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چکیده
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This research presents a new approach of a fractal model to magnetic data within a Geographic Information System (GIS) environment. We utilized data acquired by an Iranian-made geophysical instrument, equipped with both lower and upper sensors, from the Baba-Ali iron ore deposit in western Iran. The Concentration-Area (C-A) fractal method was employed for data modeling. Initially, data from the lower and upper sensors were independently interpolated using the indicator kriging method within the GIS environment, generating respective raster maps. These interpolated datasets then underwent C-A fractal modeling. This process delineated four distinct classes within the data, allowing for the determination of their corresponding geophysical threshold values and the creation of magnetic field intensity maps for each sensor’s data. Subsequently, the discrepancy between these two magnetic field intensity maps was calculated within the GIS environment to derive the final dataset. At this stage, a grid of 1.65 × 1.65 square meters, comprising 80,925 data points, was generated. These final data were then subjected to a second round of C-A fractal modeling to characterize their fractal behavior. This analysis revealed five distinct data clusters in the C-A plot. The initial three clusters were interpreted as representative of the geophysical background or the first phase of mineralization. In comparison, the subsequent two clusters were attributed to anomalous values or a second phase of mineralization. This enabled the determination of a final threshold value. The resulting geophysical anomaly map demonstrates that fractal modeling of magnetic data in a GIS environment, by effectively discerning fractal patterns within geophysical datasets, offers a highly effective approach for optimizing the identification of geophysical anomaly zones and suggesting exploratory drilling targets. Specifically, a discrepancy in Earth’s magnetic field intensity between the lower and upper magnetic induction (MI) sensors equal to or exceeding 15,548 nT was identified as indicative of an anomaly.
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