Dictionary learning for integration of evidential layers for mineral prospectivity modeling

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.