Papers by Adedibu Sunny Akingboye
arXiv Preprint, 2025
Subsurface lithological heterogeneity presents challenges for traditional geophysical methods, pa... more Subsurface lithological heterogeneity presents challenges for traditional geophysical methods, particularly in resolving nonlinear electrical resistivity and induced polarization (IP) relationships. This study introduces a data-driven machine learning and deep learning (ML/DL) framework for predicting 2D IP chargeability models from resistivity, depth, and station distance, reducing reliance on field IP surveys. The framework integrates ensemble regressors with a one-dimensional convolutional neural network (1D CNN) enhanced by global average pooling. Among the tested models, CatBoost achieved the highest prediction accuracy (R² = 0.942 training, 0.945 testing), closely followed by random forest, while the stacked ML/DL ensemble further improved performance, particularly for complex resistivity-IP behaviors. Overall accuracy ranged from R² = 0.882 to 0.947 with RMSE < 0.04. Integration with k-means clustering enhanced lithological discrimination, effectively delineating sandy silt, silty sand, and weathered granite influenced by saturation, clay content, and fracturing. This scalable approach provides a rapid solution for subsurface modeling in exploration, geotechnical, and environmental applications.
Journal of Applied Geophysics, 2025
The integration of machine learning (ML) in geophysical investigations has become pivotal for res... more The integration of machine learning (ML) in geophysical investigations has become pivotal for resolving nearsurface complexities, particularly in terrains with complex lithological heterogeneity. This study aims to develop and validate a novel ML-driven framework for jointly modeling seismic P-wave velocity (Vp) and resistivity relationships to improve subsurface characterization in tropical granitic terrains. Using collocated data from seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) surveys across the South Penang Pluton, Malaysia, the method addresses traditional integration challenges by applying contour-based mesh interpolation to generate a densely aligned dataset-reducing manual bias, increasing data volume fivefold, and enhancing nonlinear predictive performance. Stratified binning ensured balanced lithological representation across training, validation, and test sets. Six ML models-simple linear regression (SLR), support vector machine (SVM), decision tree (DT), random forest (RF), gradient boost (GB), and artificial neural network (ANN)-were trained to predict Vp from resistivity. All models showed high predictive reliability (R 2 = 0.819-0.984, RMSE = 0.029-0.09, F1 = 0.878-0.910), outperforming previous regression-based approaches in the study area. ANN achieved the best performance, followed by SVM, both maintaining stability across all partitions. The framework integrates k-means clustering with internal validation and ML-predicted Vp-resistivity profiles to automate lithological classification into four distinct subsurface units. This integrated approach improves geophysical boundary resolution, preserves structural fidelity in zones of rapid geological transition, and enables scalable deployment in tropical weathered terrains where SRT coverage is often constrained. Ultimately, it advances geophysical site modeling from deterministic to data-driven prediction, offering cost-effective, transferable solutions for geotechnical, hydrogeological, and hazard-related applications in geologically complex settings.
Scientific African, 2025
The Okpella region in southwestern Nigeria’s Precambrian Basement Complex holds significant gold ... more The Okpella region in southwestern Nigeria’s Precambrian Basement Complex holds significant gold (Au) mineralization potential but remains underexplored due to its complex lithostructural evolution. The terrain comprises diverse metasedimentary and granitoid rocks, including granite gneiss (GGN), garnet-biotite schist (GBS), calc-silicate gneiss (CGN), quartzite (Qs), banded iron formation (BIF), charnockite (Ch), granite (G), and minor pegmatite and basic dykes. This study presents a novel integration of geological, geochemical, and high-resolution aeromagnetic and radiometric data—including magnetic derivatives, optimized radiometric indices, potassium deviation analysis, Au prospectivity modeling, and magnetic depth-to-source estimations—to delineate lithological units, characterize hydrothermal alteration, identify Au-bearing zones, and estimate the depths of mineralized structures. The Ch-dominated southern domain shows low to moderate magneto-radiometric responses, while the central to northern zones (GGN, GBS, CGN, BIF, G) exhibit higher signals—with magnetic intensities reaching up to 105.6 nT—interspersed with localized lows. Enhanced magnetic derivatives, optimized radiometric indices, and Au prospectivity mapping refine auriferous targeting by highlighting structurally reactivated contacts, particularly GBS–G, CGN–G, GGN–CGN–G, and GBS–CGN–G, dominated by NE–SW and NW–SE structures. Three structurally controlled hydrothermal belts with exploitable depths from <37 to 125 m are delineated, while deeper magnetic models resolve reactivated crustal blocks down to ~3.5 km. The resulting framework provides a transferable exploration strategy, integrating geophysical, geochemical, and geological insights to support sustainable mineral development and national resource-based economic growth.
