44 research outputs found
Reconnaissance-Scale Prospectivity Analysis for Gold Mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland
Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic
Integrating geologic and Landsat-8 and ASTER remote sensing data for gold exploration: a case study from Zarshuran Carlin-type gold deposit, NW Iran
Analysis and modeling of geospatial datasets for porphyry copper prospectivity mapping in Chahargonbad area, Central Iran
Geotechnical investigations for landslide hazard and risk analysis, a case study: the landslide in Kojour Region, North of Iran
Sub pixel mapping of alteration minerals using SOM neural network model and hyperion data
Comparing U-statistic and nonstructural methods for separating anomaly and generating geochemical anomaly maps of Cu and Mo in Parkam district, Kerman, Iran
© 2015, Springer-Verlag Berlin Heidelberg.In applied geochemistry, obtaining quantitative descriptions of geochemical patterns and identifying geochemical anomalies are important. To identify and separate geochemical anomalies, several statistical methodologies (nonstructural and structural) are presented by researchers. In this study, four nonstructural methods including threshold assessment method based on median and standard deviation, median absolute deviation (MAD), P.N product and Sinclair’s method are selected first and then U-statistic is considered as a structural method to compare their performance. Subsequently, the best method is used to assess prospective areas of Parkam district. Results show that P.N and Sinclair’s methods are not always efficient. MAD method reduced the background well and roughly increased the correlation factor of points. However, U-statistic method includes both mentioned advantages meaning in addition to reducing outlier data effect, it regularizes anomalous values and also their dispersion is reduced significantly. It is possible to determine anomaly areas according to anomalous samples positioning so that denser areas are more important. Finally, lithogeochemical map of study area is generated for copper and molybdenum. In this map, the Cu mineralization which is delineated by this method is closely associated with the defined potassic alteration zone (according to alteration map of the study area), and also, the delineated Mo mineralization is exactly associated with the phyllic alteration and is spatially conformable with the zone defined for it