This article aims to aid in exploring and forecasting hydrothermal minerals and gold deposits in Central Saudi Arabia (SA), with a focus on structural contexts. Remote sensing (RS) and satellite-based Bouguer gravity (SBG) data were integrated in order to create a mineral prediction map for the researched location. Data from the Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) were transformed and enhanced using a variety of approaches. The delineation of hydrothermal alteration zones (HAZs) and highlighting of structural discontinuities in the OLI data were made possible using band ratios and oriented principal component analysis (PCA). Additionally, the underlying structural features were successfully exposed by processing the SBG using a variety of edge detection techniques, like the analytical signal (AS), total horizontal derivative (THD), tilt angle (TA), horizontal tilt angle (TDX), theta map (TM), horizontal derivative of the tilt derivative (HD_TDR), horizontal gradient of the tilt angle (HGTA), tilt angle of the analytical signal (TAAS), and soft sign function (SF). As a result, more prominent lineaments were found in the NW–SE, NNW–SSE, NE–SW, and NNE–SSW directions than in the N–S and E–W directions. The GIS incorporated surface/subsurface geological structure density maps with zones of hydrothermal alteration. It was found that the lineaments derived from the analysis of the RS and SBG data were more in line with the HAZs, which demonstrated the common connection between alteration zones and deep lineaments. The findings revealed a mineral prediction map with extremely low to extremely high probabilities. Overall, combining RS and SBG data effectively identified probable mineralization sites associated with hydrothermal processes and made it easier to create this study’s final predictive mineralization map.

The present research’s main objective was to apply thorough exploration approaches that combine remote sensing data with geochemical sampling and analysis to predict and identify potential chromitite locations in a complex geological site, particularly in rugged mountainous terrain, and differentiate the ultramafic massif containing chromitite orebodies from other lithologies. The ultramafic massif forming the mantle section of the Kırdağ ophiolite, located within the Erzurum–Kars Ophiolite Zone and emerging in the east of Oltu district (Erzurum, NE Turkey), was selected as the study area. Optimum index factor (OIF), false-color composite (FCC), decorrelation stretch (DS), band rationing (BR), minimum noise fraction (MNF), and principal and independent component analyses (PCA-ICA) were performed to differentiate the lithological features and identify the chromitite host formations. The petrography, mineral chemistry, and whole-rock geochemical properties of the harzburgites, which are the host rocks of chromitites in the research area, were evaluated to verify and confirm the remote sensing results. In addition, detailed petrographic properties of the pyroxenite and chromitite samples are presented. The results support the existence of potential chromitite formations in the mantle section of the Kırdağ ophiolite. Our remote sensing results also demonstrate the successful detection of the spectral anomalies of this ultramafic massif. The mineral and whole-rock geochemical features provide clear evidence of petrological processes, such as partial melting and melt–peridotite interactions during the harzburgite formation. The chromian spinels’ Cr#, Mg#, Fe³⁺, Al₂O₃, and TiO₂ concentrations indicate that the harzburgite formed in a fore-arc environment. The Al₂O₃ content and Mg# of the pyroxenes and the whole-rock Al₂O₃/MgO ratio and V contents of the harzburgite are also compatible with these processes. Consequently, the combined approaches demonstrated clear advantages over conventional chromitite exploration techniques, decreasing the overall costs and supporting the occurrence of chromite production at the site.

Within electrical resistivity tomography (ERT), the selection of arrays and electrode locations can effectively enhance the resolution of imaging. By properly selecting and optimizing the survey design, a better resolution will be obtained with fewer electrodes and configurations than traditional arrays. Previous work has demonstrated that the optimized survey design using the ‘Compare R’ method can provide better resolution than conventional arrays. This paper adds target-oriented selection and modified the original ‘Compare R’ method. The modified method first selects the target area in the comprehensive data sets, then optimizes the choice by the modified CR method, and finally combines the optimization results of multiple sets of target areas. For the target area, this method can select fewer electrodes and arrays than the original CR method, get better resolution than conventional arrays, and take less calculation time.

In recent decades, geoelectrical methods have played a very important role in near-surface investigation. The most widely used of these methods is electrical resistivity tomography (ERT). Regardless of the forward and inversion algorithms used, the original data collected from a survey is the most important factor for quality of the resulted model. However, 3D electrical resistivity survey design continues to be based on data sets recorded using one or more of the standard electrode arrays. There is a recognized need for the 3D survey design to get better resolution using fewer data. Choosing suitable data from the comprehensive data set is a great approach. By reasonable selecting, better resolution can be obtained with fewer electrodes and measurements than conventional arrays. Previous research has demonstrated that the optimized survey design using the 'Compare R' method can give a nice performance.This paper adds target-oriented selection and modified the original 'Compare R' method. The survey design should be focused on specific target areas, which need a priori information about the subsurface properties. We select electrodes and configurations as the target set by the comprehensive set firstly which meets the requirements of the target area. The number of measurements and electrodes is much less than the comprehensive set and the model resolution matrix takes less time to calculate. At the next step for rank, we calculate the sensitivity matrix of the target set only once and then calculate the contribution degree of each measurement separately from it. The time of iterative calculation of the resolution matrix when measurements set changing is less than the original method.The traditional method of evaluating RMS is not appropriate for comparing the quality of collected data by different survey designs. SSIM (structural similarity index) gives more reliable measures of image similarity better than the RMS. The curves of SSIM values in three dimensions and the average SSIM are given as quantitative comparisons. Besides, the frequency of electrodes utilized given to guides on selecting the highest used electrodes. Finally, the curves of the average relative resolution S and the number of electrodes as the number of measurements increase are given, which proves the method works effectively.The results show the significance of using target-oriented optimized survey design, as it selects fewer electrodes and arrays than the original CR method. Also, it produces better resolution than conventional arrays and takes less calculation time. 3D SSIM, frequency of electrodes used, the relationship between average relative resolution, number of electrodes and number of measurements, these quantitative comparison methods can effectively evaluate the data collected in various survey designs.