Physics and Chemistry of the Earth, Parts A/B/C, 2025
Accurate subsurface assessment is critical for ensuring the integrity and longevity of engineered... more Accurate subsurface assessment is critical for ensuring the integrity and longevity of engineered structures, especially in weathered terrains characterized by highly variable soil and rock properties. This challenge is particularly evident in the complex granitic terrain of Ipoh, Perak, Malaysia, where differential weathering and fracturing introduce substantial uncertainty into foundation design. Despite increasing demands for resilient infrastructure, conventional site investigation methods often fall short in capturing the spatial and lithological heterogeneity required for informed decision-making. This study addresses these limitations by integrating electrical resistivity tomography (ERT), induced polarization (IP), standard penetration testing (SPT), and supervised machine learning (ML) algorithms to enhance subsurface characterization and predictive modeling of chargeability. The combined ERT–IP inversion results, supported by borehole logs and SPT data, delineated four distinct lithological units across the study area. Four ML algorithms—SLR, KNN, SVM, and CatBoost—were trained to model relationships between geophysical and geotechnical parameters, with all achieving strong performance (training R² > 0.90; error metrics <10%). Among all the evaluated models, CatBoost exhibited the strongest overall performance, achieving high predictive accuracy and maintaining consistent generalization across validation and test sets, although with mild overfitting observed. Among all the evaluated models, CatBoost exhibited the strongest overall performance, achieving high predictive accuracy and maintaining consistent generalization across validation and test sets, although with mild overfitting observed. From a geotechnical perspective, the topsoil/residual and highly weathered/fractured units were deemed unsuitable for heavy structural loads due to poor consolidation and water retention. In contrast, the relatively weathered bedrock unit, defined by resistivity values of 800 to >1000 Ωm and chargeability >18 msec, was identified as a suitable foundation material after appropriate soil and rock modifications. This research introduces a novel, noninvasive geophysical–geotechnical framework enhanced by ML, offering a cost-effective and scalable approach for subsurface evaluation. The methodology significantly reduces uncertainty in engineering assessments and supports safer, data-driven construction decisions, with potential applicability to other complex weathered terrains, subject to site-specific calibration.
Earth Systems and Environment, 2025
Reliable characterization of subsurface conditions, such as depth to bedrock, lithological variab... more Reliable characterization of subsurface conditions, such as depth to bedrock, lithological variability, and the presence of fractures or faults, is critical for designing safe and durable foundations. However, geophysical methods remain underutilized in geotechnical design due to limited interdisciplinary collaboration, a general lack of awareness among engineers, and the absence of standardized integration frameworks. Hence, this study addresses these gaps by integrating geophysical and geotechnical data with machine learning (ML) techniques to enhance subsurface integrity modeling for foundation designs. Conducted in a granitic terrain in Minden, Pulau Penang, Malaysia, the research utilized electrical resistivity tomography, induced polarization, seismic refraction tomography, and standard penetration test (SPT-N) data acquired along two transects. The findings revealed critical subsurface anomalies-such as fractures, weathered zones, and lithological transitions-that pose potential risks to structural stability. Three major lithological layers were delineated: saturated clayey/silty topsoil extending to the residual profile; a transitional zone comprising weathered (clayey/silty) and fractured rock; and partially weathered to hard bedrock. ML algorithms applied included K-nearest neighbors (R² = 0.930 training, 0.905 testing), simple linear regression (R² = 0.898 training, 0.905 testing), principal component analysis, and K-means clustering (Silhouette score = 0.6688, residual sum of squares of 30.87 at K m = 3), all demonstrating strong predictive performance. The integrated framework proved effective for delineating subsurface lithology and assessing geomechanical properties, offering a scalable, data-driven strategy for cost-effective, high-resolution foundation design. This approach supports improved engineering decisions, reduces reliance on extensive invasive testing, and highlights the transformative potential of ML-assisted geophysical-geotechnical investigations in infrastructure development.
