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Understanding the interaction between microbes and soil nutrients during fertilization is crucial for improving plant fruit quality. However, the impact of soil mineral elements, and their interactions with microbial communities on plant performance remain unclear. In this study, we combined fruit and soil mineral analyses with microbial community resistance assessments in an 8-year watermelon continuous cropping system to investigate the microbiome-mediated plant responses to organic and bio-organic fertilizations. Our results showed that bio-organic fertilizer (BOF) treatment significantly enhanced watermelon quality, with a quality index 1.62 and 9.29 times higher than organic fertilizer (OF) and the control (CK), respectively. BOF improved soil mineral levels, particularly soil available iron (AFe), which was 1.77 and 4.01 times greater than OF and CK, and leaf iron content, which was 2.10 and 11.49 times higher than OF and CK. BOF also improved the soil microbial resistance and microbial community stability along with a promotion of symbiotic components within soil microbiomes and led to a stable microbial community, which supported enhanced soil nutrient cycling and plant health. Additionally, BOF-associated microbial clusters strongly linked with AFe and watermelon quality index. Stable mineral-solubilizing bacteria like Ammoniphilus , Bacillus , Acidibacter , and Talaromyces were enriched by BOF-treatment, which may have contributed to the dissolution of soil minerals (esp., AFe) and watermelon quality. Overall, our findings revealed a significant role of bio-organic fertilizers in improving soil minerals and crop quality through modulating key soil microbial clusters (e.g., stability and symbiont abundances).

The Gabal El-Faraid Group, in the Egyptian Southeastern Desert, comprises Neoproterozoic lithologies and structurally controlled mineralization zones that require an integrated exploration approach. This study integrates multi-source remote sensing data with detailed field observations, structural analysis, and petrographic investigations to improve lithological differentiation and delineate mineralization-related structures. Results indicate a potential structural-control spatial association among shear zones, hydrothermal alteration zones, and mineralization, suggesting their potential role in controlling mineralization. Support Vector Machine (SVM) and Random Forest (RF) algorithms have been used to best delineate lithological rock units of the study area. Accurate lithological mapping was achieved using an SVM applied to combined Sentinel-2 and ALOS PALSAR datasets, yielding an overall accuracy of 92.67%, a Kappa coefficient of 0.8928, and an F1-score of 88.64%. Six spectral mineral indices, like argillic, clay, ferrous silicates, ferrugination, hydroxyl, and phyllic alterations, have been emphasized using the hyperspectral PRISMA dataset. Exposed rocks comprise metavolcanics, metagabbro-diorite, tonalite-granodiorite, monzogranite, syenogranite, pegmatite, and late basic dikes, representing a multiphase magmatic sequence spanning arc-related to post-collisional stages. Structural investigations indicate a polyphase deformation history comprising four major events (D1-D4). The earliest phase (D1) is characterized by penetrative foliation, stretching lineation, and low-angle thrusting associated with nappe transport during intra-oceanic arc accretion. D2 reflects large-scale folding and regional shortening related to arc-to-continent collision. The D3 phase records transpressional strike-slip shearing, reactivating earlier structures and facilitating magma ascent, whereas D4 is marked by brittle faulting and jointing linked to post-orogenic uplift and extensional tectonics. Lineament analysis from PALSAR data highlights dominant NW-SE and NE-SW trends that closely correspond to field-measured fabrics and shear zones. Integration of field and remote sensing datasets demonstrates that late-stage deformation (D3-D4) exerted strong structural control over pegmatite emplacement, quartz veining, and hydrothermal alteration, with a main set trending WNW to NW and a less prominent set with pronounced ENE and NNW trends, emphasizing the role of inherited Pan-African structures in localizing mineralization. High-priority prospective zones in the studied granites were derived from integrated datasets, which were systematically combined in ArcGIS using a fuzzy-logic overlay. The detected zones are labeled as very high, high, moderate, low, and very low based on their potential for rare-metal mineralization. The integrated datasets can be applied to similar areas in the Eastern Desert of Egypt and across the entire Arabian-Nubian Shield (ANS).

