Separation of a geochemical anomaly from background by fractal and U-statistic methods,a case study: Khooni district,Central Iran |
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| Institution: | 1. Orsu Metals Corporation, London, United Kingdom;2. AIRIE, Dept. of Geosciences, Colorado State University, Fort Collins, USA;3. Paladin Energy, Hay Street, Subiaco, Perth, Western Australia, Australia;1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China;2. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China;1. CSIR-National Geophysical Research Institute, Uppal Road, Hyderabad — 500007, India;2. Indian Institute of Geomagnetism, Navi Mumbai — 410218, India;1. School of Energy Resources, China University of Geosciences, Beijing 100083, China;2. Key Laboratory for Marine Reservoir Evolution and Hydrocarbon Abundance Mechanism, Ministry of Education, China University of Geosciences, Beijing 100083, China;3. Beijing Key Laboratory of Unconventional Natural Gas Geology Evaluation and Development Engineering, China University of Geosciences, Beijing 100083, China;4. Key Laboratory for Shale Gas Exploitation and Assessment, Ministry of Land and Resources, China University of Geosciences, Beijing 100083, China;5. School of Geosciences, China University of Petroleum, Qingdao 266580, China;6. Zhongyuan Oilfield Co. Ltd., SINOPEC, Oil Recovery Plant No.3, Puyang 066004, China;1. Faculty of Earth resources, China University of Geosciences, Wuhan 430074, China;2. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China |
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| Abstract: | Separation of geochemical anomalies from background are one of the important steps in mineral exploration. The Khooni mineral district (Central Iran) has complex geochemical surface expression due to a complex geological background. This region was chosen as a study area for recognition of the spatial distribution of geochemical elements and separating anomalies from background using stream sediment geochemical data. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods, spatial statistical methods and fractal and multi-fractal methods. In this article, two efficient methods, i.e. U-statistics and the fractal concentration-area for separation and detection of anomalous areas of the background were used. The U spatial statistic method is a weighted mean, which considers sampling point positions and their spatial relation in the estimation of anomaly location. Also, fractal and multi-fractal models have also been applied to separate anomalies from background values. In this paper, the concentration–area model (C–A) was suggested to separate the anomaly of background. For this purpose, about 256 stream sediment samples were collected and analyzed. Then anomaly maps of elements were generated based on U spatial statistics and the C-A fractal methods for Au, As and Sb elements. According to obtained results, the U-statistics method performed better than C-A method. Because the comparisons of the known deposits and occurrences against the anomalous area created using thresholds from U-statistics and C-A method show that the spatial U-statistics method hits all of 3 known deposits and occurrences, the C-A fractal method hits 1 and fails 2. In addition, the results showed that these methods with regard to spatial distribution and variability within neighboring samples, in addition to concentration value frequency distributions and correlation coefficients, have more accurate results than the traditional approaches. |
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| Keywords: | Separation Geochemical anomaly Background C-A fractal U-statistic Khooni district |
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