Edge detection of subsurface structures using curvature gradient tensor of magnetic data

Summary: Edge detection of subsurface structures is an important objective in interpretation of magnetic data. In this paper, curvature gradient tensor (CGT) of magnetic data has been used along with tilt angle method to detect edges of subsurface structures. Application of these methods on synthetic and real gravity data has shown that the CGT of magnetic data, compared to the tilt angle method, can determine the edges of subsurface structures better. Introduction: The main objective of the interpretation of magnetic data is to extract information about subsurface structures. Edge detection is an important means to image the edges of subsurface structures. Therefore, edge detection has traditionally been an important objective in the interpretation of magnetic data. There are various methods for edge detection. Tilt angle method is a traditional method that can detect edges of subsurface structures quantitatively. The value of Tilt angle is zero above the edges of subsurface bodies. The curvature gravity gradient tensor (CGGT) has also been used to interpret subsurface geological structures quantitatively. The eigenvalues of CGGT are zero above edges of subsurface bodies. In this paper, the CGT of magnetic data has been used for edge detection of subsurface magnetic bodies. The results of using the CGT of magnetic data have been compared with the results obtained from applying Tilt angle method on the data. Methodology and Approaches: In order to obtain the CGT of magnetic data, at first, the magnetic data are reduced to pole (RTP). Then, horizontal vector gradients of the gradient tensors are computed from the RTP data using a Fourier transform technique. Then, the eigenvalues of the CGT of magnetic data are obtained. The small eigenvalue can only be used to detect the edges of bodies with positive susceptibility contrast, and the large eigenvalue can only be used to determine the edges of bodies with negative susceptibility contrast. As an example, chromite ore has positive density contrast with the host rock and produce positive gravity anomaly. Finally, the tilt angle method is also applied to compare its results with those of the CGT of magnetic data. Results and Conclusions: The robustness of the method used for the enhancement of edge detection is tested with a magnetic anomaly map caused by two prisms of synthetic bodies with positive and negative susceptibility contrast. The results have shown that the zero contour of the small eigenvalue of the CGT of magnetic data compared to the zero contour of the tilt angle method can better detect the edges of synthetic bodies with positive susceptibility contrast. Moreover, the zero contour of the large eigenvalue of the CGT of magnetic data compared to the zero contour of the tilt angle method can better detect the edges of synthetic bodies with negative susceptibility contrast. The Tilt angle method is also more sensitive to noise than the CGT of magnetic data. The CGT method has been applied to real magnetic data from Qahan porphyry copper deposit in Markazi Province, Iran. The results have indicated that the large eigenvalue of the CGT can determine the edges of porphyry deposit and the small eigenvalue can outline positive magnetic anomalies caused by propylitic alteration. However, the tilt angle method has not been capable of finding the edges of the porphyry deposit.