Method and system for improving seismic data resolution based on weak supervision generative adversarial network

Abstract

The invention discloses a method and a system for improving seismic data resolution based on a weak supervision generative adversarial network. The method comprises the following steps of: performing normalization processing on two pieces of three-dimensional seismic data belonging to different work areas; dividing a training set and a test set; obtaining a training sample pair in the training area in a random extraction mode; sending the low-resolution seismic data into a forward generator; sending the output of the forward generator to a reverse generator; sending the output of the forward generator and the corresponding high-resolution label to a discriminator for discrimination; and alternately training the generator and the discriminator, continuously updating network parameters until the model converges, and after training is finished, sending the whole block of low-resolution seismic data into the forward generator for testing to obtain a final high-resolution result. According to the method, the distribution characteristics of the high-resolution seismic data can be learned on the premise that paired input and labels do not exist, and the high-frequency information of the original seismic data can be accurately and effectively recovered.

Description

technical field [0001] The invention belongs to the technical field of seismic exploration data processing, and in particular relates to a method for improving the resolution of seismic data based on a weakly supervised generation confrontation network. Background technique [0002] During the underground propagation of seismic wavelet, due to the influence of stratum absorption, inelastic attenuation and interlayer reflection, the high-frequency components are rapidly attenuated, the wavelet waveform is broadened, and the resolution is reduced. Improving the resolution of seismic data is of great significance for accurately interpreting stratigraphic structures. [0003] Existing technology 1: traditional methods: traditional methods for improving the resolution of seismic data mainly include deconvolution, spectral whitening, and inverse Q filtering. These methods need to meet some assumptions and have certain limitations. For example, the spectral whitening method assume...

AI Technical Summary

Problems solved by technology

However, these methods are strongly supervised and require high-resolution results obtained by traditional methods as labels. Low-resolution data must correspond to high-resolution labels one-to-one. The effect of network learning is also limited by the quality of labels.

Due to the limitations of traditional methods, it is very difficult to obtain a good label.

Images