Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

Large-scale, high-density geochemical explorations entail enormous workloads and high costs for sample analysis, but, for early mineral exploration, absolute concentrations are not essential. Geochemists require ranges, dynamics of variation, and correlations for early explorations rather than absolute accuracy. Thus, higher work efficiency and lower costs for sample analysis are desirable for geochemical exploration. This study comprehensively analyzed the reliability and applicability of portable X-ray fluorescence (pXRF) spectrometry in geochemical exploration. The results show that pXRF can be applied effectively to rock and rock powder samples, and sample preparation and a longer detection time have been shown to increase the precision of the pXRF results. When pXRF is used on rock samples, if less than 30% of the samples are assessed as containing an element, the element is usually undetectable using pXRF when these rock samples are prepared as rock powders, indicating that the data about the detected element are unreliable; thus, it is suggested that some representative samples should be selected for testing before starting to use a pXRF in a geochemical exploration project. In addition, although the extended detection time increased the reliability of the analysis results, an increase in detection time of more than 80 s did not significantly affect the accuracy of the results. For this reason, the recommended detection time for the pXRF analysis of rock powder samples is 80 s for this study. pXRF has the advantages of being low-cost, highly efficient, and stable, and its results are reliable enough to exhibit the spatial distribution of indicator elements (arsenic, nickel, lead, sulfur, titanium, and zinc) in polymetallic mineralization exploration. Therefore, pXRF is recommendable for practical use in geochemical exploration.

Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulness of super-low-altitude HySpex hyperspectral images in estimating detritus feasibility and effectiveness of Cu element content. The results show that: (1) among all the inversion models of transformed spectra, power-logarithm transformation spectrum was the optimal prediction model (coefficient of determination(R²) = 0.586, mean absolute error(MAE) = 21.405); (2) compared to the genetic algorithm (GA) and continuous projection algorithm (SPA), the competitive weighted resampling algorithm (CARS) was the optimal feature band-screening method. The R² of the inversion model was constructed based on the characteristic bands selected by CARS reaching 0.734, which was higher than that of GA (0.519) and SPA (0.691), and the MAE (19.926) was the lowest. Only 20 bands were used in the model construction, which is lower than that of GA (105) and SPA (42); (3) The power-logarithm transforms, and CARS combined with the model of HySpex hyperspectral images and the Cu content distribution in the study area were obtained, consistent with the actual survey results on the ground. Our results prove that the method incorporating the HySpex hyperspectral data to invert copper content in detritus is feasible and effective, and provides data and a reference method for obtaining geochemical element distribution in a large area and for reducing key areas of geological exploration in the future.

Several techniques, such as chemical methods and inductively coupled plasma mass spectrometry (ICP-MS), are available to accurately determine element content. However, they are time-consuming, labor-intensive, or expensive. Portable X-ray fluorescence spectrometry (pXRF) can be applied in various scenarios, with significantly higher efficiency and cost-effectiveness than laboratory methods. However, it also has limitations such as lower detection capability, relatively high detection limits, and lower accuracy than laboratory methods. In this study, we focused on applying pXRF to determine the elemental content of sediment samples and investigate its use in mineral exploration. A variety of factors influencing the results of pXRF analysis were analyzed. Our results showed that pXRF could detect more than 30 elements in stream sediments. The reliability of pXRF’s measurements was affected by factors such as the kind of element, sediment particle size, sample grinding treatment, count time, averaged element content, standard deviation of content, and range of content variation. The combination of pXRF analysis and laboratory analysis of partial samples is adequate for establishing a multi-element content inference equation. With this equation, it is possible to effectively infer the content gradient of elements, which will provide valuable support for mineral resource exploration.

Geochemical data is crucial for reflecting geological features and is extensively applied in mineral exploration, environmental impact assessment and geological research. However, the high economic cost of geochemical data analysis hinders large-scale studies, leading to low spatial resolution, especially in remote areas. Although remote sensing data provides rich surface spectral information and shows a strong correlation with geological features, its accuracy in large-scale geochemical data inversion is insufficient. Therefore, we improve the accuracy and reliability by fusing multi-source geoscience data. Vegetation information, digital elevation models (DEMs), and aeromagnetic data, among other geoscience data, offer new perspectives for geochemical data analysis. This paper proposes a novel Multi-modal Spatial-Spectral Fusion Model with Swin Transformer and Convolutional Networks for Regression (MSSF-SCR). This model extracts spatial features from multi-source geoscience data using a multi-branch Swin Transformer and dynamically adjusts feature weights with the MMHCA module. The Swin Transformer unifies spatial features, addressing semantic disparities among diverse data sources. Spectral features from remote sensing data are then fused with spatial features through two-dimensional convolutional regression, producing 15-meter-resolution geochemical maps. Experiments conducted in the Dananhu-Tousuquan Island Arc region of East Tianshan demonstrate that MSSF-SCR achieves superior performance in terms of R ² , R, MAE, and RMSE indices for five elements (Al ₂ O ₃ , Fe ₂ O ₃ , K ₂ O, MgO, and SiO ₂ ).