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

Abstract

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.

Figures (11)

• Figure 1: The main panel shows the Gulf of Guinea and its topographic features derived from a Digital Elevation Model (DEM), together with the mapped active faults and fracture zones linking the Mid-Atlantic Ridge to the interior parts of the oceanic ridge system. The study area in southern Ghana is indicated by a black rectangle and is enlarged in the inset at the bottom right. The geographic location of the entire Gulf of Guinea is highlighted by a red rectangle in the inset globe at the top left. Faults in the Gulf of Guinea and East Africa are compiled from jones2022target. Reverse, normal, and transform faults are after thieblemont2016geoafrica.
• Figure 2: Geological map of Ghana showing the major lithological and structural units across the region (modified from akpan2016crustal). Southern Ghana lies within the southern sector of the West African Craton (WAC) and marks the junction between the Paleoproterozoic Birimian terranes and the Neoproterozoic–Cambrian Dahomeyide Orogen along the eastern margin. The legend highlights the principal formations, including Birimian volcanic belts and sedimentary basins, the Tarkwaian and Sekondian basins, the Dahomeyan and Togo Structural Unit, Voltaian sequences, and younger sedimentary cover deposits. The green line labeled 7 represents a seismic profile acquired in 1970 delteil1974continental, which is used together with borehole log data from well 16.1 to validate the compiled ambient noise sections in this study.
• Figure 3: Top: Workflow for ambient seismic noise reflection series estimation. Continuous seismic records are first preprocessed, and the data are then segmented into one-hour windows. Cross-correlations are computed up to 20-s lags. These functions are subsequently stacked using phase-weighted stacking to generate the daily reflection series. Finally, the results are converted from two-way travel time (TWT) to depth using the velocity model estimated in the bottom workflow. Bottom: Earthquake detection and velocity inversion workflow. P and S phases from continuous waveforms are detected using the EQT and SEQT models. The phases are then associated using the PyOcto method, yielding a set of preliminary events to be used as input for the joint inversion, which simultaneously estimates hypocentral parameters and a parametric 1D velocity model. The optimal velocity model is subsequently employed for TWT to depth conversion and final earthquake relocation of detected events.
• Figure 4: High-frequency reflection responses (3–13 Hz) obtained using SAANA, displayed in two-way travel time (TWT), for the stations MRON, WEIJ, and KUKU, which is optimized for imaging shallower crustal structures. Primary and multiple reflection phases are labeled in the noise-derived sections. Coherent reflection phases appear as laterally continuous horizontal bands, indicating persistent subsurface impedance contrasts retrieved from ambient seismic noise. The enhanced reflectivity confirms the presence of key crustal interfaces beneath the station.
• Figure 5: A similar high-frequency reflection responses (3–13 Hz) obtained using SAANA, displayed in two-way travel time (TWT), for the stations SHAI, KLEF, and AKOS. Coherent reflection phases appear as laterally continuous horizontal bands, indicating persistent subsurface impedance contrasts retrieved from ambient seismic noise. The enhanced reflectivity confirms the presence of key crustal interfaces beneath the station.