Improved Euler method for the interpretation of potential data based on the ratio of the vertical first derivative to analytic signal

Abstract

We propose a new automatic method for the interpretation of potential field data, called the RDAS-Euler method, which is based on Euler’s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies (structural index). It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfit between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS-Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.