Development of novel resistivity-chargeability statistical relationships for subsurface characterization at Langkawi, Kedah.

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GABRIEL ABRAHAM BALA, Andy Anderson Bery, Joseph Gnapragasan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3588974/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract 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. Resistivity Chargeability Regression Analysis Empirical Relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In the field of geosciences, there is a growing trend in the adoption of state-of-the-art surveying, inversion, and statistical techniques for multi-geophysical imaging and inversions. Both direct and indirect geophysical methods are used to assess the physical and geomechanical characteristics of subsurface geology at various spatial and temporal scales (Marquis and Hyndman 1992 ; Meju et al. 2003 ; Gallardo and Meju 2003, 2007, 2011; Bery and Saad 2012 ; Merritt 2014; Akingboye and Bery 2022 ). To achieve accurate predictions of distinctive geological features in geoscience, a range of detailed inversion models is essential. In the realm of geophysics, inversion models serve as invaluable tools for elucidating the spatial distribution of various physical attributes, such as electrical resistivity/conductivity, chargeability, seismic velocities, and other geophysical properties (Ronczka et al. 2018 ; Zeng et al. 2018 ; Shang and Hasan 2021 ; Akingboye et al. 2022 ; Loke et al. 2022 ). These attributes often stand as proxies for fundamental rock properties, including porosity, mineral composition, fracture density, and more (Muñoz et al. 2010). It's worth noting that these physical attributes are underpinned by intricate combinations of variables, rendering their interpretations far from straightforward. This complexity arises from the need to integrate diverse geophysical datasets stemming from various physical phenomena, each exhibiting different spatial scales of measurement, while also accounting for the inherent complexities and uncertainties in geological models (Gallardo and Meju 2007, 2011). Hence, the selection of a geophysical method becomes a critical decision when aiming to enhance the signal-to-noise ratio and refine our understanding of subsurface geological conditions within projects related to crustal exploration, infrastructure development, environmental assessments, subsurface simulations, and the like. This strategic choice not only accelerates project timelines and minimizes costs but also optimizes the overall success of project outcomes. As a result, seismic and electrical methods have emerged as the go-to geophysical techniques for assessing near-surface crustal projects. Exploration of the Earth's subsurface has been a foundational endeavor in the realm of geophysics, with profound implications for various fields such as resource exploration, environmental management, and civil engineering. Accurate and detailed subsurface characterization is essential for understanding the geological and hydrogeological aspects of a given region. The archipelago of Langkawi, nestled in the Kedah state of Malaysia, provides a compelling backdrop for the development of novel resistivity-chargeability statistical relationships. Characterization is an essential aspect of geotechnical and environmental investigations. Electrical resistivity tomography (ERT) and induced polarization (IP) techniques are commonly used geophysical methods for subsurface characterization. ERT measures the electrical resistivity of the subsurface, while IP measures the chargeability of the subsurface. Both techniques provide information about the subsurface's lithology, porosity, and fluid content. The statistical relationship between electrical resistivity and chargeability has been studied in various geological settings. The relationship between these two parameters is essential for subsurface characterization, especially in areas with complex geology. The development of novel resistivity-chargeability statistical relationships can improve the accuracy of subsurface characterization. This paper aims to present the development of novel resistivity-chargeability statistical relationships for subsurface characterization in Langkawi, Kedah Malaysia. Langkawi is a complex geological area with various lithologies, including granite, limestone, and sandstone. The study area's subsurface characterization is essential for groundwater exploration, environmental monitoring, and geotechnical investigations. The development of novel resistivity-chargeability statistical relationships for subsurface characterization in Langkawi can improve the accuracy of subsurface characterization and provide a better understanding of the area's geology. The paper will present the methodology used for data acquisition, processing, and analysis. The results of the study will be discussed, and the implications of the findings will be highlighted. The paper's contribution to the field of geophysics and subsurface characterization will also be discussed. Literature Review At two locations in Penang, Malaysia, Akingboye and Bery ( 2021 ) conducted research to assess the effectiveness of utilising induced polarisation (IP) and electrical resistivity tomography (ERT) in subsurface tomographic analyses of copper and traditional stainless-steel electrodes. According to the findings, The best convergence is found in ERT and IP tomographic models. The evaluation of the electrodes' performance in the two targeted sites revealed the mean absolute percentage error (MAPE). Adedibu and Bery (2023) explained the rise in popularity and significance of multi-geophysical imaging and inversions in the geosciences. The empirically harmonised velocity-resistivity connection that was derived in tropical granitic environments, statistically significant, according to the accuracy evaluation. Meanwhile, Hasan et al., 2020 conducted an integrated geophysical survey to assess the subsurface geological formation in a weathered terrain in South Huizhou, China. The survey involved the use of electrical resistivity tomography (ERT), induced polarization (IP), magnetic method, and joint profile method (JPM). The goal was to determine the suitability of the site for the construction of deep engineering structures and to identify groundwater resources. The results of the survey revealed four discrete layers in the subsurface: topsoil cover, highly weathered layer, partly weathered layer, and unweathered bedrock. Each layer had a specific range of resistivity values. The integration of ERT with IP, magnetic, and JPM allowed for the delineation of faults, fractures, and the identification of the weakest zones for engineering projects. The unweathered fresh bedrocks were identified as the most appropriate locations for the construction of deep structures. Additionally, the areas with low resistivity and low chargeability were identified as suitable places for groundwater occurrence. Sono et al. (2020) identified and characterized probable mineralization Zones containing gold in the Phistine Molopo area, Southeast Botswana, using 2D and pseudo-3D induced polarisation (IP). They were able to distinguish between different lithological units that includes mineralized and non-mineralized zones because of the chargeability and resistivity results combined. The reason behind the high value of both resistivity and chargeability anomalies is linked to the availability of Gold-Bearing Quartz and Carbonate veins that are linked to the Branded Iron Formation (BIF) units towards the east-west trends. History of the exploration shows that Gold Mineralization is linked to the Quartz and Carbonates veins that is across the branded formations. The Gold-Bearing Quartz and Carbonate veins are characterized by coarse-grained euhedral pyrite and quartz-adularia veins. Kang et al. (2023) measured took the measurement of both resistivity and chargeability at the same the face of the tunnel in the laboratory chamber during an excavation exercise. The value of the resistivity and chargeability readings that were linked with the TBM advancement was used to predict the mixed ground conditions during the tunnel excavation and construction. The pattern of the electrical resistivity during TBM advance was experimentally estimated under harsh conditions. Using the electrical resistivity and induced polarization methods will help in detecting very toxic grounds ahead of the TBM tunnel face pattern and fluctuations that were tested and recommended for perfectly predicting risky conditions were made. Due to their low noise and vibration levels and excellent safety in construction, tunnel boring machines (TBMs) are believe to be more convenient during excavation of tunnel in urban areas (cities) with hih population, complex and infrastructure that are very sensitive. The modelling of the advance tunnel was from H/M = 1.2 to 0, where H/M = 0 denotes that the TBM model has reached the top of the fault zone. The normalized distance (H/M) between the TBM model and the fault zone was determined by dividing the actual distance (H) by the width of the TBM model (M). Kyle Robinson et al. (2022) conducted a study that showcases the benefits of integrating combined geoelectrical imaging techniques, most especially 3D high-resolution Direct Current (DC) resistivity and induced polarization (IP), to enhance the interpretation of groundwater-surface water interactions along a stream reach. The study focused on investigating the streambed architecture and its impact on groundwater-surface water interactions. The findings highlighted the untapped potential of utilizing dimensional continuous, high-resolution DC-IP data to effectively map streambed architecture and enhance the understanding the dynamics of groundwater-surface water interactions along a stream reach. The study contributes to the understanding of groundwater-surface water interactions and provides insights into the use of geoelectrical imaging for improved characterization of streambed architecture. Geological Setting Peninsular Malaysia forms a part of the Southeast Asian continental core within the broader region known as Sundaland. Geologically, it consists of two distinct blocks or landmasses: the Sibumasu Terrane in the western part and the Sukhothai Arc (also known as the East Malaya Block) in the east. These two landmasses came together and merged during the Late Triassic period, as described in studies by Metcalfe (2013a, 2013b) and Pour and Hashim (2015). Peninsular Malaysia, situated on the southeastern margin of the Eurasian continent, took shape through a complex geological process involving the amalgamation of two distinct blocks: the Sibumasu Block and the Indochina Block. This geological transformation occurred along the Bentong-Raub Suture Zone (BRSZ) during the Permian-Triassic period, as supported by several studies (Metcalfe 2000, 2001, 2013a; Jasin 2013; Ng et al. 2015a , 2015b; Cao et al. 2020). Langkawi, part of the Western Belt of Peninsular Malaysia, is primarily composed of Precambrian metamorphic rocks and granite. The granite formations, resulting from slow cooling of magma, create the unique landscapes. The islands exhibit a mix of metamorphic rocks, including schists and gneisses, shaped by tectonic activity. Karst landscapes feature prominently, with limestone formations, caves, and towers formed through chemical weathering and erosion, as shown in the geology Map of Langkawi in Fig. 1 . Fossilized marine life in limestone hints at the region's marine geological history. Quaternary sediments, such as alluvium, contribute to the coastal areas. Tectonic activity over time has led to rock uplift and diverse landforms. The shoreline showcases sandy beaches influenced by ongoing geological processes. Local geological studies provide more specific details about rock types, structures, and chronological events in Langkawi. Methodology The methodologies employed for this study are in phases and the survey was carried out on a construction site in Kuah, Langkawi kedah as shown in Fig. 2 .