Enhancement of Linear Features from Gravity Anomalies by Using Curvature Gradient Tensor Matrix

In this study, a new edge enhancement technique based on the eigenvalues and determinant from the curvature gradient tensor matrix (CGTM) has been described. The performance of the technique has been tested by using theoretical examples. It has been shown that the zero values of smallest eigenvalue of CGTM are suitable for mapping in source boundaries with a high resolution. The method has been applied to CGTM computed from the gravity anomaly data from Aydın-Germencik geothermal area in the western region of Turkey. The small eigenvalue provides a detailed image of the edges of basin and uplift and therefore give the results which tend to structural trends. I have also compared the Euler deconvolution (ED) position estimates with the small eigenvalue map obtained from the CGTM of residual gravity anomaly. Both techniques have agreed in detecting the basin and uplift edges in the subsurface.