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Groundwater potential and aquifer protective capacity of the overburden unit was evaluated in part of Iju, Akure North, Ondo State using integrated geophysical methods involving Very Low Frequency Electromagnetic (VLF-EM) profiling and Vertical Electrical Sounding (VES). Four major traverses were established of varying length extents. The VLF-EM measurements were taken along the four major profiles of 10 m station interval.Forty two (42) Vertical Electrical Soundings were also conducted with half electrode spacing varying between 1 and 100 m and interpretation was done using the partial curve matching techniques and computer aided iteration. Five subsurface geological units were identified from geoelectric sections, consisting of the top soil, lateritic, weathered, partly weathered and fresh basement layers consecutively. For the first layer, resistivityranges between 23 and 323 Ωm with values of thickness ranging between 0.5 and 2.2 m. The resistivity and thickness of the second (lateritic) layer range from 132 to 430 Ωm and 1.6 to 4 m respectively. The resistivity of the weathered layer ranges from 4 to 94 Ωm and variable thickness between 10 and 24.4 m. The fourth layer has a resistivity value range of 65 to 120 Ωm and thicknesses between 20 and 30 m. The basement bedrock (fifth layer) has resistivity values between 770 and 820 Ωm. The depth to bedrock ranges from 1.8 to 31 m. The geophysical data and the basement aquifer delineated were then used to evaluate the hydrogeological setting and aquifer protective capacity of the study area. The observation from the results shows that close to 70 % of the study area falls within the zones of low groundwater potential, 25 % falls within medium potential zones while only 5 % make up the high potential zones. 75 % of the study area constituted the weak to poor protective capacity zones.

Forward modeling of direct current (DC) resistivity is very important for the inversion of the resistivity data to obtain the true resistivity of the subsurface. In this study, we demonstrated finite-element forward modeling of DC resistivity method with point electric source using COMSOL Multiphysics. We employed the AC/DC module in COMSOL which often provides comparatively easy implementation of models and permits exterior boundaries to be placed at infinity, a boundary condition often experienced in most geophysical problems. The validity and effectiveness of the results of numerical simulation using COMSOL Multiphysics were evaluated by comparing the output of the numerical simulations with the calculated analytic solutions. The result reveals that the numerical simulation is in agreement with the analytic solution. This study shows that COMSOL Multiphysics can be used to simulate the distribution of electrical potentials of point source in 3D space in real life and the information from this study can be used for further studies, such as DC resistivity inversions. Keywords Forward modeling • COMSOL • resistivity • Simulation List of symbols ρ a Apparent resistivity ∆V Difference in electrical potential σ Electrical conductivity n Normal to the surface σ 2 Anomaly conductivity V Electrical potential E Error analysis G Geometrical factor I Current injected I o Current intensity σ 1 Electric background conductivity V ana Analytical potential r Radius x, y and z Points

Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R 2 ) of 0.80. The validation of our model between the actual and predicted dry densities shows R 2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.