Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China

• Published in: Earth science informatics Vol. 11; no. 4; pp. 553 - 566
• Main Authors: Zhang, Nannan, Zhou, Kefa, Li, Dong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018; Springer Nature B.V
• Subjects: Algorithms; Back propagation networks; Basis functions; Binary data; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Gold; Information Systems Applications (incl.Internet); Kernel functions; Machine learning; Mapping; Mathematical models; Neural networks; Ontology; Parameters; Radial basis function; Research Article; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Statistical analysis; Statistical methods; Support vector machines; China; Support vector machines; Mineral prospectivity mapping; Back propagation neural network; Weight of evidence
• ISSN: 1865-0473; 1865-0481; 1865-0481
• DOI: 10.1007/s12145-018-0346-6

Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9–10 − 1; for the SVM model, a radial basis function was selected as the kernel function with best C = 1 and γ = 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight- of- evidence (W- of- E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area.