(a, b, c, d and f).The details of each research phase are encapsulated in the methodological flowchart depicted in Fig. 3 , while Fig. 4 illustrates the procedural steps specific to the Resistivity-Chargeability of the Simple Linear Regression (SLR) methodological flowchart. In order to make this research cost-effective and high-performing, the first phase entails the selection of suitable sites on Langkawi Island, with borehole data available in the locations. The electrical resistivity tomography (ERT) surveys were carried out using the Lund Resistivity Meter, employing the Schlumberger-Wenner array, following the techniques of Akingboye and Bery ( 2021 , 2022 ). ERT survey was performed along three profiles namely L1, L2 and L3 using a fixed electrode spacing of 5 m, a total of 522 datum points for resistivity and IP and a total profile length of 200 m for the three lines. The total number of electrodes and data level is 42 and 23 respectively. The drilled boreholes names BH1 and BH2 are at a station distance of 90 m along L1 and 65 m and 135 m along L3 respectively. The RES2DINV software were used to create the final underground resistivity and chargeability models for the study area. These software tools were thoroughly explained in previous works by Akingboye and Bery in 2021 and 2022. To improve the accuracy of the models at L1 and L3, we incorporated the information obtained from the borehole litho logs (BH1–BH2). To establish the empirical relationships between both parameters and accurately predict the chargeability in the targeted terrain, we first filtered the extracted resistivity and chargeability values to eliminate any outliers. The resulting dataset, consisting of 125 filtered collocated models' pixels, was then used for regression analysis. In this analysis, chargeability was considered the dependent variable, while resistivity served as the independent variable. To ensure the statistical integrity of the regression model, we employed the IBM Statistical 27 software to conduct various tests, including ANOVA, multivariate normality, multicollinearity (using tolerance and variance inflation factor, VIF), Durbin-Watson (D-W), and homoscedasticity tests. Furthermore, we assessed the accuracy of the derived resistivity-chargeability empirical relationship by comparing the actual and predicted chargeability models. This evaluation involved calculating the root-mean-square error (RMSE) and mean absolute percentage error (MAPE) using Equations 1 and 2 , respectively. For the model to be considered statistically accurate, both the RMSE and MAPE must be below 10%. $$RMSE=\sqrt{\frac{{\sum }_{i=1}^{N}{\left(O{C}_{i}-P{C}_{i}\right)}^{2}}{N}}$$ 1 $$MAPE=\frac{100\%}{N} {\sum }_{i=1}^{N}\left|\frac{O{C}_{i}-P{C}_{i}}{O{C}_{i}}\right|$$ 2 where OC and PC are the observed chargeability and predicted chargeability values for the study area. ERT field data processing and modelling The comprehensive workflow depicting the data processing and inversion steps for the Electrical Resistivity Tomography (ERT) and field datasets is illustrated in Fig. 4 . To begin, the resistivity data, along with topographical information, acquired from all the three (3) lines underwent a series of processing and inversion steps. These procedures were executed using the RES2DINV software, a well-established tool in geophysical research (Loke and Barker 1996 ; Loke 2004 ). The standard least-squares inversion methodology, as depicted in Fig. 4 , was followed meticulously throughout the analysis. In brief, the finite element method employing four nodes and L2-norm was employed as the standard least-squares constraint parameters to minimize the disparity between the measured and computed apparent resistivity values. To enhance the precision of the calculated and observed apparent resistivities, a damping factor of 0.05, with a minimum value set at 0.01, was applied. The root mean square error (RMSE) of the inverse models consistently reached convergence below 10% within a maximum of five iterations, without the exclusion of any ERT data points. However, to enhance the accuracy of the models, the RMSE statistics cutoff approach was adopted. Chargeability-Resistivity regressional analysis and modeling Regression analysis offers numerous advantages in statistical investigations and modeling, serving various purposes such as forecasting, time series modeling, and gaining insights into the relationships between variables. Simple Linear Regression (SLR) is a straightforward and efficient technique, although its application to real-time nonlinear data can be challenging. A well-fitted linear model should ideally exhibit randomly distributed residuals when viewed on a scatterplot, and its histogram should closely resemble a normal distribution (Carroll and Green 1997; González et al. 2019). In this study, we conducted a regression analysis on a dataset comprising ρ (resistivity) and chargeability values extracted from collocated model pixels (N = 125). These values were employed as dependent and independent variables, respectively, in SLR plots using Microsoft Excel. The objective was to establish empirical relationships between chargeability and resistivity in granitic environments. Outliers were identified and removed from the datasets to ensure a good fit for the SLR models. This is clearly shown in the Simple Linear Regression (SLR) of Fig. 7 . Additionally, we derived the logarithm of the collocated resistivity (ρ) and chargeability m data to explore which regression models would yield higher predictive accuracy. It's important to note that resistivity data (the independent variable) were used to predict chargeability data in this study. This approach was chosen due to the specific challenges associated with Self-Potential (SRT) data in crystalline basement terrain, especially when the overburden is less than 50 meters. Such challenges may include hidden/blind zones and difficulties in accurately determining soil-rock constituents and thicknesses (Lin et al. 2015). For the evaluation of the SLR models, we utilized IBM SPSS (v. 27) software to assess their accuracy based on the 125 extracted collocated resistivity and chargeability data. The regressed variables provided valuable insights into critical parameters, such as linear correlation (R), coefficient of determination (R 2 ), unstandardized (B), and standardized (β) coefficients, along with p values. Various statistical tests, including analysis of variance (ANOVA), assessments of multivariate normality, evaluations of multicollinearity (i.e., tolerance and variance inflation factor, VIF), Durbin–Watson (D–W) tests, and examinations of homoscedasticity, were conducted to ascertain the accuracy and reliability of the SLR models (Carroll and Green 1997; Salkind 2007; Akingboye and Bery 2022 ). The resulting histogram plot, normal P–P plot (for stepwise SLR), and scatterplot of regression normalized residuals were employed to validate the outcomes of these regression analyses. Results and Discussions Within the study area, a variety of subsurface lithologic layers were identified beneath the surveyed sites, encompassing a spectrum of materials from silt to dry sand in the uppermost weathered and fractured layers, down to the pristine bedrock. These lithologic units were discerned through both borehole logs and field mapping. For Line 1, the subsurface lithologic units extracted from borehole data from BH1 and BH2 at 65 m and 135 m and BH 1 at 90 m on Line 3 consisted of sandy silt (topsoil), silty-sand with some gravels and sandy-silt with gravel ( Fig. 5 and Fig. 6 respectively). These units exhibited varying resistivity values, with the sandy silt registering resistivity values of less than 0.5–1000 Ωm. The integration of Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) methods, combined with borehole data, has provided valuable insights into the subsurface characteristics of the study area in Kuah, Langkawi Island, Malaysia. This region is known for its complex geological history, featuring a sedimentary rock formation that has been intruded by granitic rock. In this discussion, we will delve into the key findings and interpretations of the ERT-IP models along three research lines, with a focus on lithological variations, resistivity values, and chargeability measurements. Lithological Variations The ERT-IP models have revealed distinct lithological units along the research lines. These units include sandy-silt, silty-sand with some gravel, and sandy-silt with some boulders. These variations in lithology are indicative of the heterogeneity and complexity of the subsurface geology in the study area. Understanding these lithological variations is crucial for various geotechnical and hydrogeological applications, as they can significantly influence the movement of groundwater, the stability of foundations, and the distribution of resources. One of the noteworthy outcomes of this study is the wide range of resistivity values observed across the research lines, spanning from 0.5 ohm-m to 1000 ohm-m. These values reflect the