Physics and Chemistry of the Earth, Parts A/B/C, 2025
In crystalline basement terrains, understanding near-surface crustal architecture is vital for as... more In crystalline basement terrains, understanding near-surface crustal architecture is vital for assessing geoengineering integrity and mitigating landslide risks. This study focuses on the granitic terrain of Penang Island, Malaysia, where rapid urbanization and landslides could threaten infrastructure stability. Integrating multiphysics with statistically optimized borehole-based rock quality designation (RQD) modeling provides the first geophysical-geotechnical-statistical framework to evaluate lithological conditions and landslide triggers in granitic terrains. The innovative methodology develops lithology-based geoengineering integrity models to assess soil-rock profiles at steep and low-lying sections across four sites: Sungai Ara (east; Site 1), Batu Maung (south; Site 2), Jelutong (north; Site 3), and Balik Pulau (west; Site 4). The findings identify varied subsurface characteristics using resistivity and seismic P-wave velocity tomographic models, correlated with borehole logs and RQD data. The subsurface is categorized into residual soils, highly to moderately weathered/fractured granite, and hard/fresh granitic bedrock. The study framework revealed contrasting lithologic differentiation between southern Penang Island and the northern section, including the western and eastern parts, likely due to differences in granitic feldspar magma mineralogy and susceptibility to weathering. Sites 1, 3, and 4 are dominated by lower resistivity and seismic velocity values-indicating weaker, looser, and deeper weathered materials-whereas Site 2 is characterized by more compact, sandy-rich residual soils and hard/fresh bedrock with deeper fractures. Weathered and fractured zones amplify water-rock interactions, increasing pore pressure and landslide susceptibility in the area. Contractive clay/silt (<200 Ωm) and water-escape structures within creeping residual soils are identified as primary landslide triggers, particularly in unstable, steep sections. For infrastructure design integrity, reinforced piling foundations reaching fresh granitic bedrock (RQD Units III and IV) are recommended. These measures are crucial in Penang's rapidly urbanizing terrain with numerous tall buildings. While site-specific, the study introduces a globally adaptable framework for cost-effective, large-scale geoengineering assessments in similar geological contexts, enhancing risk mitigation and infrastructure design strategies.
Discover Geoscience, 2025
This comprehensive review examines electrical and seismic refraction methods, emphasizing their a... more This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface-subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for geological and geohazard assessments. The review also explores the induced polarization (IP) and self-potential (SP) methods as complementary approaches. Despite their proven utility, electrical and seismic refraction approaches face limitations arising from lithological heterogeneities, complex geological settings, and inherent data uncertainties. These challenges highlight the need for multidisciplinary strategies, including methodological innovations and integrative data frameworks. Recently, machine learning (ML) techniques have been increasingly applied to these geophysical methods, particularly joint ERT and SRT analyses, optimizing nonlinear inversion processes and improving the interpretation of complex subsurface conditions. The case studies presented in this review evaluate how supervised and unsupervised ML techniques enhance ERT and SRT by improving data interpretation, refining inversion accuracy, automating lithological differentiation, and predicting seismic velocity from resistivity data. The findings emphasize the growing significance of integrating traditional geophysical methods with data-driven approaches to improve subsurface modeling. Continued innovations in ERT and SRT methodologies, along with emerging computational tools and ML applications, will further enhance their effectiveness in geological, hydrological, environmental, and hazard assessments.
