A Prestack Seismic Reflection Pattern Analysis Method Based on Tensor Discriminant Dictionary

Abstract

The invention discloses a pre-stack seismic reflection mode analysis method based on a tensor discriminant dictionary, comprising the following steps: S1, acquiring pre-stack seismic signals; S2, adding neighborhood gather information of target gathers, and target gather data Composing multi-dimensional pre-stack seismic data together; S3, making training data; S4, constructing the objective function, and formulating a classification strategy that is conducive to the classification of pre-stack seismic data on the basis of the discriminant tensor dictionary learning algorithm; S5, iteratively updating the model; S6. Put the pre-stack seismic data into the model trained in step S6, and obtain the reflection mode result of the target layer in the whole work area. The invention is based on Tucker tensor decomposition, can process multi-dimensional data well, better utilize multi-dimensional information of pre-stack seismic data, can have higher classification accuracy for pre-stack seismic reflection mode analysis, and is robust to noise better.

Description

technical field [0001] The invention belongs to the technical field of seismic signal analysis, in particular to a pre-stack seismic reflection mode analysis method based on a tensor discrimination dictionary. Background technique [0002] Seismic reflection pattern analysis is a very effective and important method for reservoir characterization. Seismic reflection pattern analysis mainly interprets reservoir distribution by classifying or clustering seismic waveforms or seismic attributes. At present, limited by the signal-to-noise ratio of seismic signals, one-dimensional seismic reflection pattern analysis of post-stack signals is one of the main methods of seismic phase analysis. Theoretically speaking, the stacking processing of pre-stack seismic signals at common reflection points can significantly improve the signal-to-noise ratio of seismic signals, but the stacking process loses a large number of seismic reflection amplitudes in the pre-stack seismic traces of comm...

AI Technical Summary

Problems solved by technology

[0006] (1) Limited by the dimensionality requirements of the input data, existing methods usually concatenate multi-dimensional pre-stack data into a vector form, which loses a lot of information that changes with offset or azimuth

[0007] (2) Existing methods can lead to poor results under low SNR pre-stack training data

[0008] (3) According to the sedimentology theory, the seismic facies has the feature of lateral continuity, which is not effectively considered in the existing methods

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