Rapid 3D cross-correlation imaging of potential field data for mapping iron deposits: a case study from the Shavaz region

Abstract

This study explores the application of a rapid 3D cross-correlation imaging technique for analyzing potential field gravity and magnetic data. The approach involves calculating cross-correlation values between observed potential field anomalies and theoretical data, followed by the assessment of correlation coefficients. The method's efficacy was demonstrated through preliminary tests on various synthetic models with differing characteristics, highlighting its ability to detect and accurately locate subsurface sources, even in complex scenarios with inclined and multiple sources. The 3D imaging outcomes provided comprehensive insights into the location and center of anomaly sources. The methodology was then applied to ground magnetic and gravity datasets to evaluate the spatial distribution of underground iron deposits in the Shavaz region. The results closely matched drilling data and prior studies in the area, reinforcing the technique's effectiveness in interpreting real geophysical data. This research emphasizes the advantages of the cross-correlation imaging method in enhancing the understanding of subsurface structures, revealing hidden anomalies, and estimating the depth and arrangement of buried features.