Separation of magnetic-field data using the differential Markov random-field (DMRF) approach

Abstract

In this paper, the differential Markov random-field (DMRF) method is introduced and applied to the magnetic anomaly separation problem, in which residual anomalies are separated from a regional field. The DMRF method is all unsupervised statistical model-based learning approach that does not require prior knowledge. A data-adaptive program, based on the evaluation of noise and superimposed effects of various geologic structures, is presented by considering a statistical maximum a posteriori (MAP) criterion. The aim of our method is to capture the intrinsic properties of geologic structures and then to identify and hence understand the behavior of the observed magnetic-anomaly map. The magnetic-anomaly map is modeled using a 2D matrix. In the DMRF approach, each pixel of the matrix is evaluated considering neighboring pixels. In synthetic models, anomalies of magnetic dipoles are tested for different depths, orientation angles, and lengths.