Gold anomaly ranking based on stream sediment geochemistry in the Fariman–Kashmar axis, NE Iran

One of the essential tasks accelerate the decision-making process in mineral exploration projects is ranking anomalous areas.In this study,we used fourteen geologic maps(at scale 1:100,000)in areas where systematic geochemical explorations were conducted in the Fariman-Kashmar axis in northeast Iran to conduct the anomaly ranking.On all these maps,samples were consistently prepared to be analyzed through statistical and geostatistical methods.At first,anomaly separation was carried out by fractal methods that resulted in the detection of 308 anomalous samples in 128 areas.These samples were classified into three groups of first,second,and thirdorder anomalies,whose number of anomalous samples were calculated based on this ranking technique.Three factors,including the average concentration of each anomaly,its surface area,and the number of its samples,were used to rank the areas.According to this technique,the maximum anomaly score obtained was 172 for the Taknar area,and the minimum score was 3 for several areas.To validate the ranking results,some exploration operations were carried out in some of these anomalous areas in which mining operations started later.Several significant gold anomalous areas were introduced,which is considered an important result of this study.

Geochemical Features and Metallogenic Prediction of the Caixiashan-Weiquan Area in the East Tianshan Region

The Caixiashan-Weiquan area is an important ore concentration area in the eastern Tianshan metallogenic belt. Firstly, this paper studies geochemical features of 1564 samples of 1：200000 stream sediments of the Matoutan mapsheet, where the Caixiashan and Weiquan deposits are located. Processing, analysis and explanation of exploration geochemical data play an important role in the procedure of finding the ore, which are related to whether the measured elements content of geochemical samples can effectively guide the work of mineral exploration. As a highly nonlinear dynamical system, the neural network is more analogous to the human brains in terms of principles and features compared with conventional geochemical approaches. It can adapt itself to the environment, sum up laws, complete pattern recognition. Secondly, the authors used the Kohonen neural network to classify all samples based on 10 mineralization elements of stream sediment samples in order to determine possible mineral ores, reduce the scope of ore targets and study indicator elements of the ninth group of samples, which is the mostly closest to the deposit. The results show that the neural network can delineate metallogenic prospective areas and is effective in the discovery of deep geochemical information.