首页|Dictionary learning for multivariate geochemical anomaly detection for mineral exploration targeting
Dictionary learning for multivariate geochemical anomaly detection for mineral exploration targeting
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NSTL
Elsevier
? 2022 Elsevier B.V.Dictionary learning is usually used to train an overcomplete dictionary composed of basis vectors (i.e., atoms) to best sparsely represent the training dataset. The sparse representation of a data point is a sparse linear combination of the atoms derived from the well-trained overcomplete dictionary. The sparse representations of all the training data points constitute a supporting set of the population distribution of the training dataset. In this paper, geochemical exploration data was used as the training data of the five dictionary learning algorithms to train overcomplete dictionaries. The sparse representations of all the geochemical data points were used to describe the (complex) geochemical background, and the Euclidean distance between a data point and its sparse representation was used to express the anomality of the data point in multivariate geochemical anomaly detection. For demonstration purposes, the Chengde district in Hebei Province (China) was used as the case study area, and five dictionary learning models were established for multivariate geochemical anomaly detection for gold mineral exploration targeting. The performances of the five dictionary learning models were compared with that of the k-nearest neighbor (KNN) model, combined KNN model, and Gaussian mixture model (GMM) in gold mineral exploration targeting. The results show that (a) the five dictionary learning models are superior to the KNN model, the combined KNN model, and the GMM model either in terms of receiver operating characteristic curve (ROC) or in terms of area under the curve (AUC), and (b) the gold prospective areas optimally delineated in the study area accounts for a small proportion of the area, but contains most of the known gold deposits, and have spatial correlation with the favorable ore-forming factors such as the outer contact zone of multistage magmatic intrusion and the NE-trending fault zone. Therefore, the dictionary learning algorithms are feasible techniques of multivariate geochemical anomaly detection for mineral exploration targeting. It is worth to further test the usefulness of the dictionary learning algorithms in different areas with complex geochemical backgrounds.
NSTL
Elsevier
