Delineation Of Geochemical Anomalies Research Articles

Incorporation of geological constraints into geochemical anomaly identification using BME-GWR: A case study from Inner Mongolia of China

Delineation of geochemical anomalies is a fundamental task in mineral and environmental investigation. Mineralization area of interest is mainly controlled by underlying geological environment. These geological constraints facilitate to identify mineralization anomalies and remove pseudo-anomalies. Regional geochemical data is one of most commonly used in deriving mineralization information, which is also considered to be the primary data for direct geochemical anomaly identification. The selected study area, Jining in central Inner Mongolia of China, is mostly covered with Quaternary sediments, whereas is located in the important molybdenum polymetallic mineralized belt. The overburden shields the geochemical signatures from deep, resulting in missing some important information. The NE-trending faults, intersection of faults, Jining formation and mid-acid granites is mainly ore-forming factors in this study area, which are used as the geological constraints for identifying molybdenum polymetallic mineralization. Geographical weighted regression (GWR) generates the mean and variance of probability soft data based on these geological constraints. Bayesian maximum entropy (BME) integrates the constructed soft data with geochemical data for obtaining practical mineralization anomalies. These results shown that the combination of BME and GWR can identify local anomalies and weak anomalies compared with the results obtained from geochemical data alone. Specially, some anomalies of different periods of deposits have been highlighted in the results of BME-GWR model. The discovered deposits correspond tothe identified mineralization anomalies well. The BME-GWR model provides a potentially powerful tool for geochemical anomaly identification under consideration of geological constraints.

Applying a Structural Multivariate Method Using the Combination of Statistical Methods for the Delineation of Geochemical Anomalies

Quantitative descriptions of geochemical patterns and providing geochemical anomaly map are important in applied geochemistry. Several statistical methodologies (nonstructural and structural) are presented in order to identify and separate geochemical anomalies. Structural methods take the sampling locations and their spatial relation into account for estimating the anomalous areas. In the present study, a nonstructural method (Mahalanobis distance method as a multivariate method) is used and U-statistic is considered as a structural method to assess prospective areas of Susanvar district as a gold mineralization index in the Torud-Chah Shirin mountain range of Semnan Province, northern Iran. Results show that the U-statistic method is an efficient method according to spatial distribution of the anomalous samples. In the present study, according to the ability of U-statistic method in combining with other methods, the goal is to use and develop Mahalanobis distance method in structural mode. For this purpose, Mahalanobis distance should be combined with a structural method which devotes a new value to each sample based on its surrounding samples. For this reason, the combination of efficient U-statistic method and multivariate Mahalanobis distance method has been used to separate geochemical anomalies from background. Combination results show that the performance of these two methods is more accurate than using just one of them. Because samples indicated by the combination of these methods as anomalous are less dispersed and closer to each other than in the case of using just the U-statistic and other nonstructural methods such as Mahalanobis distance. Also it was observed that the combination results are closely associated with the defined Au ore indication within the study area. Finally, univariate and bivariate geochemical anomaly maps are provided for Au and As, which have been, respectively, prepared using U-statistic and its combination with Mahalanobis distance method.

Applying the combination of U-statistic and Mahalanobis distance as a multivariate structural method for the delineation of geochemical anomalies

ABSTRACTThere are several statistical methods for separating anomalous values from background leading to determination of anomalous areas. These methods range from simple approaches to complicated ones and include nonstructural and structural methods. Structural methods take the sampling locations and their spatial relation into account for estimating the anomalous areas. The U-statistic method is one of the most important structural methods. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and also to determine anomalous areas. In the present study, two nonstructural methods including gap statistic and Mahalanobis distance method (as a multivariate method) are used and U-statistic is considered as a structural method to assess prospective areas of Parkam district. Results show that the samples indicated by U-statistic and gap statistic methods as anomalous are more regular and less dispersed compared to those indicated by other nonstructural methods. Both gap statistic and U-statistic methods have shown almost similar results for estimating threshold and separation, but U-statistic method is even better than gap statistic because the U-statistic method devotes a new value to each sample which can be combined with other methods. For this reason, the combination of U-statistic and Mahalanobis distance method has been employed to separate geochemical anomaly from background. Combination results show that the performance of these two methods is more accurate than using just one of them. Finally, bivariate lithogeochemical map of the study area is provided for Cu and Mo which has been prepared using combination of the U-statistic and Mahalanobis distance method. In this map, the delineated Cu-Mo mineralization is closely associated with the defined zone of potassic alteration, which is also consistent with the field and microscopic observation of the Cu mineralization in this alteration zone. Moreover, it is associated with the phyllic alteration and is spatially conformable with the zone defined for it.

Delineation of geochemical anomalies using factor analysis and multifractal modeling based on stream sediments data in Sarajeh 1:100,000 sheet, Central Iran

The aim of this study is to delineate the Cu, Au, and Pb anomalies in Sarajeh 1:100,000 sheet located in Urumieh-Dokhtar ore belt, central Iran. The analyzed elements of stream sediment samples taken in the area can be classified into six groups (factors) by factor analysis. The concentration–area and number–size multifractal inverse distance weighted models were applied for recognition of the elemental thresholds which are similar in both used multifractal models. According to the thresholds, the elemental concentration distribution for Cu, Au, and Pb were divided to three lithological classifications, namely mainly c alkaline porphyry with Cu–Au mineralization, mafic and sedimentary rocks. The results illustrate that the major anomalies of Cu, Au, Pb and related factors are mostly located around intrusions, volcanics, and along NW–SE faults.

Assessing Uncertainty Associated with the Delineation of Geochemical Anomalies

A pedogeochemical exploratory survey of gold deposits was carried out in the region of Sao Sepe (southernmost Brazil). The region comprises a predominantly metamorphosed belt of volcanoclastics, sediments, serpentinites, basalts, gabbros, chert, tuffs, and banded iron formation of the Proterozoic age. The anomalies were identified first by stream sediment heavy mineral survey at the regional scale of exploration. Once spatial continuity was modeled, ordinary block kriging was performed to generate geochemical maps. Indicator block kriging also was used as an alternative in analyzing and interpreting geochemical data. A novel approach is proposed, which combines both ordinary and indicator kriging for delineating geochemical anomalies. Probability maps proved to be appropriate for selecting new sites for further exploration. Gold anomalies in soils trending NE were well defined by geostatistical analysis and subsequently confirmed by drilling.