Application of remote sensing to determine promising areas of iron mineralization in Kashan district

Document Type : Original Article

Authors

1 Mining Engineering Department, Faculty of Geo- Engineering Sciences, Arak University of Technology, Arak, Iran

2 Mining Engineering Department, Faculty of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

Abstract

Introduction
The studied area, which is a part of the central Iran zone and the magmatic belt of Urmia-Dokhtar zone, is located in Isfahan province, Kashan district. Due to the volcanic and plutonic evolutions of Urmia-Dokhtar zone, various types of iron mineralization can be found in this zone in the form of hydrothermal, skarn and volcanic. Since the ASTER and EO-1 sensors are powerful in the short-wave infrared (SWIR) and visible-near-infrared (VNIR) spectrum bands, respectively, therefore, in this research, the combination of these two bands from these two different sensors was used to increase the precision and accuracy of iron prospecting through remote sensing in the Kashan district.
Materials and methods
The remote sensing process of the studied region comprises two stages. The first stage is pre-processing and data preparation before entering the processing stage. The second step is determining the best bands of ASTER and EO-1 ALI sensors and applying processing techniques containing false color composite (FCC), banding ratio (BR), Crosta selection method or directed principal component analysis (DPCA), supervised classification through spectral angle mapper (SAM) method and classification by the land surface temperature (LST) method which finally alteration-zoning map associated with iron mineralization in the studied region was produced.
Aeromagnetic data was acquired in an area of approximately 852.5 km2 in 754 stations with flight lines interspacing of 7.5 km on the alteration zones related to iron mineralization obtained by remote sensing method. Geosoft Oasis montaj software was employed for processing operation and qualitative interpretation of magnetic data via applying various corrections and filters including reduce to pole, upward continuation up to the variety of elevations, low-pass filter, derivative filters containing total horizontal derivative and analytical signal. To simulate and model the magnetic data, the studied area was divided into three-dimensional blocks with dimensions of 125*250*250 meters. At the end, to investigate the trend of magnetic anomalies observed on the surface, determination of approximate shape of the deposit and estimation of its depth, 3-D inverse modeling of the data was carried out using Lee and Oldenberg algorithm by UBC Mag3D 4.0 software.
Discussion and results
In this study, to identify phyllic alteration zones, bands 4, 6, and 7, argillic alteration zones, bands 4, 5, and 7, and propylitic alteration zones, bands 7, 8, and 9 from ASTER sensor was used as input to the component analysis method. The spectral angle mapping algorithm was applied with the data of both ASTER and EO-1 sensors which according to the obtained results, the ASTER sensor was better than the EO-1 to detect iron-related alterations. To calculate LST, radiometric and atmospheric temperature corrections were made on the band 10 ETM8 sensor whereas geometric and radiometric corrections were made on multispectral bands. The magnetometric studies of the region showed that the greatest changes in the intensity of the magnetic field are in the center of the study area and the continuation of these changes is towards the southeast of the area.
Conclusion
Based on the results of the recognition and prospecting phases by remote sensing method and the possibility of iron oxide in the area using airborne magnetometry, making the necessary corrections and applying various processes on the data, the anomaly zones of the area were identified. As a result of the three-dimensional modeling and inversion process of the magnetic data, two large masses located in the center and southeast of the region were identified. The results of the research through integrating two fast and relatively inexpensive methods of remote sensing and airborne magnetometry with 3-D inverse modeling of magnetic data, reveal that Kashan district has a high potential from viewpoint of iron ore-bearing.

Keywords

Main Subjects

References

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