Method for seismic data reconstruction based on texture constraint U-net network

Abstract

The invention discloses a seismic data reconstruction method based on a texture constraint U-net network, and the method comprises the steps: S1, segmenting a training data set through a K-means algorithm, and obtaining a texture label of training data; S2, training a U-net network as a texture extraction network through the training data set and the texture label to obtain optimized texture extraction network parameters; S3, connecting the trained texture extraction network with a reconstruction network in series, extracting texture information of label data and reconstruction seismic data byadopting optimized texture extraction network parameters, and obtaining texture loss; and S4, reconstructing the network through texture loss optimization, and then reconstructing the seismic data. The characteristic that the seismic data has rich texture information is combined, and the reconstruction accuracy and continuity of the seismic event can be improved under the condition of limited samples by strengthening texture learning.

Description

technical field [0001] The invention belongs to the technical field of using artificial intelligence for seismic data processing, and relates to a seismic data reconstruction method, in particular to a texture-constrained U-net network-based seismic data reconstruction method. Background technique [0002] Seismic exploration is an important method for studying underground geological structures. However, due to surface obstacles, terrain constraints such as mountains and rivers, and bad and waste roads during the acquisition process, the acquired seismic data is usually under-sampled along the spatial direction, which directly affects subsequent migration imaging, inversion and Interpretation and description of geological formations. Therefore, it is of great practical significance to interpolate and reconstruct the missing seismic data to obtain data with high signal-to-noise ratio, high resolution and high fidelity. [0003] At present, seismic data reconstruction method...

AI Technical Summary

Problems solved by technology

The research has achieved better results than the f-x method, but at the same time the paper also pointed out the limitations of this method: data that is not highly similar to the training data, the reconstruction effect is not good

[0005] These research results demonstrate the great potential of DL in the field of seismic data interpolation, but also expose an important problem: the requirement of huge training samples and the difficulty of network generalization

That is to say, if the network test effect is to be good, a large amount of labeled training data is required. If the training data is not large enough, the traditional DL network cannot achieve good generalization performance.

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