Depth Estimation of Potential Field by Minimum Inversion Fitting Error

Abstract

Estimation of source depth plays an important role in quantitative interpretation of magnetic or gravity data. Various methods have been developed to conduct this estimation, especially for magnetic source depth. They include slope, Naudy, Werner deconvolution, Euler deconvolution, analytical signal, source parameter imaging (SPI), the continuous wavelet transform (CWT) and tilt‐depth approaches. We present a new method to estimate the depth of a field source, which is based on equivalent source technology and potential field inversion. A single layer of 2.5D cuboids model is established as an equivalent source with initial physical property parameters. The single equivalent source layer moves from shallow to deep at certain interval and is used as the initial model to invert the data. Then we estimate the field source depth by inversion fitting error. From shallow to deep, the inversion fitting error usually becomes smaller. The minimum inversion fitting error matches the corresponding field source depth. Because only one equivalent source layer is necessary to invert, the inversion is faster than traditional inversion methods and does not require depth weighting. Calculation of theoretical model data shows that this method can obtain accurate depth of the field source. The data processing of a thin plate with an aspect ratio of 7.5 shows that the depth calculation error is about one measured point (25 m). The data processing of a thick plate with an aspect ratio from 0.5 to 1.5 shows that the depth calculation error is less than one measured point (25 m). Processing of measured aeromagnetic gradient data indicates that the central depth of the magnetic source is between 200 m to 250 m. Drilling data show that such anomalies are caused by the diorite at depth from 200 m to 300 m, in agreement well with estimation. These tests demonstrate that the depth estimation method suggested in this paper is applicable to both isolated anomalies and combined anomalies.