Earth Systems and Environment, 2025
The Okpella region, located in the eastern Igarra Schist Belt of southwestern Nigeria, is a Neopr... more The Okpella region, located in the eastern Igarra Schist Belt of southwestern Nigeria, is a Neoproterozoic metasedimentary-granitoid terrain known for its gold (Au) mineralization potential. Despite this, the complex interactions between litho-nuclide dynamics, hydrothermal alteration, and associated radiogenic and potentially toxic element (PTE) hazards have not been fully explored in the region. This study, the first to apply magnetic-assisted radiometric analysis combined with geochemical and PTE assessments in Nigerian geology, aims to address this gap. The results reveal significant geochemical heterogeneity, with elevated concentrations of K (up to 5.04%), eTh (up to 51.12 ppm), and eU (up to 13.42 ppm) predominantly found in garnet-biotite schist, calc-silicate gneiss, granite, and their contacts. These concentrations are attributed to hydrothermal fluid activity and fluid/melt-rock interactions. Conversely, charnockite in the southern region shows depleted radionuclide levels, reflecting limited alteration and the presence of K-poor minerals. Advanced mapping techniques have identified hydrothermal alteration zones and Au mineralization belts aligned with NE-SW and NW-SE structural trends. Magnetic depth-structure models suggest basement depths of ~ 0.3 to 3.5 km, with fractures acting as conduits for hydrothermal fluids, facilitating radionuclide redistribution near the surface. The study also highlights elevated radiogenic hazard indices, particularly in the central and northern regions, surpassing crustal limits. The PTE (mg/kg) trend in sediments (Hg > Cu > As > Pb > Zn > Co > Cr > Ni) indicates environmental and health risks, particularly from Hg contamination linked to artisanal Au mining. These findings underscore the need for controlled gold extraction, Hg-free processing, and continuous environmental monitoring. This study provides a scalable framework for balancing resource extraction with environmental sustainability, offering insights into best practices for mineralized terrains globally.
Scientific African, 2025
Groundwater suitability for irrigation is a crucial component of sustainable agriculture, particu... more Groundwater suitability for irrigation is a crucial component of sustainable agriculture, particularly in regions like Obosi, southeastern Nigeria, where freshwater resources are limited and agricultural demands are high. Despite growing interest in improving prediction models for groundwater suitability, there remains a gap in employing advanced machine learning techniques alongside hydrogeochemical analysis for precise and reliable assessments. This study aims to address this gap by evaluating the performance of six novel machine learning models-Artificial Neural Networks (ANNs), Random Forest (RF), Support Vector Machine (SVM), CatBoost (CatB), AdaBoost (AB), and Gradient Boosting (GB)-in predicting irrigation suitability using 42 groundwater samples. The hydrogeochemical approach in this study employs Piper, Durov, and Schöller plots to analyze groundwater composition and geochemical processes. The findings reveal a sodium chloride (NaCl)-dominated water type, influenced by dissolution, mixing, and reverse ion exchange, with implications for water quality and management. The physicochemical analysis showed pH values ranging from 4.49 to 6.29, electrical conductivity (EC) between 8.16 and 101.7 µS/cm, and chloride concentrations from 8.43 to 32.65 mg/L. All measured parameters fell within permissible limits. However, irrigation indices such as sodium absorption ratio (SAR), sodium percentage (Na%), Kelly's ratio (KR), soluble sodium percentage (SSP), and potential salinity (PS) indicated the groundwater was unsuitable for irrigation. Spatial analysis showed distinct regional patterns, with SAR, Na%, and KR aligning NE-SW, while PS followed a S-N direction. Hydrogeochemical trends revealed Cl⁻ dominance in 71% of samples, with evidence of dissolution, ion exchange, and mixing processes. Principal component analysis highlighted complex inter-parameter relationships, with pH and EC explaining 14.94% of variance. ANN and RF consistently outperformed other models across multiple metrics, which include RMSE, MAPE, R^2 , adjusted R^2 , Akaike Information Criterion (AIC), and Bayesian Infor-⁎ Corresponding author.
Journal of African Earth Sciences, 2025
Migmatitic metapelites in the Ikare-Akoko area of southwestern Nigeria offer critical insights in... more Migmatitic metapelites in the Ikare-Akoko area of southwestern Nigeria offer critical insights into the metamorphic and retrograde equilibration of the Nigerian basement complex. While previous studies have detailed its primary mineralogy, the metamorphic evolution and particularly the retrograde equilibration in these rocks remain inadequately explored. This study addresses these gaps through petrologic analysis of seven samples to assess pressure-temperature (P-T) conditions related to post-peak metamorphic equilibration. Field observations indicate that migmatitic metapelites occur as lenses within grey gneiss, characterized by porphyroblastic textures and a mineral assemblage of biotite, quartz, garnet, alkali feldspar, plagioclase, hornblende, sillimanite, cordierite, and orthopyroxene, with accessory minerals including ilmenite, apatite, and magnetite. Garnets are frequently rimmed by cordierite, suggesting retrogressive metamorphic events. The breakdown of garnet in the presence of biotite to form cordierite, alongside the decomposition of pyroxene with plagioclase and quartz to produce hornblende, indicates rehydration during the transition from granulite to amphibolite facies, likely driven by tectonic uplift and subsequent decompression. Geochemical analysis reveals that the migmatitic metapelites are silica-poor (SiO₂ < 60 wt%) and enriched in rare earth elements (REEs), exhibiting significant light REE enrichment and heavy REE depletion, with a negative europium anomaly indicative of a Ca-poor sedimentary precursor. Garnet-biotite thermometry suggests peak metamorphic conditions of approximately 650 °C at 5 kbar, with retrograde reactions underscoring the complex P-T changes during uplift. However, thermodynamic modeling on two samples recorded temperatures of 750 oC at 5 kbar. This research allows progress in understanding the regional tectonometamorphic evolution and clarifies retrograde processes in high-grade metamorphic terrains. This offers a framework for similar terrains in southwestern Nigeria and comparable metamorphic contexts worldwide.