electrical properties of the subsurface materials and are instrumental in distinguishing between different lithological units. The sandy-silt layers typically exhibit lower resistivity values (< 20–40 ohm-m), indicating higher moisture content and greater electrical conductivity. The presence of resistivity values ranging up to 1000 ohm-m in the study area is consistent with the intrusion of granitic rock, known for its higher resistivity compared to sedimentary formations. The granitic intrusions are likely responsible for the localized spikes in resistivity observed in certain areas. Chargeability Measurements The chargeability measurements, ranging from 1 ms to 50 ms, provide insights into the polarization characteristics of the subsurface materials. Higher chargeability values often correspond to the presence of metallic or conductive minerals, which can be indicative of geological features such as sulfide mineralization or altered zones. In contrast, lower chargeability values may signify the absence of such minerals or the predominance of non-conductive materials. Interpretation and Geological Significance The observed lithological variations, resistivity values, and chargeability measurements are consistent with the complex geological history of the study area. The sandy-silt layers are likely associated with sedimentary deposits, while the silty-sand with gravel and sandy-silt with boulders layers may indicate a mixture of sedimentary and granitic materials. The higher resistivity values in certain areas are suggestive of granitic intrusions, which are known to exhibit lower conductivity. The chargeability measurements can aid in identifying potential mineralization zones or altered rock units within the subsurface, which may have economic significance for mineral exploration. In conclusion, the ERT-IP models, in conjunction with borehole data, have provided valuable information about the subsurface composition and characteristics of the study area in Kuah, Langkawi Island, Malaysia. These findings are essential for geological and geotechnical assessments and may have implications for various applications, including groundwater management, engineering design, and mineral exploration in this geologically diverse region. Further studies and detailed geological investigations are warranted to refine our understanding of the complex sedimentary and granitic rock interplay within this area. The subsurface lithologic units below Line 1, including sandy silt (topsoil), sand, and silty sand, exhibit resistivity values within the ranges of approximately < 100–400 Ωm, 600–800 Ωm, and 30–50 Ωm, respectively. Line 1 displays chargeability models characterized by distinct layers, encompassing residual soils (silty sand), weathered or weathered-fractured rock units, integral or hard granite, and fresh granitic bedrock, each with corresponding chargeability values of 39–50 ms, 14–20 ms, and 1–14 ms, respectively. Conversely, the resistivity models for Line 3 reflect a diverse range of resistive subsurface properties, mirroring their chargeability models and borehole logs. Elevated resistivity values signify the presence of bedrock near the surface, as well as the existence of stiff-to-hard silty sand and sand layers. Notably, the topmost silty sand exhibits resistivity values ranging from about 100 to > 1000 Ωm. Line 1, in particular, identifies the dry sand as the highly resistive entity within the models. From this collocated inversion approach, we extracted a substantial dataset comprising 125 paired resistivity and chargeability data points, originating from coincident and closely situated model pixels. These data points were then subject to graphical regression analysis, the results of which are presented here. Through this process, we've enhanced our understanding of the variations in chargeability and ρ at specific station positions and depths. Furthermore, the outcomes of this analysis shed light on the typical range of resistivity and chargeability values associated with the lithologic units we've delineated within the study area. This information extends to cover not only our immediate research site but also sheds light on analogous tropical granitic terrains with relatively shallow overburden, broadening the applicability of our findings. Subsurface lithologic characterization and chargeability–resistivity statistical modeling To gain deeper insights into the characteristic range of lithologic units below our study area, we combined and co-located the resistivity and chargeability models obtained from Lines 1, 2, and 3. This comprehensive approach allowed us to establish correlations and unveil the intricate relationships between resistivity and chargeability within the geological context. Within this integrated framework, we present a representative model that encapsulates the combined data for Lines 1, 2, and 3. In total, we extracted 125 collocated resistivity and chargeability data points, strategically chosen to coincide with and closely align to model pixels. These data points were then subjected to graphical regression analysis, revealing a nuanced perspective of the resistivity and chargeability models within the same station positions and depths. This analytical process serves to elucidate the inherent variability in resistivity and chargeability, ultimately shedding light on the typical range of values characterizing the delineated lithologic units within the study area. Moreover, these findings provide valuable insights into the distinctive characteristics of geological formations in tropical sedimentary terrains, particularly those with shallow overburden. Owing to the intricate nature of subsurface lithologic units in tropical regions, characterized by their complexity, rugged topography, irregular distribution, and susceptibility to weathering and fracturing, our investigation necessitated the formulation of a unified empirical relationship connecting resistivity and chargeability. This pivotal empirical relation was initially derived from the Statistical Linear Regression (SLR) plot, as detailed in Eq. 3. The established unified resistivity-chargeability relationship was rigorously validated by employing the Semi-Logarithmic Resitivity (SLR) data, where the dependent chargeability was regressed against independent resistivity data. This validation process involved comprehensive statistical analyses, encompassing essential correlation tests and an analysis of variance (ANOVA), as outlined in Table 1 . The SLR analysis revealed a notably robust positive correlation between chargeability and resistivity, with an impressive R value of 0.953. This strong relationship is further substantiated by an R-squared (R²) value of 0.87, indicating a prediction accuracy of approximately 87%. The precision of this prediction is bolstered by the remarkably low standard error of 4.5688. Furthermore, our analysis suggests that for each predicted chargeability value inferred from resistivity (ρ), there is an associated unstandardized (B) value of 0.953, with a constant term of 168.764. Notably, the Durbin-Watson (D-W) statistic of 1.822, which is less than 2, implies an absence of autocorrelation between the two parameters. The statistical results presented in Table 1 are highly compelling. The p-value, which is less than 0.0001%, and the substantial F-statistics of 841.35 collectively demonstrate the excellent fit of the variables within the regressed model. This underscores the robustness and significance of our findings, aligning with prior research (González et al. 2019; Akingboye and Bery 2022 ; Balarabe et al. 2022). By employing the unified empirical relationship between chargeability and resistivity, as described in Eq. 3, along with its lower and upper empirical bounds as given by Equations 4 and 5 , it becomes possible to assess the extent of variability and the potential range of predicted chargeability values within the study area. This approach enhances the robustness and accuracy of the results obtained. IP = 0.0512 ρ + 9.7046 (3) where IP is the induced polarization or chargeabilty and ρ is the resistivity. The 95% lower and upper confidence intervals for B are related as: $${IP}_{LB,UB}= \rho +C$$ 4 Therefore, the equations that define the 95% lower and upper confidence intervals for B, utilizing the derived constant C, are provided as follows: $${IP}_{LB}= 0.048 \rho +8.385$$ 5 $${IP}_{UB}= 0.0055 \rho +11.024$$ 6 where, \({m}_{LB}\) and \({m}_{UB}\) represent the respective lower and upper bounds of the predicted chargeability based on the unified chargeability-resistivity empirical relationship. Moreover, the histogram trend and the linearity observed in the standardized residuals of the regressed data (refer to Fig. 8 a, b) strongly support the significant accuracy of the Simple Linear Regression (SLR) model. The collinearity statistics of the regression model, indicated by tolerance and VIF values both at 1.0, further indicate the absence of multicollinearity. Additionally, the scatterplot (Fig. 8 c) displays no clustering or systematic patterns among the analyzed variables, meeting the assumption of homoscedasticity as per Tabachnick and Fidell (2019) and González et al. (2019). Consequently, the derived unified velocity–resistivity empirical relationship demonstrates statistical significance in tropical sedimentary region with granitic intrusion environments, as affirmed by the accuracy assessment. Consequently, based on this comprehensive accuracy assessment, it is evident that the derived unified chargeability-resistivity empirical relationship holds significant statistical merit, particularly in the context of tropical sedimentary region with granitic intrusion environments. Conclusions In this study, we have endeavored to construct statistically sound relationships between chargeability and resistivity through a comprehensive approach that integrates collocated chargeability-resistivity tomographic modeling and Semi-Logarithmic Resistivity (SLR) analysis. Our investigation was conducted in the unique geological context of tropical sedimentary environments with granitic intrusions on Langkawi Island, Malaysia. These sites exhibit diverse subsurface crustal structures, resulting in varying resistivity values along the three Electrical Resistivity Tomography (ERT) lines, particularly in regions with high-resistivity materials. Through a meticulously designed methodology that includes simultaneous chargeability-resistivity inversion and SLR modeling workflows, we have successfully established unified and specific empirical relationships