Geosystems and Geoenvironment (May 2023)
Application of deep learning for seismicity analysis in Ghana
Abstract
We present the characterization of regional seismicity in Ghana by processing the Ghana Digital Seismic Network (GHDSN) data set recorded between September 2012 and April 2014, implementing deep learning (DL). Local earthquakes are detected in this dataset using EQTransformer, a DL model with a hierarchical attentive mechanism (HAM) for simultaneous earthquake detection and P- and S-phase picking. A Conservative Strategy (CS) is devised to detect the missing phases and to associate the detected phases to circumvent the false-negative issue of EQTrans-former processing low signal-to-noise ratio (SNR) seismograms. We performed a joint inversion by grid search in 1D velocity model space and simultaneous inversion for the hypocentral parameters, incorporating 559 detected arrival times (292 P and 267 S phases). The results obtained by velocity inversion contain thicknesses of 1, 13, 8, 13, and 10 km, from the surface to a depth of 45 km, with Vp = 5.9, 6.1, 6.3, 6.5, 6.9, and 7.2 km/s, respectively. The updated velocities for the first and last layers are 6% and 26% and the Vp/Vs (=1.70) is 3.03% higher than the previously reported values. A total number of 73 earthquakes with a local magnitude of 2.5 < Ml < 3.9 are located, comprising four main clusters of events, showing a high correlation with the mapped faults zones. The hypocentral depth distribution is mainly in the range of 7-15 km, confined to the upper crust in the region. No specific seismic activity in the eastern branch of Coastal Boundary Fault (CBF) and the continuation of Romanche Fracture Zone (RFZ) in the study period was observed, casting further doubt on the activity of this branch and the hypothesis of stress transfer from RFZ to southern Ghana. The results reinforce the intraplate nature of the tectonic activities in the region. Finally, an updated seismic catalog up to April 2022 is presented for Ghana by incorporating all reported catalogs and combining the newly detected events.
