2D inverse modeling for potential fields on rugged observation surface using constrained Delaunay triangulation

The regular grid discretization is prevalent in the inverse modeling for gravity and magnetic data. However, this subdivision strategy performs lower precision to represent the rugged observation surface. To deal with this problem, we evaluate a non-structured discretization method in which the subsurface with rolling terrain is divided into numbers of Delaunay triangular cells and each mesh has the uniform physical property distributions. The gravity and magnetic anomalies of a complex-shaped anomalous body are represented as the summaries of the single anomaly produced by each triangle field source. When inverting for the potential field data, we specify a minimization objective function composed of data constraints and then use the preconditioned conjugate gradient algorithm to iteratively solve the matrix minimization equations, where the preconditioner is determined by the distances between triangular cells and surface observers. We test our method using synthetic data; all tests return favorable results. In the case studies involving the gravity and magnetic anomalies of the Mengku and Pobei deposits in Xinjiang, northwest China, the inferred magnetite orebodies and ultrabasic rocks distributions are verified by the additional drilling and geological information. The discretization of constrained Delaunay triangulation provides an useful approach of computing and inverting the potential field data on the situations of undulate topography and complicated objects. Use the constrained Delaunay triangulation to divide the subsurface.Implement the preconditioned conjugate gradient inverting for potential fields.Use the synthetic and field data to test the method and return favorable results.Be applicable for the scenarios with undulate topography and complicated objects.