between chargeability and resistivity. These relationships are tailored to address the distinctive characteristics of residual soils, weathered or weathered-fractured granitic formations, and intact or fresh bedrock. The derived chargeability-resistivity empirical relations have demonstrated a noteworthy practical prediction accuracy exceeding 85%. These relations also exhibit robust positive correlations, meeting all the criteria essential for accurate SLR models. Consequently, we can confidently assert that the chargeability-resistivity empirical relationships we have developed hold substantial statistical significance within the geological context of Langkawi Island. Declarations Acknowledgements The authors would like to express gratitude and appreciation to individuals who have contributed to the completion of this paper. Special appreciation to the Editor and the reviewers for their valuable comments and recommendations, which have greatly contributed to improving the quality of our paper. Materials availability The writers can provide data upon request. Author contributions Gabriel Abraham Bala: Conceptualisation, writing-original draft preparation, investigation, and methodology. Andy Anderson Bery: Supervision, Resources, Writing-review & editing, and Validation. Joseph Gnapragasan: Editing, and Validation. Adedibu Sunny Akingboye: Validation, and Resources. Funding The authors express their gratitude to the Malaysian Ministry of Higher Education (MoHE) for providing financial support via the Fundamental Research Grant Scheme (203/PFIZIK/6712108), as well as to Universiti Sains Malaysia for the Short-Term Grant (304/PFIZIK/6315489), which have enabled the funding of this research endeavours. Ethics approval: All ethical standards have been followed during this research. Consent to participate: All Authors and corresponding authors have given consent to participate Consent to publish: All Authors and corresponding authors have given consent to participate Conflict of interest/competing interest: The authors declare no competing interest. I want to clearly state that, this manuscript has not been submitted to any preprint server prior to submission to ESPR. References Akingboye, A. S., and Bery, A. A. (2021). Evaluation of lithostratigraphic units on groundwater potential using the resolution capacities of two electrical different tomographic electrodes at dual spacing. Contributions to Geophysics and Geodesy , 51 (4), 295–320. Akingboye, A. S., and Bery, A. A. (2022). Characteristics and rippability condition of near-surface strata (Batu Maung, Penang Island, Malaysia) derived from borehole-constrained geotomographic models and geostatistical analyses. Journal of Applied Geophysics , 204, 104723. Akingboye, A. S., and Ogunyele, A. C. (2019). 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Analytical model for predicting the UCS from P-wave velocity, density, and porosity on saturated limestone. Applied Sciences 9:5265. https:// doi. org/ 10. 3390/ APP92 35265 Loke M.H, Barker RD (1996). Practical techniques for 3D resistivity surveys and data inversion 1. Geophysical Prospecting, 44:499–523. https://doi.org/ 10. 1111/j. 1365- 2478. 1996. tb001 62.x Loke M.H, Wilkinson PB, Chambers JE, Uhlemann S, Dijkstra T, Dahlin T (2022). The use of asymmetric time constraints in 4-D ERT inversion. Journal Applied Geophysics 197:104536. https://doi. org/ 10. 1016/j. jappg eo. 2022. 104536 Loke M.H (2004). Rapid 2D resistivity and IP inversion using the least-square method— Geoelectrical Imaging 2-D and 3D. p 129 Marquis G, Hyndman RD (1992) Geophysical support for aqueous fluids in the deep crust: seismic and electrical. Geophysics Journal International. p 110:91–105. https:// doi.org/10. 1111/j.1365- 246X. 1992. tb007 16.x Meju M.A, Gallardo LA, Mohamed AK (2003). Evidence for correlation of electrical resistivity and seismic velocity in heterogeneous near-surface materials. Geophysical Research Letters. https://doi.org/10. 1029/ 2002G L016048 Ng SW-P., Whitehouse MJ, Searle MP, Robb LJ, Ghani A. A, Chung, S.L, Oliver,G.J.H, Sone M, Gardiner NJ, Roselee MH (2015a). Petrogenesis of Malaysian granitoids in the Southeast Asian tin belt: Part 2. U-Pb zircon geochronology and tectonic model. Geological Society of America Bulletin. 127:1238–1258. https://doi.org/10. 1130/B31214.1 Pelton, W. H & Ward, S, H. (1998). Induced Polarization in Geophysical methods in geology (vol. 3. 583-626) Elsevier. Ronczka M, Wisen R, Dahlin T (2018). Geophysical pre-investigation for a Stockholm tunnel project: joint inversion and interpretation of geoelectric and seismic refraction data in an urban environment. Near Surface Geophysics 16:258–268. https://doi.org/10. 3997/1873- 0604. 20180 09 Shang Y, Hasan M (2021). Analysis of rockslide and engineering slide via integration between rock mechanical and geophysical parameters. In: IOP Conference series: Earth and Environmental science vol 861, https://doi.org/10.1088/1755- 1315/ 861/2/ 022013 p 022013 Slater LD, Glaser DR (2003). Controls on induced polarization in sandy unconsolidated sediments and application to aquifer characterization. Geophysics 68:1542–1558. https:// doi. org/ 10. 1190/1. 1620628 Slater LD, Lesmes D (2002). IP interpretation in environmental investigations. Geophysics 67:77–88. https:// doi. org/ 10. 1190/1. 14513 53 Tabachnick B.G, Fidell L. S, (2019). Using multivariate statistics, 7 th edition Pearson. https:// www. pears on. com/ us/ higher- educa tion/ program/ Tabac hnick- Using- Multi varia te- Stati stics- 7th- Edition/ PGM24 58367. html Telford W..M, Geldart L.P, Sheriff RE (1990). Applied geophysics, 2 nd edition Cambridge University Press, p 792. ISBN: 9780521339384 Zeng Z, Kong L, Wang M, Sayem HM (2018). Assessment of engineering behaviour of an intensely weathered swelling mudstone under full range of seasonal variation and the relationships among measured parameters. Can Geotech J 55:1837–1849. https://doi.org/10.1139/cgj- 2017- 0582 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Jan, 2024 Editor assigned by journal 21 Dec, 2023 First submitted to journal 13 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3588974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270121500,"identity":"6666e5f0-908b-44ef-b1c4-3f01ce85d906","order_by":0,"name":"GABRIEL ABRAHAM BALA","email":"data:image/png;base64,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","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":true,"prefix":"","firstName":"GABRIEL","middleName":"ABRAHAM","lastName":"BALA","suffix":""},{"id":270121501,"identity":"05d2b148-bb80-4e43-96a7-3d84e9145c0c","order_by":1,"name":"Andy Anderson Bery","email":"","orcid":"https://orcid.org/0000-0001-8974-9808","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Andy","middleName":"Anderson","lastName":"Bery","suffix":""},{"id":270121502,"identity":"4ca8566b-e6ea-4cf8-8dd5-69d325b74781","order_by":2,"name":"Joseph Gnapragasan","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Gnapragasan","suffix":""},{"id":270121503,"identity":"c14f0c36-585c-463e-ad9a-aa0697ca0ddd","order_by":3,"name":"Adedibu Sunny Akingboye","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Adedibu","middleName":"Sunny","lastName":"Akingboye","suffix":""}],"badges":[],"createdAt":"2023-11-10 06:42:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3588974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3588974/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50484040,"identity":"bd37f3f7-106f-4ca3-a6f2-3a12828c8248","added_by":"auto","created_at":"2024-02-01 08:40:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":163213,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of Langkawi Island (Shafeea et al., 2007).Top of Form\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/8f04ce5d9c13fc1c0cdb36b3.png"},{"id":50484309,"identity":"4f214e18-3c5d-48d7-80e4-d740b07d05d2","added_by":"auto","created_at":"2024-02-01 08:48:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2560229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eA Google Earth Aerial image of the study showing all the three ERT lines.; \u003cstrong\u003e(b)\u003c/strong\u003eA georeferenced map of the study area showing the three ERT lines (L1, L2, and L3).; \u003cstrong\u003e(c)\u003c/strong\u003e A GPRS device showing the Longitude and Latitude of the starting point of Line 1 (L 1). ; \u003cstrong\u003e(d)\u003c/strong\u003e A geological hammer placed at the starting point of Line 1 at, Kuah Langkawi. ; \u003cstrong\u003e(e)\u003c/strong\u003e The two borehole logs (BH 1 and BH 2) at the study area showing the different lithologies.; (f) The researcher at one of the historic locations of Legion Langkawi.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/39c1aeacf405f1de0f1773ab.png"},{"id":50484044,"identity":"82795d53-a944-49ad-98a2-a0d2d776984a","added_by":"auto","created_at":"2024-02-01 08:40:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":397726,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological Flowchart for the different phases of the study.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/468890e1959c64348098df77.png"},{"id":50484037,"identity":"6822ca09-784e-4f79-abf0-7ba1947fcd1b","added_by":"auto","created_at":"2024-02-01 08:40:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":629854,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flowchart Resistivity-Chargeability of the Simple Linear Regression (SLR).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/d420cec4bb008623d9c31aae.png"},{"id":50484310,"identity":"9cb72b92-de7b-490a-a677-759572762c6a","added_by":"auto","created_at":"2024-02-01 08:48:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":830580,"visible":true,"origin":"","legend":"\u003cp\u003e(a) ERT inversion model for L 1 with borehole (BH 1 and BH 2). (b) Chargeability inversion model for L 1.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/c6a9202f8138bf7978e60bbe.png"},{"id":50484308,"identity":"2a9c7311-1fc7-46b8-9193-ab4b7998dcaa","added_by":"auto","created_at":"2024-02-01 08:48:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":609486,"visible":true,"origin":"","legend":"\u003cp\u003e(a) ERT inversion model for L 3 with borehole (BH 1). (b) Chargeability inversion model for L 3.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/57dbe671a20171a32d5fe9cb.png"},{"id":50484038,"identity":"43cffb93-50b4-4375-9b5c-6739a5927ff3","added_by":"auto","created_at":"2024-02-01 08:40:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47908,"visible":true,"origin":"","legend":"\u003cp\u003eSLR plot of the filtered Chargeability–resistivity data for the study area (numbers of extracted collocated pixel datasets, (\u003cem\u003eN \u003c/em\u003e= 125).