Exploration
Hamid Geranian; Mohammad Amir Alimi
Abstract
This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided ...
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This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided into three and fifteen rock units. The classification accuracy of the resulting model is 90.97 and 84.85% for the three-class training and testing data, and 94.76 and 63.92% for the fifteen-class training and testing data, respectively. These models are then applied to the Sentinel-2 satellite images' data of the Birjand-IV region to prepare two preliminary geological maps. The Birjand-IV region's three-class geology map reveals that igneous rocks are present in the northern and southern regions, while sedimentary rocks occupy the middle section and metamorphic rocks are found within the region's igneous masses. Similarly, the fifteen-class geology map of Birjand-IV indicates that andesite, dacite, intermediate tuff rock units, and metamorphic rocks characterize the northern region. Conversely, the southern part of the region is mainly composed of ophiolite, flysch sediments, basaltic and ultra-basic volcanic rocks, and limestone and shale interlayers. Field studies in three areas confirm the accuracy of the preliminary geology maps.
H. Geranian; Z. Khajeh Miry
Abstract
In this work, we aim to identify the mineralization areas for the next exploration phases. Thus, the probabilistic clustering algorithms due to the use of appropriate measures, the possibility of working with datasets with missing values, and the lack of trapping in local optimal are used to determine ...
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In this work, we aim to identify the mineralization areas for the next exploration phases. Thus, the probabilistic clustering algorithms due to the use of appropriate measures, the possibility of working with datasets with missing values, and the lack of trapping in local optimal are used to determine the multi-element geochemical anomalies. Four probabilistic clustering algorithms, namely PHC, PCMC, PEMC, PDBSCAN, and 4138 stream sediment samplings, are used to divide the samples into the three clusters of background, possible anomaly, and probable anomaly populations. In order to determine these anomalies, ten and eight metal elements are selected as the chalcophile and siderophile elements, respectively. The results obtained show the areas of approximately 500 and 5,000 km2 as the areas of the probable and possible anomalies, respectively. The composite geochemical anomalies of the chalcophile and siderophile elements are mostly dominant in the metamorphic-acidic-intermediate rock units and the alkaline-metamorphic-intermediate rock units of the studied area, respectively. Besides, the obtained anomalies of the four clustering algorithms also cover about 65% of the mineralized areas, all mines, and almost 60% of the alteration areas. The validity criterion of the clustering methods show more than 70% validity for the obtained anomalies. The results obtained indicate that the probabilistic clustering algorithms can be an appropriate statistical tool in the regional-scale geochemical explorations.