Earth Systems and Environment, 2025
Recently, significant progress has been made in establishing relationships between geophysical an... more Recently, significant progress has been made in establishing relationships between geophysical and geotechnical datasets to evaluate subsurface geological characteristics, especially to address engineering infrastructure failures. This research, therefore, explores the efficiency of machine learning (ML) combined with descriptive statistics in optimizing geophysical and geotechnical datasets from electrical resistivity tomography (ERT) and standard penetration tests (SPT-N) for subsurface lithological characterization of the Kabota-Tawau area in Sabah, Malaysia. As the first report of such analysis in this area, the study aims to address infrastructure design challenges posed by the increasing needs of the growing population. The derived ERT models, coupled with k-means clustering and regression results for modeled resistivity-SPT-N data at varying depths, clearly depict the main lithologies of the study area. The lithological units include the topsoil, weathered soil units, highly weathered/fractured units, and relatively weathered/fractured units. The use of regression analysis enabled the identification of new statistical correlations between resistivity and SPT-N in the area. These correlations accurately predicted outcomes with a 77% accuracy rate, supported by highly efficient performance values from the descriptive statistics. The predictions were based on resistivity values specific to different lithologies, which were determined to be < 150 Ωm. This progress is useful for forecasting SPT-N and reducing survey expenses, especially in extensive regions earmarked for infrastructure projects. Despite the study area's generally low resistivities, associated with low load-bearing capacity, the study suggests modifications for the placement of medium to heavy infrastructure weights in highly to relatively weathered units. However, piling to the fresh bedrock is advised for high-rise buildings of super weights. Based on the research findings, the established ML-assisted methodological approach can be applied to other terrains with similar geology, particularly for early-stage subsurface characterization.
Environmental Earth Sciences, 2024
Potentially toxic elements from heavy metal pollution in soils and water pose significant health ... more Potentially toxic elements from heavy metal pollution in soils and water pose significant health risks to humans and ecosystems. This study aims to assess heavy metal pollution in active dumpsite soils in northern Ondo State, Nigeria, and evaluate associated hazards across different Nigerian geological environments using multi-pollution indices and statistical optimization. Twenty-seven soil samples from three major active dumpsites around Owo and Ikare-Akoko were collected from nine profiles at different depths. These samples were analyzed for concentrations of various heavy metals including Na, K, Ca, Mg, Cr, Fe, Pb, Zn, Ni, and Mn. The concentrations of earth and heavy metals were considerably low compared to other parts of Nigeria and Africa. Pairwise correlations among heavy metal concentrations in the three dumpsite soils revealed both strong and weak correlations. Only Fe–Zn showed a strong correlation. Interestingly, no common correlation was found among the three geological environments, indicating that the concentrations might not be impacted by the varying geological conditions. In Ondo State’s dumpsite soils, contamination factor (CF) ratings indicate moderate to very high contamination, while pollution load index (PLI) values suggest very high pollution. Index of geo-accumulation (Igeo) and ecological risk factor (ERF) values reflect moderately high to very high pollution and low ecological risk, respectively. The mean Igeo values follow this order: Cr < Ni < Mn < Zn < Pb < Fe (Tertiary environments); Cr < Ni < Mn < Pb < Zn < Fe (Cretaceous environments); and Ni < Cr < Zn < Mn < Pb < Fe (Basement environments). For mean ERF values, the order is Cr<Zn<Ni<Mn<Pb<Fe in Tertiary, Cr<Zn<Mn<Ni<Pb<Fe in Cretaceous, and Zn<Cr<Ni<Mn<Pb<Fe in Basement environments. The CF sequence is Cr<Ni<Zn<Mn<Pb<Fe in Tertiary, Cr<Zn<Ni<Mn<Pb<Fe in Cretaceous, and Cr<Ni<Zn<Pb<Mn<Fe in Basement environments. All samples exhibit extremely high contamination, with basement areas dominated by notably high Fe and other heavy metal concentrations, while the Tertiary environments show the lowest concentrations. Based on the research findings, regular monitoring and remediation efforts are essential in other areas to mitigate imminent pollution and risks.
