Geochemical anomaly definition using stream sediments landscape modeling

Abstract

Geochemical anomaly mapping is a crucial part of environmental and mineral exploration projects. Stream sediments are the most frequently sampling medium for geochemical mapping in regional scales, resulting from various geological processes. To enhance geochemical anomaly in stream sediments, the geochemical landscape, including topographic factors and drainage catchment basin extracted from a digital elevation model (DEM), and average precipitations, were applied. These factors were taken into consideration in this research to investigate the influence of the geochemical landscape constraints on the Mo polymetallic mineralization anomaly of stream sediments in a typical hilly area of Jining, Inner Mongolia, China, using Bayesian Maximum Entropy (BME) approach. Geographically weighted regression was applied to construct probability density function of soft data based on slope, aspect, drainage catchment basin and precipitation. Stream sediment data, at a scale of 1:200,000 (hard data), was integrated with the soft data to map stream sediment anomalies associated with molybdenum-polymetallic mineralization. Considering the influences of geochemical landscape during stream sediment anomaly identification can make up the shortage of missing and incomplete observations in regional geochemical data. By integrating the geochemical landscape factors during the stream sediment anomaly identification process, the impacts of the geochemical landscape on sediment dispersion halos were addressed, and the anomaly identification accuracy was improved. The anomaly areas defined by the BME model demonstrated that 31.41% of the study area contained 75% of the known mineral deposits and consequently the uncertainty of geochemical anomalies was reduced.