Geoelectrical Characterization of Subsurface Structures in Dala Hill, Kano, Nigeria: A Comparative Study with Ground Magnetic Data
Journal Title: Acadlore Transactions on Geosciences - Year 2024, Vol 3, Issue 1
Abstract
A geoelectrical imaging survey, employing resistivity and induced polarization (IP) methodologies, was executed on Dala Hill, Kano, Nigeria, positioned between latitudes 12.008611°N and 12.009722°N, and longitudes 8.505833°E and 8.507222°E. The objective was to assess and compare findings with prior ground magnetic studies to delineate subsurface geological structures. The survey utilized an ABEM Terrameter SAS 1000 for data acquisition along three distinct profiles encompassing the hill and adjacent areas, with electrode separations fixed at 10 meters. Data processing was conducted using RES2DINV software, revealing resistivity profiles that identified three stratified layers with resistivity values ranging from 300Ωm to 6798Ωm for the first layer, 128Ωm to 744Ωm for the second, and 4Ωm to 127Ωm for the third. IP profiles identified zones of varying chargeability, from -3.44 msec to 19.6 msec. Analysis indicated a consistent positive correlation between zones of high resistivity and low chargeability. For instance, a zone along Profile 1 demonstrated high resistivity values (2142Ωm - 6798Ωm) between 60m and 190m, coinciding with a low chargeability zone (0.506 msec to 2.43 msec) observed from 20m to 100m along the profile, equating to depths of 10m to 39.6m. Similar correlations were observed in the subsequent profiles, with significant intersections between high resistivity and low chargeability zones. These areas were interpreted as being rich in iron ore minerals, predominantly magnetite, based on the comparative analysis with standard values of rocks and minerals. The presence of magnetite, known for its high iron content and magnetic properties, underscores the area's potential for steel production. Moreover, the identification of a dyke within the study area corroborates findings from earlier magnetic studies, further validating the geophysical methodology's effectiveness in revealing the shallow subsurface structural settings. This alignment not only substantiates the layered configurations deduced from magnetic studies but also highlights the geoelectrical survey's capability in providing a comprehensive understanding of subsurface geology.
Authors and Affiliations
Abba Y. Usman, Mohammed Saleh, Abubakar G. Shuaibu
Keywords
Related Articles
- EP ID EP749382
- DOI 10.56578/atg030104
- Views 29
- Downloads 0
Comparative Analysis of Trigonometric and Polynomial Models in Meteorological Parameter Prediction for Sub-Saharan West African Stations
Meteorological parameter modeling is imperative for predicting future atmospheric conditions. This study focuses on the Sub-Saharan region of West Africa, a region characterized by its climatic diversity and unique weath...
Seismic Capacity Using Finite Element Analysis: A Case Study of Murum Powerhouse
Malaysia has become more aware of potential seismic hazards after one of the most devastating earthquakes in 2015. It is necessary to make seismic analysis of areas with active fault lines and access the current structur...
Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery
In 2020, the world witnessed an unprecedented event: the outbreak of the COVID-19 pandemic, leading to significantly curtailed human activities. This study sought to elucidate the potential spatial ramifications of this...
Identification of Areas with Significant Flood Risks in Counties along the Danube River in Serbia and Their Risk Assessment
For countries along the Danube River, their sustainable economic and social development needs the optimum water utilization of both the Danube and its tributaries. In the context of climate change, the risks of floods an...
Comparing Artificial Neural Networks with Multiple Linear Regression for Forecasting Heavy Metal Content
This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Ou...