Modeling Earth Systems and Environment, 2024
Surface-subsurface soil-rock modeling is crucial for infrastructure design and borehole groundwat... more Surface-subsurface soil-rock modeling is crucial for infrastructure design and borehole groundwater yield optimization, especially in terrains like Penang Island, Malaysia, prone to soil and slope instabilities exacerbated by heavy rainfall. With the increasing demand for potable water due to population growth and tourism, this study provides vital insights into sustainable groundwater management and infrastructure development. Optimized geophysical-geotechnical methods, including regression modeling, were employed to integrate seismic P-wave velocity (Vp) and resistivity models with borehole lithologic logs, revealing distinct soil-rock characteristics and deep-weathered/fractured zones. The study area's eastern to northern sections exhibit thick, saturated, and loose silty to sandy bodies, contrasting with sandy compositions and penetrative fractures in the southern part. Good correlations between rock quality designation (RQD) and standard penetration tests (SPT N-values) were observed, with intra-bedrock weathered/fractured unit depths varying between 12 and > 35 m. Suitable foundation sites were identified at sections with high RQD values (> 90% at Site 1) and N-values (> 50 at Sites 1 and 3). However, pile foundations were recommended due to varied weak and water-filled zones. It is important to investigate deep sections and the site-specific nature of the uncovered areas, necessitating validation in terrains with comparable geology. Nevertheless, the established lithology-based empirical relationships offer significant benefits for geophysical-geotechnical studies, reducing associated costs across large areas.
Journal of African Earth Sciences, 2024
Environmental contamination is a complex issue that significantly impacts human health and ecolog... more Environmental contamination is a complex issue that significantly impacts human health and ecological systems, especially in developing countries like Nigeria. This study explores the interplay between surface-subsurface soil-rock resistivity and subsurface geotechnical properties, along with multiple soil-water pollution indices at and around the Old Owo-Ikare road (OOIR) dumpsite in southwestern Nigeria. Water samples from five wells and one stream were collected and analyzed, alongside twelve soil samples from four trial pits at varied depths for geoenvironmental assessment. Analyzed hydrogeochemical parameters include Na, Ca, Mg, K, Cr, Pb, Zn, Ni, Mn, Fe, and cations (NO, SO4, Cl) in the water samples. Nine dipole-dipole electrical resistivity tomography (ERT) profiles and six Schlumberger vertical electrical sounding (VES) points were used to determine surface–subsurface resistivity distribution. Water analysis results showed elevated levels of K, Ni, Cr, and Pb concentrations. Geotechnical tests indicated that most dumpsite soil meets good landfill material criteria with low permeability ( to ). ERT and VES inverted models revealed four lithologic layers with two main curve types (HA and QA). Elevated heavy metal levels in the dumpsite soil and inverted resistivity models suggested leachate contamination in the topsoil and weathered bedrock units. Despite relatively lower heavy metal concentrations compared to other Nigerian and Ghanaian dumpsites, multi-pollution indices indicated significant contamination in both water and soil samples, posing moderate to very high environmental risks based on World Health Organisation standards. Additionally, human health risk assessment suggested a risk of cancer and non-cancer-related diseases from prolonged well water consumption. Therefore, ongoing monitoring and remediation efforts are essential to forestall imminent pollution in uncovered areas.
