Full-tensor gravity gradiometer data quality evaluation and filtering processing method

Abstract

The invention provides a full-tensor gravity gradiometer data quality evaluation and filtering processing method. The method comprises the following steps: carrying out noise estimation on each gradient component measured by a full-tensor gravity gradiometer through an odd-even grid analysis method; constructing a weighted joint inversion filtering equation according to weight of each gravity gradient component determined by a noise estimation result and a Fourier series solution of a Laplace equation; solving a Fourier series coefficient in the weighted joint inversion filtering equation by utilizing an optimal linear inversion method; and carrying out forward calculation by using the Fourier series coefficient obtained by calculation to obtain a filtering result. According to the full-tensor gravity gradiometer data quality evaluation and filtering processing method provided by the invention, effective high-frequency effective gravity gradient information can be reserved while a high-frequency noise of measurement data of the full-tensor gravity gradiometer is removed, and precision and a resolution of filtered result data are remarkably improved.

Description

technical field [0001] The invention relates to the technical field of gravity gradient data processing, in particular to a full tensor gravity gradiometer data quality evaluation and filtering processing method. Background technique [0002] The main advantage of the full tensor gravity gradient survey over the traditional gravity survey is that it can obtain the gravity gradient component information in six different directions at the same time, so as to reflect the same underground geological target from different directions. [0003] Each gravity gradient component satisfies the constraints of the Laplace equation that the gravitational potential satisfies, has internal consistency, and has the advantages of high resolution and high precision, and the high-frequency signal components included can better describe small anomaly features . [0004] At present, the quality evaluation of the full tensor gravity gradient data mainly uses the Laplacian property satisfied by th...

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Problems solved by technology

In addition, the inversion filtering method combined with multiple gravity gradient components is also used for corresponding data processing, but they are all based on plane grid data. It needs to be leveled and gridded in advance, so as to introduce data post-processing errors, such as leveling error and grid difference error, etc.

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