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/18d785810d82184524eff73e.png"},{"id":50484041,"identity":"2a79c7d0-b211-489a-9538-f4a6a0cbbf46","added_by":"auto","created_at":"2024-02-01 08:40:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":265306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eHistogram plot of the regression standardized residual, \u003cstrong\u003eb \u003c/strong\u003enormal P–P plot of regression standardized residual, and \u003cstrong\u003ec \u003c/strong\u003escatterplot of the regression standardized residual against the regression of standardized predicted value for the analyzed chargeability–resistivity models\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/7bd8c63388f598a021f38a21.png"},{"id":50485501,"identity":"83543d91-0c72-43e6-a9bd-8942dff4b1fb","added_by":"auto","created_at":"2024-02-01 09:04:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4525586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3588974/v1/721167f0-5551-48cd-8a06-f6af6abc96e3.pdf"}],"financialInterests":"","formattedTitle":"Development of novel resistivity-chargeability statistical relationships for subsurface characterization at Langkawi, Kedah.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the field of geosciences, there is a growing trend in the adoption of state-of-the-art surveying, inversion, and statistical techniques for multi-geophysical imaging and inversions. Both direct and indirect geophysical methods are used to assess the physical and geomechanical characteristics of subsurface geology at various spatial and temporal scales (Marquis and Hyndman \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Meju et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gallardo and Meju 2003, 2007, 2011; Bery and Saad \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Merritt 2014; Akingboye and Bery \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To achieve accurate predictions of distinctive geological features in geoscience, a range of detailed inversion models is essential.\u003c/p\u003e \u003cp\u003eIn the realm of geophysics, inversion models serve as invaluable tools for elucidating the spatial distribution of various physical attributes, such as electrical resistivity/conductivity, chargeability, seismic velocities, and other geophysical properties (Ronczka et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zeng et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shang and Hasan \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Akingboye et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Loke et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These attributes often stand as proxies for fundamental rock properties, including porosity, mineral composition, fracture density, and more (Mu\u0026ntilde;oz et al. 2010). It's worth noting that these physical attributes are underpinned by intricate combinations of variables, rendering their interpretations far from straightforward. This complexity arises from the need to integrate diverse geophysical datasets stemming from various physical phenomena, each exhibiting different spatial scales of measurement, while also accounting for the inherent complexities and uncertainties in geological models (Gallardo and Meju 2007, 2011).\u003c/p\u003e \u003cp\u003eHence, the selection of a geophysical method becomes a critical decision when aiming to enhance the signal-to-noise ratio and refine our understanding of subsurface geological conditions within projects related to crustal exploration, infrastructure development, environmental assessments, subsurface simulations, and the like. This strategic choice not only accelerates project timelines and minimizes costs but also optimizes the overall success of project outcomes. As a result, seismic and electrical methods have emerged as the go-to geophysical techniques for assessing near-surface crustal projects.\u003c/p\u003e \u003cp\u003eExploration of the Earth's subsurface has been a foundational endeavor in the realm of geophysics, with profound implications for various fields such as resource exploration, environmental management, and civil engineering. Accurate and detailed subsurface characterization is essential for understanding the geological and hydrogeological aspects of a given region. The archipelago of Langkawi, nestled in the Kedah state of Malaysia, provides a compelling backdrop for the development of novel resistivity-chargeability statistical relationships.\u003c/p\u003e \u003cp\u003eCharacterization is an essential aspect of geotechnical and environmental investigations. Electrical resistivity tomography (ERT) and induced polarization (IP) techniques are commonly used geophysical methods for subsurface characterization. ERT measures the electrical resistivity of the subsurface, while IP measures the chargeability of the subsurface. Both techniques provide information about the subsurface's lithology, porosity, and fluid content.\u003c/p\u003e \u003cp\u003eThe statistical relationship between electrical resistivity and chargeability has been studied in various geological settings. The relationship between these two parameters is essential for subsurface characterization, especially in areas with complex geology. The development of novel resistivity-chargeability statistical relationships can improve the accuracy of subsurface characterization.\u003c/p\u003e \u003cp\u003eThis paper aims to present the development of novel resistivity-chargeability statistical relationships for subsurface characterization in Langkawi, Kedah Malaysia. Langkawi is a complex geological area with various lithologies, including granite, limestone, and sandstone. The study area's subsurface characterization is essential for groundwater exploration, environmental monitoring, and geotechnical investigations. The development of novel resistivity-chargeability statistical relationships for subsurface characterization in Langkawi can improve the accuracy of subsurface characterization and provide a better understanding of the area's geology. The paper will present the methodology used for data acquisition, processing, and analysis. The results of the study will be discussed, and the implications of the findings will be highlighted. The paper's contribution to the field of geophysics and subsurface characterization will also be discussed.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eAt two locations in Penang, Malaysia, Akingboye and Bery (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted research to assess the effectiveness of utilising induced polarisation (IP) and electrical resistivity tomography (ERT) in subsurface tomographic analyses of copper and traditional stainless-steel electrodes. According to the findings, The best convergence is found in ERT and IP tomographic models. The evaluation of the electrodes' performance in the two targeted sites revealed the mean absolute percentage error (MAPE). Adedibu and Bery (2023) explained the rise in popularity and significance of multi-geophysical imaging and inversions in the geosciences. The empirically harmonised velocity-resistivity connection that was derived in tropical granitic environments, statistically significant, according to the accuracy evaluation. Meanwhile, Hasan et al., 2020 conducted an integrated geophysical survey to assess the subsurface geological formation in a weathered terrain in South Huizhou, China. The survey involved the use of electrical resistivity tomography (ERT), induced polarization (IP), magnetic method, and joint profile method (JPM). The goal was to determine the suitability of the site for the construction of deep engineering structures and to identify groundwater resources. The results of the survey revealed four discrete layers in the subsurface: topsoil cover, highly weathered layer, partly weathered layer, and unweathered bedrock. Each layer had a specific range of resistivity values. The integration of ERT with IP, magnetic, and JPM allowed for the delineation of faults, fractures, and the identification of the weakest zones for engineering projects. The unweathered fresh bedrocks were identified as the most appropriate locations for the construction of deep structures. Additionally, the areas with low resistivity and low chargeability were identified as suitable places for groundwater occurrence.\u003c/p\u003e \u003cp\u003eSono et al. (2020) identified and characterized probable mineralization Zones containing gold in the Phistine Molopo area, Southeast Botswana, using 2D and pseudo-3D induced polarisation (IP). They were able to distinguish between different lithological units that includes mineralized and non-mineralized zones because of the chargeability and resistivity results combined. The reason behind the high value of both resistivity and chargeability anomalies is linked to the availability of Gold-Bearing Quartz and Carbonate veins that are linked to the Branded Iron Formation (BIF) units towards the east-west trends. History of the exploration shows that Gold Mineralization is linked to the Quartz and Carbonates veins that is across the branded formations. The Gold-Bearing Quartz and Carbonate veins are characterized by coarse-grained euhedral pyrite and quartz-adularia veins.\u003c/p\u003e \u003cp\u003eKang et al. (2023) measured took the measurement of both resistivity and chargeability at the same the face of the tunnel in the laboratory chamber during an excavation exercise. The value of the resistivity and chargeability readings that were linked with the TBM advancement was used to predict the mixed ground conditions during the tunnel excavation and construction. The pattern of the electrical resistivity during TBM advance was experimentally estimated under harsh conditions. Using the electrical resistivity and induced polarization methods will help in detecting very toxic grounds ahead of the TBM tunnel face pattern and fluctuations that were tested and recommended for perfectly predicting risky conditions were made. Due to their low noise and vibration levels and excellent safety in construction, tunnel boring machines (TBMs) are believe to be more convenient during excavation of tunnel in urban areas (cities) with hih population, complex and infrastructure that are very sensitive. The modelling of the advance tunnel was from H/M\u0026thinsp;=\u0026thinsp;1.2 to 0, where H/M\u0026thinsp;=\u0026thinsp;0 denotes that the TBM model has reached the top of the fault zone. The normalized distance (H/M) between the TBM model and the fault zone was determined by dividing the actual distance (H) by the width of the TBM model (M).