INTEGRATING DIGITAL TRANSFORMATION IN GEOSPATIAL COMPUTATIONAL IDENTIFICATION OF Pb-Zn MINERALIZATION ZONES IN ABAKALIKI URBAN USING AEROMAGNETIC AND ELECTRICAL RESISTIVITY TOMOGRAPHY DATA
DOI:
https://doi.org/10.58885/ijees.v5i1.17.iiKeywords:
Digital geoscience, Digital tools, Digital transformation.Abstract
An integrated geophysical investigation employing aeromagnetic data, Electrical Resistivity Tomography (ERT), and Induced Polarization (IP) methods was conducted in Abakaliki and its environs to delineate zones of Pb-Zn mineralization. The study area, located within latitudes 6°04′11″N – 6°02′01″N and longitudes 8°04′11″E – 8°01′21″E, spans approximately 16 km² in the Lower Benue Trough. High-resolution aeromagnetic data were acquired across the region, while ERT and IP surveys were carried out along coincident profiles at selected sites to allow for integrated interpretation and mutual validation.
To enhance spatial analysis and improve interpretation accuracy, Geographic Information Systems (GIS) and remote sensing tools were utilized for georeferencing, structural lineament mapping, and spatial data integration. The aeromagnetic survey provided regional insights into magnetic anomalies and structural discontinuities, while the ERT and IP techniques offered detailed subsurface resistivity and chargeability distributions. ERT profiles were aligned with IP survey lines using GPS and mobile data collection platforms, ensuring data consistency and real-time field updates.
Visual inspection and 2D/3D visualization of the Total Magnetic Intensity (TMI) map—processed with geophysical modeling software—revealed magnetic intensity values ranging from 21.1 nT to 95.3 nT, indicative of varying lithologies and potential mineralized zones. Lineament analysis using automated edge-detection and image enhancement algorithms further revealed a network of faults, fractures, and veins likely to host mineralization.
Resistivity values within the zones of interest ranged from 4.00 Ωm to 2710 Ωm, while chargeability values varied between 39.3 mV/V and 472 mV/V, reflecting significant lithological contrasts and ore-bearing structures at depths between 100 m and 120 m. The integration of magnetic and geo-electrical data—supplemented with IT-based tools such as GIS platforms, machine learning-assisted anomaly detection, and cloud-enabled data management—proved effective in delineating Pb-Zn mineralization zones, offering valuable insights for future mineral exploration activities in the area.
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