Efficient 3-D Gravity Data Inversion With Depth Weighting Function

Gravity inversion plays a crucial role in estimating subsurface structural information. This process typically involves dividing the subsurface into numerous rectangular prisms and determining the parameters for each one. However, applying the method to large-area model parameters inversion is challenging due to high memory demands and computational costs associated with the extensive sensitivity matrix. To address this problem, a new gravity anomaly analytical expression has been developed to reduce the computational cost of the sensitivity matrix for gravity inversion. The compressed matrix technique, which eliminates the need to store and calculate the redundant sensitivity matrix, further reduces storage and computation time. In addition, depth and horizontal weighting functions have been introduced to alleviate inherent ambiguity and enhance the resolution of gravity inversion. The conjugate gradient approach is utilized as the fundamental solver leveraging the fast-forward modeling that comes from the fast calculation of the sensitivity matrix and compressed matrix techniques. Model tests and real data applications demonstrate that the proposed approach effectively resolves large-scale precise gravity inversion challenges.