\u003c/p\u003e \u003cp\u003eKyle Robinson et al. (2022) conducted a study that showcases the benefits of integrating combined geoelectrical imaging techniques, most especially 3D high-resolution Direct Current (DC) resistivity and induced polarization (IP), to enhance the interpretation of groundwater-surface water interactions along a stream reach. The study focused on investigating the streambed architecture and its impact on groundwater-surface water interactions. The findings highlighted the untapped potential of utilizing dimensional continuous, high-resolution DC-IP data to effectively map streambed architecture and enhance the understanding the dynamics of groundwater-surface water interactions along a stream reach. The study contributes to the understanding of groundwater-surface water interactions and provides insights into the use of geoelectrical imaging for improved characterization of streambed architecture.\u003c/p\u003e"},{"header":"Geological Setting","content":"\u003cp\u003ePeninsular Malaysia forms a part of the Southeast Asian continental core within the broader region known as Sundaland. Geologically, it consists of two distinct blocks or landmasses: the Sibumasu Terrane in the western part and the Sukhothai Arc (also known as the East Malaya Block) in the east. These two landmasses came together and merged during the Late Triassic period, as described in studies by Metcalfe (2013a, 2013b) and Pour and Hashim (2015).\u003c/p\u003e \u003cp\u003ePeninsular Malaysia, situated on the southeastern margin of the Eurasian continent, took shape through a complex geological process involving the amalgamation of two distinct blocks: the Sibumasu Block and the Indochina Block. This geological transformation occurred along the Bentong-Raub Suture Zone (BRSZ) during the Permian-Triassic period, as supported by several studies (Metcalfe 2000, 2001, 2013a; Jasin 2013; Ng et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, 2015b; Cao et al. 2020).\u003c/p\u003e \u003cp\u003eLangkawi, part of the Western Belt of Peninsular Malaysia, is primarily composed of Precambrian metamorphic rocks and granite. The granite formations, resulting from slow cooling of magma, create the unique landscapes. The islands exhibit a mix of metamorphic rocks, including schists and gneisses, shaped by tectonic activity. Karst landscapes feature prominently, with limestone formations, caves, and towers formed through chemical weathering and erosion, as shown in the geology Map of Langkawi in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Fossilized marine life in limestone hints at the region's marine geological history. Quaternary sediments, such as alluvium, contribute to the coastal areas. Tectonic activity over time has led to rock uplift and diverse landforms. The shoreline showcases sandy beaches influenced by ongoing geological processes. Local geological studies provide more specific details about rock types, structures, and chronological events in Langkawi.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe methodologies employed for this study are in phases and the survey was carried out on a construction site in Kuah, Langkawi kedah as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.(a, b, c, d and f).The details of each research phase are encapsulated in the methodological flowchart depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the procedural steps specific to the Resistivity-Chargeability of the Simple Linear Regression (SLR) methodological flowchart. In order to make this research cost-effective and high-performing, the first phase entails the selection of suitable sites on Langkawi Island, with borehole data available in the locations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe electrical resistivity tomography (ERT) surveys were carried out using the Lund Resistivity Meter, employing the Schlumberger-Wenner array, following the techniques of Akingboye and Bery (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ERT survey was performed along three profiles namely L1, L2 and L3 using a fixed electrode spacing of 5 m, a total of 522 datum points for resistivity and IP and a total profile length of 200 m for the three lines. The total number of electrodes and data level is 42 and 23 respectively. The drilled boreholes names BH1 and BH2 are at a station distance of 90 m along L1 and 65 m and 135 m along L3 respectively.\u003c/p\u003e \u003cp\u003eThe RES2DINV software were used to create the final underground resistivity and chargeability models for the study area. These software tools were thoroughly explained in previous works by Akingboye and Bery in 2021 and 2022. To improve the accuracy of the models at L1 and L3, we incorporated the information obtained from the borehole litho logs (BH1\u0026ndash;BH2).\u003c/p\u003e \u003cp\u003eTo establish the empirical relationships between both parameters and accurately predict the chargeability in the targeted terrain, we first filtered the extracted resistivity and chargeability values to eliminate any outliers. The resulting dataset, consisting of 125 filtered collocated models' pixels, was then used for regression analysis. In this analysis, chargeability was considered the dependent variable, while resistivity served as the independent variable. To ensure the statistical integrity of the regression model, we employed the IBM Statistical 27 software to conduct various tests, including ANOVA, multivariate normality, multicollinearity (using tolerance and variance inflation factor, VIF), Durbin-Watson (D-W), and homoscedasticity tests.\u003c/p\u003e \u003cp\u003eFurthermore, we assessed the accuracy of the derived resistivity-chargeability empirical relationship by comparing the actual and predicted chargeability models. This evaluation involved calculating the root-mean-square error (RMSE) and mean absolute percentage error (MAPE) using Equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively. For the model to be considered statistically accurate, both the RMSE and MAPE must be below 10%.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$RMSE=\\sqrt{\\frac{{\\sum }_{i=1}^{N}{\\left(O{C}_{i}-P{C}_{i}\\right)}^{2}}{N}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$MAPE=\\frac{100\\%}{N} {\\sum }_{i=1}^{N}\\left|\\frac{O{C}_{i}-P{C}_{i}}{O{C}_{i}}\\right|$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere OC and PC are the observed chargeability and predicted chargeability values for the study area.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eERT field data processing and modelling\u003c/h2\u003e \u003cp\u003eThe comprehensive workflow depicting the data processing and inversion steps for the Electrical Resistivity Tomography (ERT) and field datasets is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. To begin, the resistivity data, along with topographical information, acquired from all the three (3) lines underwent a series of processing and inversion steps. These procedures were executed using the RES2DINV software, a well-established tool in geophysical research (Loke and Barker \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Loke \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The standard least-squares inversion methodology, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, was followed meticulously throughout the analysis. In brief, the finite element method employing four nodes and L2-norm was employed as the standard least-squares constraint parameters to minimize the disparity between the measured and computed apparent resistivity values. To enhance the precision of the calculated and observed apparent resistivities, a damping factor of 0.05, with a minimum value set at 0.01, was applied.\u003c/p\u003e \u003cp\u003eThe root mean square error (RMSE) of the inverse models consistently reached convergence below 10% within a maximum of five iterations, without the exclusion of any ERT data points. However, to enhance the accuracy of the models, the RMSE statistics cutoff approach was adopted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eChargeability-Resistivity regressional analysis and modeling\u003c/h2\u003e \u003cp\u003eRegression analysis offers numerous advantages in statistical investigations and modeling, serving various purposes such as forecasting, time series modeling, and gaining insights into the relationships between variables. Simple Linear Regression (SLR) is a straightforward and efficient technique, although its application to real-time nonlinear data can be challenging. A well-fitted linear model should ideally exhibit randomly distributed residuals when viewed on a scatterplot, and its histogram should closely resemble a normal distribution (Carroll and Green 1997; Gonz\u0026aacute;lez et al. 2019).\u003c/p\u003e \u003cp\u003eIn this study, we conducted a regression analysis on a dataset comprising ρ (resistivity) and chargeability values extracted from collocated model pixels (N\u0026thinsp;=\u0026thinsp;125). These values were employed as dependent and independent variables, respectively, in SLR plots using Microsoft Excel. The objective was to establish empirical relationships between chargeability and resistivity in granitic environments. Outliers were identified and removed from the datasets to ensure a good fit for the SLR models. This is clearly shown in the Simple Linear Regression (SLR) of Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we derived the logarithm of the collocated resistivity (ρ) and chargeability \u003cem\u003em\u003c/em\u003e data to explore which regression models would yield higher predictive accuracy.\u003c/p\u003e \u003cp\u003eIt's important to note that resistivity data (the independent variable) were used to predict chargeability data in this study. This approach was chosen due to the specific challenges associated with Self-Potential (SRT) data in crystalline basement terrain, especially when the overburden is less than 50 meters. Such challenges may include hidden/blind zones and difficulties in accurately determining soil-rock constituents and thicknesses (Lin et al. 2015).