A Fast Approach to Magnetic Equivalent Source Processing Using an Adaptive Quadtree Mesh Discretization

Abstract

The use of equivalent source processing on magnetic datasets is important for the regular gridding and denoising of data before any other processing can occur. The processing technique is setup as an inverse problem and solved for susceptibilities to reproduce the observed data. The drawback to the inverse problem is computation cost and overall speed for large-scale problems. Since aeromagnetics has become common in exploration, it is rare that the datasets acquired are small in data volume or space, and can be handled rapidly on a single workstation. One way to minimize the computational cost is to reduce the number of model parameters. We present an equivalent source processing technique that minimizes the number of cells in the model domain via an adaptive quadtree mesh discretization. The mesh remains coarse where no significant anomalies are present, yet fines on the edges of observed anomalies. The transition from the fine to coarse mesh grid is based on the total-gradient of the dataset, placing smaller cells on the edges of the anomaly where the susceptibilities have the greatest variation spatially. We show that the algorithm can perform over four times as fast as traditional equivalent source processing with a regular cell mesh yet preserves the same accuracy. In this paper, we present a synthetic example for proof of concept.