Abnormal extraction of geochemical data based on Kalman filter and SVM

Abstract

Exploration geochemistry integrating anomaly characteristics is an important index of mineral prediction. Mineralizing process is complex. Because of the superposition of the primary environment and the evolution of the secondary environment, the dispersion of geochemical model presents complex features. Effective method of comprehensive abnormal extraction is especially important. In recent years, information fusion technology is a focal point of research. And it has achieved great success in signal and image processing. Information fusion technology offers a new way for comprehensive abnormal extraction. It is a processing method of multi-order, multi-aspects and multi-level for multi-sensor information, so as to get some new efficient information. Kalman filtering is one of the typical representatives. Kalman filtering is a kind of optimal estimation algorithm and it take for linear, unbiased and the minimum variance as the criterion, and the algorithm thought correspond to the comprehensive characteristics. The introduction of Kalman filter for exploration geochemistry integrating anomaly can provide a new method of exploration geochemical prospecting.