\u003c/p\u003e \u003cp\u003eFor the evaluation of the SLR models, we utilized IBM SPSS (v. 27) software to assess their accuracy based on the 125 extracted collocated resistivity and chargeability data. The regressed variables provided valuable insights into critical parameters, such as linear correlation (R), coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), unstandardized (B), and standardized (β) coefficients, along with p values. Various statistical tests, including analysis of variance (ANOVA), assessments of multivariate normality, evaluations of multicollinearity (i.e., tolerance and variance inflation factor, VIF), Durbin\u0026ndash;Watson (D\u0026ndash;W) tests, and examinations of homoscedasticity, were conducted to ascertain the accuracy and reliability of the SLR models (Carroll and Green 1997; Salkind 2007; Akingboye and Bery \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The resulting histogram plot, normal P\u0026ndash;P plot (for stepwise SLR), and scatterplot of regression normalized residuals were employed to validate the outcomes of these regression analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussions","content":"\u003cp\u003eWithin the study area, a variety of subsurface lithologic layers were identified beneath the surveyed sites, encompassing a spectrum of materials from silt to dry sand in the uppermost weathered and fractured layers, down to the pristine bedrock. These lithologic units were discerned through both borehole logs and field mapping. For Line 1, the subsurface lithologic units extracted from borehole data from BH1 and BH2 at 65 m and 135 m and BH 1 at 90 m on Line 3 consisted of sandy silt (topsoil), silty-sand with some gravels and sandy-silt with gravel \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cstrong\u003eand\u003c/strong\u003e Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e respectively). These units exhibited varying resistivity values, with the sandy silt registering resistivity values of less than 0.5\u0026ndash;1000 Ωm.\u003c/p\u003e\n\u003cp\u003eThe integration of Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) methods, combined with borehole data, has provided valuable insights into the subsurface characteristics of the study area in Kuah, Langkawi Island, Malaysia. This region is known for its complex geological history, featuring a sedimentary rock formation that has been intruded by granitic rock. In this discussion, we will delve into the key findings and interpretations of the ERT-IP models along three research lines, with a focus on lithological variations, resistivity values, and chargeability measurements.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eLithological Variations\u003c/h2\u003e\n \u003cp\u003eThe ERT-IP models have revealed distinct lithological units along the research lines. These units include sandy-silt, silty-sand with some gravel, and sandy-silt with some boulders. These variations in lithology are indicative of the heterogeneity and complexity of the subsurface geology in the study area. Understanding these lithological variations is crucial for various geotechnical and hydrogeological applications, as they can significantly influence the movement of groundwater, the stability of foundations, and the distribution of resources.\u003c/p\u003e\n \u003cp\u003eOne of the noteworthy outcomes of this study is the wide range of resistivity values observed across the research lines, spanning from 0.5 ohm-m to 1000 ohm-m. These values reflect the electrical properties of the subsurface materials and are instrumental in distinguishing between different lithological units. The sandy-silt layers typically exhibit lower resistivity values (\u0026lt;\u0026thinsp;20\u0026ndash;40 ohm-m), indicating higher moisture content and greater electrical conductivity. The presence of resistivity values ranging up to 1000 ohm-m in the study area is consistent with the intrusion of granitic rock, known for its higher resistivity compared to sedimentary formations. The granitic intrusions are likely responsible for the localized spikes in resistivity observed in certain areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eChargeability Measurements\u003c/h2\u003e\n \u003cp\u003eThe chargeability measurements, ranging from 1 ms to 50 ms, provide insights into the polarization characteristics of the subsurface materials. Higher chargeability values often correspond to the presence of metallic or conductive minerals, which can be indicative of geological features such as sulfide mineralization or altered zones. In contrast, lower chargeability values may signify the absence of such minerals or the predominance of non-conductive materials.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eInterpretation and Geological Significance\u003c/h2\u003e\n \u003cp\u003eThe observed lithological variations, resistivity values, and chargeability measurements are consistent with the complex geological history of the study area. The sandy-silt layers are likely associated with sedimentary deposits, while the silty-sand with gravel and sandy-silt with boulders layers may indicate a mixture of sedimentary and granitic materials. The higher resistivity values in certain areas are suggestive of granitic intrusions, which are known to exhibit lower conductivity. The chargeability measurements can aid in identifying potential mineralization zones or altered rock units within the subsurface, which may have economic significance for mineral exploration.\u003c/p\u003e\n \u003cp\u003eIn conclusion, the ERT-IP models, in conjunction with borehole data, have provided valuable information about the subsurface composition and characteristics of the study area in Kuah, Langkawi Island, Malaysia. These findings are essential for geological and geotechnical assessments and may have implications for various applications, including groundwater management, engineering design, and mineral exploration in this geologically diverse region. Further studies and detailed geological investigations are warranted to refine our understanding of the complex sedimentary and granitic rock interplay within this area.\u003c/p\u003e\n \u003cp\u003eThe subsurface lithologic units below Line 1, including sandy silt (topsoil), sand, and silty sand, exhibit resistivity values within the ranges of approximately\u0026thinsp;\u0026lt;\u0026thinsp;100\u0026ndash;400 Ωm, 600\u0026ndash;800 Ωm, and 30\u0026ndash;50 Ωm, respectively. Line 1 displays chargeability models characterized by distinct layers, encompassing residual soils (silty sand), weathered or weathered-fractured rock units, integral or hard granite, and fresh granitic bedrock, each with corresponding chargeability values of 39\u0026ndash;50 ms, 14\u0026ndash;20 ms, and 1\u0026ndash;14 ms, respectively. Conversely, the resistivity models for Line 3 reflect a diverse range of resistive subsurface properties, mirroring their chargeability models and borehole logs. Elevated resistivity values signify the presence of bedrock near the surface, as well as the existence of stiff-to-hard silty sand and sand layers. Notably, the topmost silty sand exhibits resistivity values ranging from about 100 to \u0026gt;\u0026thinsp;1000 Ωm. Line 1, in particular, identifies the dry sand as the highly resistive entity within the models.\u003c/p\u003e\n \u003cp\u003eFrom this collocated inversion approach, we extracted a substantial dataset comprising 125 paired resistivity and chargeability data points, originating from coincident and closely situated model pixels. These data points were then subject to graphical regression analysis, the results of which are presented here. Through this process, we\u0026apos;ve enhanced our understanding of the variations in chargeability and \u0026rho; at specific station positions and depths.\u003c/p\u003e\n \u003cp\u003eFurthermore, the outcomes of this analysis shed light on the typical range of resistivity and chargeability values associated with the lithologic units we\u0026apos;ve delineated within the study area. This information extends to cover not only our immediate research site but also sheds light on analogous tropical granitic terrains with relatively shallow overburden, broadening the applicability of our findings.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSubsurface lithologic characterization and chargeability\u0026ndash;resistivity statistical modeling\u003c/h2\u003e\n \u003cp\u003eTo gain deeper insights into the characteristic range of lithologic units below our study area, we combined and co-located the resistivity and chargeability models obtained from Lines 1, 2, and 3. This comprehensive approach allowed us to establish correlations and unveil the intricate relationships between resistivity and chargeability within the geological context. Within this integrated framework, we present a representative model that encapsulates the combined data for Lines 1, 2, and 3. In total, we extracted 125 collocated resistivity and chargeability data points, strategically chosen to coincide with and closely align to model pixels. These data points were then subjected to graphical regression analysis, revealing a nuanced perspective of the resistivity and chargeability models within the same station positions and depths.\u003c/p\u003e\n \u003cp\u003eThis analytical process serves to elucidate the inherent variability in resistivity and chargeability, ultimately shedding light on the typical range of values characterizing the delineated lithologic units within the study area. Moreover, these findings provide valuable insights into the distinctive characteristics of geological formations in tropical sedimentary terrains, particularly those with shallow overburden. Owing to the intricate nature of subsurface lithologic units in tropical regions, characterized by their complexity, rugged topography, irregular distribution, and susceptibility to weathering and fracturing, our investigation necessitated the formulation of a unified empirical relationship connecting resistivity and chargeability. This pivotal empirical relation was initially derived from the Statistical Linear Regression (SLR) plot, as detailed in Eq. 3. The established unified resistivity-chargeability relationship was rigorously validated by employing the Semi-Logarithmic Resitivity (SLR) data, where the dependent chargeability was regressed against independent resistivity data. This validation process involved comprehensive statistical analyses, encompassing essential correlation tests and an analysis of variance (ANOVA), as outlined in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe SLR analysis revealed a notably robust positive correlation between chargeability and resistivity, with an impressive R value of 0.953. This strong relationship is further substantiated by an R-squared (R\u0026sup2;) value of 0.87, indicating a prediction accuracy of approximately 87%. The precision of this prediction is bolstered by the remarkably low standard error of 4.5688. Furthermore, our analysis suggests that for each predicted chargeability value inferred from resistivity (\u0026rho;), there is an associated unstandardized (B) value of 0.953, with a constant term of 168.764. Notably, the Durbin-Watson (D-W) statistic of 1.822, which is less than 2, implies an absence of autocorrelation between the two parameters.