Fusion and analysis of thematic information layers using machine learning algorithms provide an important step toward achieving accurate mineral potential maps in the reconnaissance stage of mineral exploration. This study developed the Neuro-Fuzzy-AHP (NFAHP) technique for fusing remote sensing (i.e., ASTER alteration mineral image-maps) and geological datasets (i.e., lithological map, geochronological map, structural map, and geochemical map) to identify high potential zones of volcanic massive sulfide (VMS) copper mineralization in the Sahlabad mining area, east Iran. Argillic, phyllic, propylitic and gossan alteration zones were identified in the study area using band ratio and Selective Principal Components Analysis (SPCA) methods implemented to ASTER VNIR and SWIR bands. For each of the copper deposits, old mines and mineralization indices in the study area, information related to exploration factors such as ore mineralization, host-rock lithology, alterations, geochronological, geochemistry, and distance from high intensity lineament factor communities were investigated. Subsequently, the predictive power of these factors in identifying copper occurrences was evaluated using Back Propagation Neural Network (BPNN) technique. The BPNN results demonstrated that using the exploration factors, copper mineralizations in Sahlabad mining area could be identified with high accuracy. Lastly, using the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) method, information layers were weighted and fused. As a result, a potential map of copper mineralization was generated, which pinpointed several high potential zones in the study area. For verification of the results, the documented copper deposits, old mines, and mineralization indices in the study area were plotted on the potential map, which is particularly appearing in high favorability parts of the potential map. In conclusion, the Neuro-Fuzzy-AHP (NFAHP) technique shows great reliability for copper exploration in the Sahlabad mining area, and it can be extrapolated to other metallogenic provinces in Iran and other regions for the reconnaissance stage of mineral exploration.

In various geosciences branches, including mineral exploration, geometallurgical characterization on established mining operations, and remote sensing, the regionalized input variables are spatially well sampled across the domain of interest, limiting the scope of spatial uncertainty quantification procedures. In turn, response outcomes such as the mineral potential, mining throughput, metallurgical recovery, or in situ estimations from remote satellite imagery are usually modeled from a highly restricted subset of testing samples, collected at certain locations due to accessibility restrictions and high acquisition costs. Our limited understanding of these functions, in terms of the multidimensional complexity of causalities and hidden dependencies on inaccessible inputs, may lead to observing changes in such functions based on their geographical location. Pooling different response functions across a domain is critical to correctly predicting outcome responses, the uncertainty associated with these inferred values, and the significance of inputs in such predictions in under-explored areas. This paper introduces the notion of a dual random field (dRF), where the response function itself is modeled as a regionalized variable. In this way, different established response models across the geographical domain are considered observations of a dRF realization, enabling the spatial inference and uncertainty assessment of both response models and their predictions. We explain how dRFs inherit all the properties from classical random fields, allowing the use of standard Gaussian simulation procedures to simulate them. Additionally, the application of dRFs is demonstrated through a mineral potential mapping case study on which different local binary response models are calibrated on the domain using support vector classification. These models are combined to obtain a mineral potential response, providing an example of how to rigorously integrate machine learning approaches with geostatistics.

This study tested and compared the mineral potential mapping capabilities of the random forest (RF) and maximum entropy (MaxEnt) algorithms using gold deposit occurrences within the Hezuo–Meiwu district, West Qinling Orogen, China. Eighteen orogenic gold deposits in this district and associated regional exploration datasets were used to construct data-driven predictive models to identify locations prospective for gold mineralization. The 18 orogenic gold deposits used in the modeling can be divided into magmatic-hydrothermal gold deposits and mesothermal gold deposits in terms of metallogenic characteristics and nine evidential maps associated with Au deposit occurrences (i.e., distance to intrusions and faults; Au, As, Ag, Cu, and Sb singularity indices; and principal component scores (PC1 and PC2) based on isometric logratio-transformed geochemical data were selected as inputs to the models). The PC1 represents a primary geochemical signature of tectonic process or their products (i.e., fault system), whereas PC2 represents a secondary geochemical signature. Both RF and MaxEnt models were then used to quantitatively rank the importance and identify the sensitivity of the evidential maps based on their spatial relationships to the known gold deposits in the study area. The two groups of populations in the response curves and marginal effect curves indicate that the mineral potential mapping should be performed by zones in consideration of different metallogenic characteristics of gold deposits. The accuracy of the resulting models was then assessed, and the results of the mineral potential mapping were examined using receiver operating characteristic (ROC) analysis, capture-efficiency curve, and success rate curve. Both mineral potential mapping by zones with RF and MaxEnt models have higher area under the ROC curve (AUC) values than the models performed in the study area and delineate 19% of the study area containing > 88% of the known deposit occurrences. Finally, according to the concentration–area (C-A) thresholds for prospectivity maps, two ternary prospectivity maps were generated for further mineral exploration. The results indicate that the RF and MaxEnt algorithms can be used effectively for mineral potential mapping and represent machine learning algorithms that can be used in areas with a few known mineral occurrences.

