Abstract

Multivariate geochemical anomalies are of great significance to the mineral exploration. The general method for multivariate geochemical anomalies is application of a hybrid method such as combining principal component analysis (PCA) and local singularity analysis (LSA). However, the unknown multivariate probability distribution of the geochemical data may could not meet the application condition of PCA for the detection of multivariate geochemical anomalies. In this study, the local RX anomaly detector based on double sliding windows was used to detect multivariate geochemical anomalies. Based on the idea of the local data of nonlinear manifold can be approximated linearly, the local RX anomaly detector converted the global nonlinear problem into a local linear problem in the multidimensional feature space of the geochemical data. The geochemical data from southwestern Fujian district (China) were carried out to validate the method. The anomaly map showed that majority of skarn Fe deposits are situated in areas with high value of RX(x), demonstrating that the detected anomalies may have a close spatial relationship with Fe mineralization. The comparing results with deep autoencoder network and the hybrid method combining with PCA and LSA, suggest that the local RX anomaly detector is a potential method to identify multivariate geochemical anomalies in a complex geological background.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.