Pre-stack seismic inversion method for hybrid drive regularization

Abstract

The invention relates to a pre-stack seismic inversion method based on hybrid drive regularization, and belongs to the technical field of geophysical inversion. The method comprises the following steps: 1) acquiring data, and inputting an observation data set; 2) carrying out unconstrained inversion on the data to obtain an initial inversion result set; 3) calculating the spatial gradient of the initial inversion result in the step 2); 4) classifying the spatial gradients in the step 3) to obtain two types of spatial positions of the spatial gradients; 5) calculating a distribution characteristic function of a to-be-inverted result according to the initial inversion result in the step 2) and the position information in the step 4); 6) solving a regularization item according to the initial inversion result in the step 2) and the distribution characteristic function in the step 5); and 7) solving the objective function to obtain a final result. Regularization constraint is carried out on the inversion process by simultaneously utilizing the characteristics of the to-be-inverted parameters and the model hypothesis of the inversion error, so that the stability of the inversion result is improved, and the detail characteristics of the inversion result are protected at the same time.

Description

Technical field[0001]The present invention relates to a superpositional seismic inversion method of hybrid driving regularization, which belongs to the technical field of geophysical inversion.Background technique[0002]The pre-stack of earthquake inversion is based on the relationship between the variation of the groundbroad, the ground, and is an important technique for obtaining underground gathelling parameters. This technique can simultaneously obtain elastic parameters such as longitudinal wave velocity, horizontal wave velocity, and density of the underground medium, and have been widely used in the field of exploration seismic. Multi-parameter inversion of pre-stacking seismic data is a highly nonlinear unspecified problem. The minor changes in data will result in a huge change in solution; at the same time, in addition, there is noise in the observation data, which further reduces the results of inversion stability. The details in the inversion result are covered by noise, w...

AI Technical Summary

Problems solved by technology

In the regularized inversion process, the smoothness of the parameters to be inverted is usually used as prior information to constrain the inversion results, but the traditional smoothness constraints will destroy the parameters to be inverted while improving the stability of the inversion results If you want to keep the detailed information of the inversion results, it may be difficult to effectively suppress the inversion noise

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