Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest - Yang, Na; Zhang, Zhenkai; Yang, Jianhua; Hong, Zenglin (Natural resources research (New York, N.Y.), 2023)
According to the characteristic that mineralized-anomaly samples have larger reconstruction errors, traditional autoencoder networks have been applied widely in mineralized-anomaly identification. However, they easily ignore spatial coupling information of multi-source ore-indicating factors have lower generalization abilities caused by excessive feature redundancies, and rely on known non-mineralization samples when they are utilized in mineralized-anomaly identification. This paper utilizes a convolutional sparse autoencoder network for realizing mineralized-anomaly identification. The proposed method retains the extraction of spatial coupling correlations of geological and geochemical variables through the addition of convolutional operations, learning the relationship between mineralized-features and the locations of mineralization, and being conducive to analyzing results with geological structures. The establishment of sparse terms of using ReLU activation functions and adding sparsity constraints into the objective loss function improves the generalization ability of the whole network through suppression of several feature units to reduce redundant features. Moreover, the isolated forest is employed as an autonomous extractor of background multichannel image samples, overcoming the limitation of traditional autoencoder networks that rely on labeled non-mineralization samples. The integration of convolutional sparse autoencoder network and isolated forest can accurately predict more known mineral deposits (68.96%) in smaller prospective areas (16.77%) in the Fengxian District, Shaanxi Province, China. The obtained mineralized-anomaly map reveals that most of the known mineralization is distributed in the delineated areas of larger reconstruction errors, demonstrating that this approach can effectively identify mineralized-anomalies without relying on prior knowledge.