Discover Civil Engineering, 2024
Waste disposal on land is a major environmental issue that affects groundwater through soil. The ... more Waste disposal on land is a major environmental issue that affects groundwater through soil. The aim of this study is to evaluate and compare the physical and engineering properties of subsoil at varying depths from different dumpsites and to determine the effects of leachates and parent rock types on these soil properties. Thirty-six subsoil samples were obtained from twelve trial pit profiles at depths of 0.5 m, 1.0 m and 1.5 m respectively. These soil samples were subjected to both geotechnical index and strength tests. Grain size analysis, linear shrinkage, CBR and compaction tests revealed that many of the soil samples from dumpsites and their environs met the criteria of a good landfill material. All the soil samples have low permeability that ranges from 2.07 × 10-6 to 1.49 × 10-4. Liquid limit, plasticity index and MDD values were higher in the control samples while the dumpsite soils became more permeable. The results of grain size analysis, linear shrinkage and CBR revealed that there was no significant difference in the properties of dumpsite and control soils. All the soil samples have TDS and EC values below 1000 ppm and 1000 µS/cm, except for trial pit 1, suggesting that the soil has a low risk of leaching contaminants into groundwater. For analysis of variance and Pearson's correlation coefficient, the P values of some parameters such as pH, EC, TDS, CBR, OMC, MDD, SG, LS, PI and LL were significant at a 0.05 level of significance. The following pairwise parameters: pH-MDD, TDS-CBR, EC-CBR, OMC-MDD and TDS-EC recorded strong positive correlation values for the three dumpsites. The statistical analyses reveal that the soil's properties were only slightly influenced by parent rock types and can be used to limit contaminant flow into the groundwater in the short term.
arXiv (Preprint), 2024
This comprehensive review examines electrical and seismic refraction methods, emphasizing their a... more This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface–subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for geological and geohazard assessments. The review also explores the induced polarization (IP) and self-potential (SP) methods as complementary approaches. Despite their effectiveness, ERT and SRT face challenges due to lithological heterogeneities, complex geological processes, and geophysical data uncertainties, necessitating multidisciplinary solutions such as methodological advancements and data integration strategies. Recently, machine learning (ML) techniques have been increasingly applied to joint ERT and SRT analyses, optimizing nonlinear inversion processes and improving the characterization of complex subsurface lithologies. The case studies presented in this review evaluate how supervised and unsupervised ML techniques enhance ERT and SRT by improving data interpretation, refining inversion accuracy, automating lithological differentiation, and predicting seismic velocity from resistivity data. The findings underscore the importance of integrating traditional geophysical methods with advanced data-driven approaches to improve subsurface investigations. Continued innovations in ERT and SRT methodologies, along with emerging computational tools and ML applications, will further enhance their effectiveness in geological, hydrological, environmental, and hazard assessments.
Environmental Science and Pollution Research, 2024
The significance of resistivity-chargeability relationships has been acknowledged and applied in ... more The significance of resistivity-chargeability relationships has been acknowledged and applied in various geologic terrains and different environmental conditions. However, there remains an underexplored opportunity to fully utilize these methods in complex geological terrains with a mixture of granitic and sedimentary rocks where empirical relationships have not been established. Such discoveries are crucial for accurately delineating petrophysical and geomechanical properties, which are essential in addressing urgent environmental concerns like landslides, foundation collapse, groundwater shortages, and pollution. To address this research gap, a novel approach was employed: resistivity-chargeability data with simple linear regression modeling. The study focused on developing resistivity-chargeability relationships specifically tailored for tropical granitic environments, using a typical example from Kedah Langkawi, Malaysia. The regions are characterized by complex geological features, ruggedness, and irregular progressive weathering and fracturing of subsurface strata, making the task challenging. Despite these complexities, the study successfully derived an efficient resistivity-chargeability empirical relation that correlates resistivity and chargeability. The derived empirical relationship exhibited high accuracy, surpassing 87%, in predicting chargeability from resistivity datasets or vice versa. This achievement holds great promise in promptly and accurately addressing environmental issues specific to the target region under study. By utilizing this novel resistivity-chargeability relationship, geoscientists, engineers, and environmental practitioners can make informed decisions and effectively manage environmental challenges in these regions, especially during the pre-development stage.
