Enhanced insight into Ghana's Seismicity through a Refined Crustal Velocity Model and Earthquake Focal Mechanisms

This abstract presents the findings of a comprehensive seismicity analysis conducted in Ghana using the advanced Deep-Scan workflow [1]. The study compiled data from 461 earthquakes, revealing new insights into active seismogenic sources in this relatively under-explored region. A more accurate crustal velocity model is computed, and the detailed Green functions for focal mechanisms of detected events are estimated. Data integration from a single Ivory Coast station improved the azimuthal coverage of hypocenters, facilitating more precise velocity model estimations for deeper layers. The study utilized 5213 accurately picked arrival phases from 284 earthquakes, applying joint inversion techniques to optimize crustal velocity and hypocentral parameters. This resulted in a detailed 6-layer model, with P-wave velocity values ranging from 5.6 to 6.3 km/s and a high-velocity layer at 7.8 km/s, maintaining a consistent Vp/Vs ratio of 1.70. Analysis of the earthquakes revealed six compact clusters, predominantly aligned with the Akwapim fault zone and linked to the active section of the Romanche fracture zone in Axim. The distribution of hypocentral depths consistently falls within the range of 9-18 km, confined to the upper crust of the region. Utilizing the revised velocity model notably enhanced the identification of the seismogenic zone in southern Ghana. Moreover, it facilitated the generation of detailed Green functions for the next step for moment tensor (MT) inversion of small-magnitude earthquakes, employing a bootstrap-based probabilistic inversion schema. The results enhance our understanding of Ghana’s seismicity and represent key points for hazard and risk assessment in the region. This research is supported by Funda¸c˜ao para a Ciˆencia e Tecnologia (UIDB/00645/2020 and https://doi.org/10.54499/UIDB/00645/2020, and UIDB/50008/2020).References[1] H. Mohammadigheymasi et al., "IPIML: A Deep-Scan Earthquake Detection and Location Workflow Integrating Pair-Input Deep Learning Model and Migration Location Method," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-9, 2023, Art no. 5914109, doi: 10.1109/TGRS.2023.3293914.