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[Display omitted] • Field-portable exploration methods were systematically described in shallow overburden areas for vein-type fluorite deposits. • The advantages and disadvantages of field-portable fluorite exploration methods have been analyzed. • The Effectiveness of RS + VLF-EM + PRXF for exploration has been validated by trenching and drilling. Fluorite deposits are widespread throughout eastern China and commonly occur in vein systems. These vein-type fluorite deposits are the primary source of fluorine worldwide and were predominantly formed during the Mesozoic or Cenozoic periods. In the absence of ore outcrops, fluorite exploration is necessarily concentrated within the shallow overburden area. The Wuliji'Oboo fluorite deposit is an example of a medium–low temperature vein-type subsurface orebody in eastern Inner Mongolia. Five portable technology methods have been implemented within the study area, including multispectral remote sensing (RS) technology, very low-frequency electromagnetic measurement (VLF-EM), portable X-ray fluorescence analyses (PXRF), portable gamma-ray spectroscopy, and partial extraction geochemistry. This paper describes the application principles, influencing factors, and technical solutions for each method. By investigating various examples, the applications of portable technology methods and their associated limitations were evaluated. Combining geological research with multi-spectral RS techniques is essential for optimum fluorite ore body prediction, thus narrowing prospecting targets. First, the target area was scanned using VLF-EM to identify the distribution of potential anomalies. Second, PXRF was used to distinguish the mineralised anomaly. Finally, trenching and drilling were performed to verify the effectiveness of the combination of methods. This study establishes a geology-geophysics-geochemistry-remote sensing integrated exploration model that may provide theoretical and technical support for the prediction of fluorite ore bodies in large Quaternary shallow overburden areas. [ABSTRACT FROM AUTHOR]

• Dictionary learning is used to model gold prospectivity for gold exploration targeting. • The case study shows that the dictionary learning model is no inferior to the logistic regression model and superior to the one-class SVM model. Machine learning and deep learning anomaly detectors have been successfully used to map mineral prospectivity. However, the establishment of machine learning or deep learning anomaly detection models for mineral prospectivity mapping often requires the determination of a set of initialization parameters in the absence of ground truth data. Improper initialization parameters will degrade the performance of these mineral prospectivity mapping models. Different from most machine learning and deep learning algorithms, dictionary learning is a "white-box" algorithm involving only linear transformations, and building a dictionary learning model requires only an empirical definition of the total number of atoms. Therefore, in this paper, two dictionary learning anomaly detectors were established for mineral prospectivity modeling based on the least angle regression-Lasso (LARS-Lasso) algorithm and the iterative shrinkage-thresholding algorithm (ISTA). The following five-step procedure was proposed for mineral prospectivity modeling using the dictionary learning techniques: (a) an overcomplete dictionary is constructed based on the input data; (b) each data point is transformed into sparse coefficients according to the overcomplete dictionary; (c) the sparse representation of each data point is calculated based on the overcomplete dictionary and the sparse coefficients; and (d) the Euclidean norm of the difference between each data point and its sparse representation is calculated and used as the mineral potential of the data point. The dictionary learning models were established to model gold prospectivity in the Jinchanggouliang district, Inner Mongolia, China, and compared with the logistic regression model and one-class support vector machine model in gold exploration targeting. The result shows that (a) the performances of the dictionary learning models are no less than that of the logistic regression (LGR) model and better than that of the one-class support vector machine (OCSVM) model, and (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. Therefore, the dictionary learning algorithms are high-performance mineral prospectivity modeling techniques. It is worth to further test the effectiveness of the dictionary learning algorithms for different types of mineral deposits in different areas in mineral exploration targeting. [ABSTRACT FROM AUTHOR]