A deep embedded clustering algorithm in conjunction with an ensemble technique for mineral prospectivity mapping

Traditional clustering algorithms are popular unsupervised methods and have been widely applied in mineral prospectivity mapping (MPM). Despite the advantages of these algorithms in terms of simplicity and popularity, they are not strong enough to struggle with high-dimensional, complex, and non-linear geospatial data. Consequently, they may lead to suboptimal clustering performance, a reason for not being able to precisely recognize and discriminate complex mineralization-related anomaly patterns in mineral exploration datasets. To improve the clustering performance, we propose a deep embedded clustering (DEC) approach for MPM. DEC is an unsupervised method that uses deep neural networks to learn from the feature representations and optimize cluster assignments simultaneously. In this study, evidence layers, representing porphyry copper mineralization, were first generated. Then, four clustering techniques were applied to generate prospectivity models. The prediction rate of the models was evaluated using the prediction-area (P-A) plot. The results showed that the prediction rates of K-means, Gaussian mixture model (GMM), DEC-K-means, and DEC-GMM prospectivity models were 66, 68, 69, and 72%, respectively. This demonstrates that DEC-based clustering outperforms conventional clustering algorithms and that DEC-GMM effectively recognizes mineralization-related patterns. Finally, to benefit from the advantages of all the applied clustering methods, we calculated a confidence index, as an ensemble technique, to recognize exploration targets, those that support further mineral exploration programs in terms of low uncertainty.