Using the shortwave infrared data from the Aster sensor with the Artificial neural network method to identify areas with high purity of calcite in the Dahuiyeh limestone, Kerman province
Dahuiyeh mine area in the northwest of Kerman province has high-purity limestone resources and is a valuable example in the exploration of high-purity calcium carbonate deposits, with emphasis on remote sensing. The main workable limestone layer in the area belongs to the Shotori Formation and Espahk Limestone Member of the Permo-Triassic age, which is placed in contact with lower Paleozoic formations and is overlain by the Red Shale Formation. Studies and observations in this area have identified limestone layers with high purity of calcite. These carbonate layers are located between the lower Espahk and Shotori limestone deposits that exhibit a natural fluctuation trend due to structural issues. In order to analyze these layers more accurately, a neural network method was applied to the ASTER data using remote sensing. Training data were created for the neural network using field samples and laboratory information. This neural network accurately separates the ranges of maximum purity and minimum contamination in the short infrared wavelength range using ASTER sensor data with a precision of 80%. All results of these methods were validated through field studies, sampling and chemical analysis, So that the samples collected from the training and validation pixels were samples with CaO higher than 54% and calcium carbonate purity higher than 99% among the sediments of Espahk and Shotori limestone, which were correctly classified with this level of accuracy. This study demonstrates that remote sensing methods, especially those utilizing neural networks, can effectively and accurately detect and differentiate areas with high carbonate quality.
Sedighian, S. (2025). Using the shortwave infrared data from the Aster sensor with the Artificial neural network method to identify areas with high purity of calcite in the Dahuiyeh limestone, Kerman province. Advanced Applied Geology, (), -. doi: 10.22055/aag.2024.47837.2473
MLA
Sedighian, S. . "Using the shortwave infrared data from the Aster sensor with the Artificial neural network method to identify areas with high purity of calcite in the Dahuiyeh limestone, Kerman province", Advanced Applied Geology, , , 2025, -. doi: 10.22055/aag.2024.47837.2473
HARVARD
Sedighian, S. (2025). 'Using the shortwave infrared data from the Aster sensor with the Artificial neural network method to identify areas with high purity of calcite in the Dahuiyeh limestone, Kerman province', Advanced Applied Geology, (), pp. -. doi: 10.22055/aag.2024.47837.2473
CHICAGO
S. Sedighian, "Using the shortwave infrared data from the Aster sensor with the Artificial neural network method to identify areas with high purity of calcite in the Dahuiyeh limestone, Kerman province," Advanced Applied Geology, (2025): -, doi: 10.22055/aag.2024.47837.2473
VANCOUVER
Sedighian, S. Using the shortwave infrared data from the Aster sensor with the Artificial neural network method to identify areas with high purity of calcite in the Dahuiyeh limestone, Kerman province. Advanced Applied Geology, 2025; (): -. doi: 10.22055/aag.2024.47837.2473
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