With the increasing use of machine learning for big data analytics, several methods have been implemented for the purpose of exploration targeting using mineral potential mapping in a GIS environment. Random forests (RF) have been successfully applied to data-driven mineral potential mapping using relatively small numbers of input maps that have typically been pre-classified by a geologist familiar with the mineral system being targeted. However, it is useful to understand how well RF perform for mineral potential mapping when a large number of multi-class categorical or non-thresholded numeric input maps are used in the classification or when weighted or ranked training data are used. Four different implementations of RF are presented to examine how the results vary depending on the degree of intervention from an expert in the modeling process. A case study has been devised using data from the eastern Lachlan Orogen in New South Wales (Australia) for the purposes of targeting porphyry Cu–Au mineralization related to the Macquarie Arc. The results demonstrate that the use of a large number of multi-class categorical or non-thresholded numeric predictive input maps results in a poor mineral potential map outcome. An expert review to determine reclassifications or thresholds that produce geologically meaningful maps as proxies for the mineral system being targeted results in more effective RF-based mineral potential maps being produced. Weighting or ranking the deposits used as training data produces more narrowly defined prospective areas that may assist with targeting tier-one economic deposits. Comparison of the RF results to a standard weights of evidence analysis highlighted some significant differences in which predictive maps should be considered important for modeling, and in the extent of prospective area delineated from each output mineral potential map.

Here, we present holistic targeting models for lithium–cesium–tantalum (LCT) pegmatites in Western Australia, the world’s largest supplier of hardrock lithium ores, and Ontario, an emerging hardrock lithium mining jurisdiction. In this study, the LCT pegmatite targeting models, informed by a review of this deposit type and framed in the context of a mineral system approach, served to identify a set of targeting criteria that are mappable in the publicly available exploration data for Western Australia and Ontario. This approach, which formed the basis for artificial intelligence (AI)-powered mineral potential modeling (MPM), using multiple, complimentary modeling techniques, not only delivered the first published regional-scale views of lithium potential across the Archean to Proterozoic terrains of Western Australia and Ontario, but it also delivered an effective framework for exploration and revealed hidden trends. For example, we identified a statistically verifiable proximity relationship between lithium, gold, and nickel occurrences and confirmed a significant size differential between LCT pegmatites in Western Australia and Ontario, with the former typically containing much larger resources than the latter. Overall, this regional-scale targeting study served to demonstrate the power of precompetitive, high-quality geoscience data, not only for regional-scale targeting but also for the development of camp-scale targets that have the resolution to be investigated using conventional prospecting techniques. Importantly, MPM does not generate ‘treasure maps’. Rather, MPM provides another tool in the ‘exploration toolbox’, and its output should be taken as the starting point for further investigations.

Replenishment of mineral resources, especially gold and rare metals, is critical for progress in the mining and metallurgical industry of Eastern Kazakhstan. To substantiate the scientific background for mineral exploration, we study microinclusions in minerals from gold and rare-metal fields, as well as trace-element patterns in ores and their hosts that may mark gold and rare-metal mineralization. The revealed compositions of gold-bearing sulfide ores and a number of typical minerals (magnetite, goethite, arsenopyrite, antimonite, gold and silver) and elements (Fe, Mn, Cu, Pb, Zn, As, and Sb) can serve as exploration guides. The analyzed samples contain rare micrometer lead (alamosite, kentrolite, melanotekite, cotunnite) and nickel (bunsenite, trevorite, gersdorffite) phases and accessory cassiterite, wolframite, scheelite, and microlite. The ores bear native gold (with Ag and Pt impurities) amenable to concentration by gravity and flotation methods. Multistage rare-metal pegmatite mineralization can be predicted from the presence of mineral assemblages including cleavelandite, muscovite, lepidolite, spodumene, pollucite, tantalite, microlite, etc. and such elements as Ta, Nb, Be, Li, Cs, and Sn. Pegmatite veins bear diverse Ta minerals (columbite, tantalite-columbite, manganotantalite, ixiolite, and microlite) that accumulated rare metals late during the evolution of the pegmatite magmatic system. The discovered mineralogical and geochemical criteria are useful for exploration purposes.

This paper presents the results of an integrated targeting study covering the central-eastern Archean Yilgarn Craton of Western Australia, a region renowned for its substantial gold endowment (>40 Moz Au). The cornerstones of this study included custom-built geophysical and remote sensing targeting tools, a new lithostructural interpretation of the area, a targeting model based on the mineral systems approach, and a best-practice mineral potential modeling (MPM) workflow employing five complementary modeling techniques. The geophysical targeting tools were used to identify proximity, association, and abundance relationships between gold mineralization and gravity ridges or edges, as well as 95th-percentile K/Th radiometric and remotely sensed goethite–clay–iron feature depth index ratio anomalies. The lithostructural interpretation revealed structural trends oblique or orthogonal to the NNW-SSE-striking greenstone belts, likely representing important structural controls on gold mineralization. Fry analysis, used to assess the spatial distribution of geological point patterns, showed similar directions of maximum gold occurrence alignment. Together, these observations proved to be strong predictors of gold prospectivity in the MPM component of this targeting study. The MPM not only identified most known gold occurrences but also highlighted several underexplored areas with significant potential. The highest-priority MPM targets represent roughly an order-of-magnitude reduction in search space, the hallmark of a well-performing and practically useful targeting methodology.

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.