\u003c/p\u003e\n \u003cp\u003eThe statistical results presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e are highly compelling. The p-value, which is less than 0.0001%, and the substantial F-statistics of 841.35 collectively demonstrate the excellent fit of the variables within the regressed model. This underscores the robustness and significance of our findings, aligning with prior research (Gonz\u0026aacute;lez et al. 2019; Akingboye and Bery \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Balarabe et al. 2022).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"1268\" height=\"840\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eBy employing the unified empirical relationship between chargeability and resistivity, as described in Eq. 3, along with its lower and upper empirical bounds as given by Equations \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, it becomes possible to assess the extent of variability and the potential range of predicted chargeability values within the study area. This approach enhances the robustness and accuracy of the results obtained.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0512 \u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;+\u0026thinsp;9.7046 (3)\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003eIP is the induced polarization or chargeabilty and \u0026rho; is the resistivity.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe 95% lower and upper confidence intervals for B are related as:\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$${IP}_{LB,UB}= \\rho +C$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eTherefore, the equations that define the 95% lower and upper confidence intervals for B, utilizing the derived constant C, are provided as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$${IP}_{LB}= 0.048 \\rho +8.385$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$${IP}_{UB}= 0.0055 \\rho +11.024$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{LB}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{UB}\\)\u003c/span\u003e\u003c/span\u003e represent the respective lower and upper bounds of the predicted chargeability based on the unified chargeability-resistivity empirical relationship.\u003c/p\u003e\n \u003cp\u003eMoreover, the histogram trend and the linearity observed in the standardized residuals of the regressed data (refer to Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea, b) strongly support the significant accuracy of the Simple Linear Regression (SLR) model. The collinearity statistics of the regression model, indicated by tolerance and VIF values both at 1.0, further indicate the absence of multicollinearity. Additionally, the scatterplot (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ec) displays no clustering or systematic patterns among the analyzed variables, meeting the assumption of homoscedasticity as per Tabachnick and Fidell (2019) and Gonz\u0026aacute;lez et al. (2019). Consequently, the derived unified velocity\u0026ndash;resistivity empirical relationship demonstrates statistical significance in tropical sedimentary region with granitic intrusion environments, as affirmed by the accuracy assessment. Consequently, based on this comprehensive accuracy assessment, it is evident that the derived unified chargeability-resistivity empirical relationship holds significant statistical merit, particularly in the context of tropical sedimentary region with granitic intrusion environments.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we have endeavored to construct statistically sound relationships between chargeability and resistivity through a comprehensive approach that integrates collocated chargeability-resistivity tomographic modeling and Semi-Logarithmic Resistivity (SLR) analysis. Our investigation was conducted in the unique geological context of tropical sedimentary environments with granitic intrusions on Langkawi Island, Malaysia. These sites exhibit diverse subsurface crustal structures, resulting in varying resistivity values along the three Electrical Resistivity Tomography (ERT) lines, particularly in regions with high-resistivity materials. Through a meticulously designed methodology that includes simultaneous chargeability-resistivity inversion and SLR modeling workflows, we have successfully established unified and specific empirical relationships between chargeability and resistivity. These relationships are tailored to address the distinctive characteristics of residual soils, weathered or weathered-fractured granitic formations, and intact or fresh bedrock. The derived chargeability-resistivity empirical relations have demonstrated a noteworthy practical prediction accuracy exceeding 85%. These relations also exhibit robust positive correlations, meeting all the criteria essential for accurate SLR models. Consequently, we can confidently assert that the chargeability-resistivity empirical relationships we have developed hold substantial statistical significance within the geological context of Langkawi Island.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe authors would like to express gratitude and appreciation to individuals who have contributed to the completion of this paper. Special appreciation to the Editor and the reviewers for their valuable comments and recommendations, which have greatly contributed to improving the quality of our paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u0026nbsp;\u003c/strong\u003eThe writers can provide data upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eGabriel Abraham Bala: Conceptualisation, writing-original draft preparation, investigation, and methodology.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAndy Anderson Bery: Supervision, Resources, Writing-review \u0026amp; editing, and Validation.\u003c/li\u003e\n \u003cli\u003eJoseph Gnapragasan: Editing, and Validation.\u003c/li\u003e\n \u003cli\u003eAdedibu Sunny Akingboye: Validation, and Resources.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors express their gratitude to the Malaysian Ministry of Higher Education (MoHE) for providing financial support via the Fundamental Research Grant Scheme (203/PFIZIK/6712108), as well as to Universiti Sains Malaysia for the Short-Term Grant (304/PFIZIK/6315489), which have enabled the funding of this research endeavours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eAll ethical standards have been followed during this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eAll Authors and corresponding authors have given consent to participate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eAll Authors and corresponding authors have given consent to participate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/competing interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003eI want to clearly state that, this manuscript has not been submitted to any preprint server prior to submission to ESPR.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkingboye, A. S., and Bery, A. A. (2021). Evaluation of lithostratigraphic units on groundwater potential using the resolution capacities of two electrical different tomographic electrodes at dual spacing. \u003cem\u003eContributions to Geophysics and Geodesy\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(4), 295\u0026ndash;320.\u003c/li\u003e\n\u003cli\u003eAkingboye, A. S., and Bery, A. A. (2022). Characteristics and rippability condition of near-surface strata (Batu Maung, Penang Island, Malaysia) derived from borehole-constrained geotomographic models and geostatistical analyses. \u003cem\u003eJournal of Applied Geophysics\u003c/em\u003e, 204, 104723.\u003c/li\u003e\n\u003cli\u003eAkingboye, A. S., and Ogunyele, A. C. (2019). Insight into seismic refraction and electrical resistivity tomography techniques in subsurface investigations. \u003cem\u003eRudarsko Geolosko Naftni Zbornik\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 93\u0026ndash;111. https://doi.org/10.17794/rgn.2019.1.9\u003c/li\u003e\n\u003cli\u003eAkingboye A.S, Bery A.A, Kayode J.S, Asulewon A.M, Bello R, Agbasi O.E (2022), Near-surface crustal architecture and geohydrodynamicsof the crystalline basement terrain of Araromi, Akungba-Akoko, SW Nigeria, derived from multi-geophysical methods. Natural Resources Research 31:215\u0026ndash;236. https://doi. org/ 10. 1007/s11053- 021- 10000-z \u003c/li\u003e\n\u003cli\u003eBery A.A, Saad R (2012). Correlation of seismic p-wave velocities with engineering parameters (N value and rock quality) for tropical environmental study. International Journal of Geosciences 3:749\u0026ndash;757. https://doi.org/ 10. 4236/ ijg. 2012. 34075 \u003c/li\u003e\n\u003cli\u003eBinley A., Kemna A (2005). 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Assessment of engineering behaviour of an intensely weathered swelling mudstone under full range of seasonal variation and the relationships among measured parameters. Can Geotech J 55:1837\u0026ndash;1849. https://doi.org/10.1139/cgj- 2017- 0582\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Resistivity , Chargeability , Regression Analysis, Empirical Relationship","lastPublishedDoi":"10.21203/rs.3.rs-3588974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3588974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"Development of novel resistivity-chargeability statistical relationships for subsurface characterization at Langkawi, Kedah.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 08:40:22","doi":"10.21203/rs.3.rs-3588974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-29T17:32:21+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-21T05:01:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2023-12-13T23:32:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ed9af8dc-0540-43e6-8af5-6b4ee247853b","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-03-07T23:59:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 08:40:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3588974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3588974","identity":"rs-3588974","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}