Joint inversion of full-tensor gravity gradiometry data based on source growing

Three-dimensional inversion based on source growing uses systematic searches. Compared with the regularization inversion, this method has lower computational requirements and faster processing speed. The criteria determining the source growth is crucial for the quality of the results. This study proposes an inversion based on source growing with full-tensor gravity gradiometry data to improve vertical inversion effectiveness. First, a depth weighting function is introduced for the criteria to optimize the determination of source growing at different depths. Second, the weights of different data are adjusted based on the inversion results of single-component gradient data, and a joint inversion method is established. Finally, matrix compression reduces memory occupation and improves computational efficiency. By the tests of synthetic data and real data from Vinton Dome, it is demonstrated that the proposed method can effectively guide source growing, providing stronger ability for distinguishing deep targets and being suitable for the inversion of complex-shaped targets. Furthermore, the method has high computational efficiency and anti-noise ability.