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| | Abstract | | We propose inversion algorithms to analyze tensor gravity data. Tensor gravity is directional derivatives of acceleration vector. When the Vp or density structure gradually varies with depth, it is difficult to obtain clear seismic reflection images. On the other hand, tensor gravity responds to gravity anomaly and it is much higher resolution than Bouguer or Free-air gravity anomaly. We developed two algorithms, inversion using hyper parameters and Laplacian eigenfunction. The latter algorithm has an advantage in no requirement on selection of hyper parameters. Eigen values fit from lower-order to higher order. It acts much quicker than hyper-parameter method. We evaluated the precision of solution, method of survey and distance to the target anomaly. We are planning the joint inversion with seismic data. |
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