Integrating sequential indicator simulation and singularity analysis to analyze uncertainty of geochemical anomaly for exploration targeting of tungsten polymetallic mineralization, Nanling belt, South China

2019 ◽  
Vol 197 ◽  
pp. 143-158 ◽  
Author(s):  
Yue Liu ◽  
Qinglin Xia ◽  
Emmanuel John M. Carranza
2021 ◽  
pp. 1-10
Author(s):  
Mengxue Cao ◽  
Laijun Lu ◽  
Yu Zhong

How to more effectively perform anomaly detection of combination information has always been an important issue for the scholars in various fields. In order to identify and extract the geochemical anomaly information related to polymetallic mineralization in the Hunjiang area, this article uses the hybrid method that combines multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing map (IAFSOM). First, multiple sets of combination feature information with multi-dimensional variables will be obtained through the MCHTA method, which information is considered as the initial information for the subsequent analysis. Next, the singularity analysis method is used to process the combination concentration value to calculate the singularity indexes. Finally, the singularity indexes are classified by the IAFSOM method, and nine groups of sample data are obtained. The analysis results found that the samples information in fourth group covered most of the low α-values. The main conclusions in this study are as follows: (1) The MCHTA method can effectively detect the combination information related to geochemical anomaly; (2) The application of singularity analysis method with radius-areal metal amount can reveal the significant characteristics of mineralization combination elements; (3) IAFSOM can be used as an effective tool for the classification and identification of geochemical anomaly with combination information; (4) the hybrid method that combines MCHTA method, singularity analysis and IAFSOM model has a good indication significance in the prospecting of geochemical anomalies, and could provide a good method for geochemical prospecting.


2018 ◽  
Vol 54 (6) ◽  
pp. 3755-3785 ◽  
Author(s):  
Huan Li ◽  
Hua‐Shan Sun ◽  
Thomas J. Algeo ◽  
Jing‐Hua Wu ◽  
Jing‐Ya Cao ◽  
...  

2007 ◽  
Vol 87 (5) ◽  
pp. 551-563
Author(s):  
Carol Luca ◽  
Bing C Si ◽  
Richard E Farrell

Petroleum hydrocarbon (PHC) contamination is one of the most common contaminants in soils and remediation of PHC-contaminated sites requires methods for characterizing the spatial distribution of PHC on a site. Few studies have compared the performance of indicator kriging (IK) and sequential indicator simulation (SIS) in site characterization of petroleum-contaminated sites, or the application of these methods given the fraction based guidelines. The objectives of this study were to determine if IK and SIS indicate similar contaminated areas and to examine how the probability of exceeding thresholds changes when multiple fractions are considered simultaneously. An abandoned refinery near Kamsack, Saskatchewan, characterized by clay-textured soils was sampled and analyzed for PHC fractions (F2 and F3). The probability of a location exceeding a fraction’s remediation criteria was determined using IK and SIS. Based on critical probability thresholds, IK indicated a greater area was contaminated by F2 (6.3%) and F3 (0.8%) than SIS (4.5 and 0.6%, respectively). When the remediation criteria for both F2 and F3 were considered simultaneously, “dependent” and “independent” cases were examined. The dependent case assumed perfect correlation and used the maximum probability of either F2 or F3 as the new estimate. The independent case assumed no correlation and evaluated the probability of F2 > 2500 mg kg–1 or F3 > 6600 mg kg–1. The dependent case resulted in a smaller contaminated area than the independent case in both IK and SIS. On this site the differences between the two methods were small, although IK did smooth the distribution. Key words: Sequential indicator simulation, indicator kriging, geostatics, petroleum hydrocarbon contamination, uncertainty


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