori Y, Mousavirad SJ, Fernandes R. Data Brief 2023; 47: e108969.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.dib.2023.108969

PMID

36879614

PMCID

PMC9984768

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].

Language: en

Keywords

Deep learning; Earthquake waveforms; Live Matlab figures; Seismic catalog