Quarterly Journal of Engineering Geology and Hydrogeology, 2024
Understanding the spatial variations in soil-rock profiles is crucial for identifying weathered r... more Understanding the spatial variations in soil-rock profiles is crucial for identifying weathered rock layers and mapping geological structures, particularly in the complex feldspar-rich granitic terrain of Penang Island, Malaysia. This study employs a combination of electrical resistivity and seismic refraction tomographic techniques, along with borehole lithological data, in three distinct areas on the island. Its goal is to provide insights into surficial and subsurface soil-rock properties and water-bearing structures within the North Penang Pluton (including a part of the Sungai Ara axis) for sustainable groundwater development, addressing the region's growing population's needs. The research identifies three distinct geological units near the surface: residual soils, highly to moderately weathered/fractured granitic layers, and fresh bedrock, each with its hydrogeological properties. Promisingly, the weathered layers offer significant groundwater development potential, with deep-weathered and potentially fractured zones exceeding 30 meters in the study area. These findings have substantial implications for sustainable groundwater development, especially in tropical hard-rock terrains. Additionally, the study underscores the limitations of small geophysical spacings for probing deeper groundwater resources. This knowledge is essential for informed decision-making in water resource management and infrastructure development, addressing the ongoing global challenge of ensuring access to clean water resources amid population growth.
Bulletin of Engineering Geology and the Environment, 2023
Rock quality designation (RQD) is a critical geoengineering/geotechnical parameter for evaluating... more Rock quality designation (RQD) is a critical geoengineering/geotechnical parameter for evaluating rock mass quality (RMQ), which is a preliminary construction decision-making tool. As a result, the soil-rock conditions of the southern part of Penang Island, Malaysia, a typical tropical granitic terrain, were evaluated using integrated seismic P-wave velocity (Vp), electrical resistivity (), and borehole-based RQD datasets. The regression analytical modeling technique was used to establish lithology-based correlations linking RQD with Vp and data. The study aims to provide novel insights for estimating RQD from Vp and based models to understand the RMQ, boundary conditions, and architecture of surficial-to-subsurface soil-rock profiles for infrastructure design. In addition, methodological approaches and empirical relationships adaptable to granitic terrains for estimating RQD where borehole drillings are impossible are being developed. The model provided significant results in addressing the limitations of the seismic refraction method by accurately delineating soil-rock conditions with shallow overburden. The study area is characterized by residual soils and poorly weathered rocks, which are the rippable and unsuitable units for the placement of infrastructure foundations. However, the potential sections for foundation placement were identified suitably on the integral/fresh bedrock between the depths of 8 m and 25 m in the study area. Reinforced concrete piling to fresh bedrock is most preferred. Most importantly, the empirical relations derived for RQD with Vp and data yielded strong correlations and potentially high prediction results, with R 2 values of 0.96 (96%) to 0.99 (99%). Generally, the research findings will considerably reduce the uncertainties and costs associated with borehole-based RQD evaluation for large aerial extent investigations.
Geophysica, Feb 2020
This study entails the detailed analyses of high-resolution gravimetry dataset using enhancement ... more This study entails the detailed analyses of high-resolution gravimetry dataset using enhancement techniques for characterising and delineating the locations, edges/boundaries, trends, and depths of litho-structural features around Precambrian basement complex of Igabi region, Northwestern Nigeria with a view of evaluating the structural architectures that harbor mineralization in the study area. The analyzed results of the bouguer anomaly and residual maps of study area showed the distribution of the gravity anomalies and magnitudes of the concealed structures based on the observed low to very high gravity anomalies. Bouguer anomalies around Igabi area ranged between-67.77 to-53.34 mGal reflecting the density variations within bedrock. The upward continued bouguer anomaly maps at distance 500 m, 1 km, 2 km, 3 km, and 4 km revealed the variations of the deep-seated basement rocks, the structures and the concealed anomalous bodies with general regional trends in NW-SE, E-W, and NE-SW directions. The bouguer analytic signal and its superimposed maps further revealed that areas with low amplitude signals may be associated with migmatites, schists, less dense felsic rocks (porphyritic granites) and fractures, and areas of high amplitude signals may be associated with denser biotite granitic and gneissic rocks. In addition , the second vertical derivative and tilt derivative maps clearly revealed the density of shallow basement rocks and near circular closures anomalies associated with fractures within the granitic rocks. Spectral analysis suggests depth to gravity sources range between 0.3 km and 0.67 km for shallow, 0.90 km to 0.97 km for intermediate and 1.5 km to 1.86 km for deep sources while Euler sources depths ranged from <1392.3 m to >2059 m. Based on the calculated bouguer anomalies such as variation in rocks densities, different structures and varying trends of litho-structures in with subsurface depth may have suggested intense deformation of the Basement rocks with varying tectonic framework in the study area over time.
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Papers by Adedibu Sunny Akingboye