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Cyril D. Boateng

Publications and source records attributed to Cyril D. Boateng.

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Crustal Structure Imaging of Ghana from Single-Station Ambient Noise Autocorrelations and Earthquake Arrival Time Inversion

The crustal architecture of southern Ghana remains inadequately resolved despite its tectonic significance and resource potential. Existing geological and geophysical studies provide only broad constraints on crustal composition, lacking the resolution to accurately define sediment-basement interfaces or intra-crustal stratigraphy. To address these limitations, we employ single-station ambient noise autocorrelation (SSANA) on continuous waveform data from the Ghana Digital Seismic Network (GHDSN). We extract P-wave reflectivity responses using a processing sequence that involves data pre-processing, Phase Cross-Correlation (PCC) for robust noise correlation, and phase-weighted stacking (PWS) of the derived autocorrelograms. This procedure yields a two-way travel-time (TWT) function representing the zero-offset P-wave reflection response beneath each station, enabling high-resolution imaging of the stratified crustal column. To facilitate depth conversion, we develop an enhanced one-dimensional crustal velocity model for the region. Using a compiled dataset of local earthquake P- and S-wave arrival times from the GHDSN and an additional station in Cote d'Ivoire, we perform a joint inversion via a grid-search algorithm to derive a regional 1D velocity structure. Our results provide new constraints on the depth and configuration of the Paleozoic basement beneath the Voltaian Basin, demonstrating the efficacy of ambient noise autocorrelation for crustal imaging in sparsely instrumented regions. We also present an updated seismicity catalog, relocated using the new velocity model, and analyze the spatial clustering of seismicity in southern Ghana. This study highlights the utility of passive seismic methods for elucidating crustal structure and evaluating resources in intraplate West Africa and analogous Precambrian terrains.

physics.geo-ph

Building Capacity for Artificial Intelligence in Africa: A Cross-Country Survey of Challenges and Governance Pathways

Artificial intelligence (AI) is transforming education and the workforce, but access to AI learning opportunities in Africa remains uneven. With rapid demographic shifts and growing labour market pressures, AI has become a strategic development priority, making the demand for relevant skills more urgent. This study investigates how universities and industries engage in shaping AI education and workforce preparation, drawing on survey responses from five African countries (Ghana, Namibia, Rwanda, Kenya and Zambia). The findings show broad recognition of AI importance but limited evidence of consistent engagement, practical training, or equitable access to resources. Most respondents who rated the AI component of their curriculum as very relevant reported being well prepared for jobs, but financial barriers, poor infrastructure, and weak communication limit participation, especially among students and underrepresented groups. Respondents highlighted internships, industry partnerships, and targeted support mechanisms as critical enablers, alongside the need for inclusive governance frameworks. The results showed both the growing awareness of AI's potential and the structural gaps that hinder its translation into workforce capacity. Strengthening university-industry collaboration and addressing barriers of access, funding, and policy are central to ensuring that AI contributes to equitable and sustainable development across the continent.

cs.CY