Seismic Data Interpolation Method Combining Gabor Feature Extraction and Support Vector Regression

Abstract

The invention discloses a seismic data interpolation method combining Gabor feature extraction and support vector regression. The method includes sequentially executing pre-interpolation, Gabor feature extraction, feature vector extraction, training regression function and reconstructing seismic images. Pre-interpolate the missing seismic image that lacks seismic traces to complete, obtain a low-resolution seismic image, and then perform Gabor filtering on this low-resolution seismic image to obtain a feature image, and then use the low-resolution seismic image and feature image transformation to obtain Predict the feature vector, and then train the regression function through the training feature vector and label, and finally input the predicted feature vector into the trained regression function, and reconstruct the seismic image through regression reconstruction and transformation. The invention designs a seismic data interpolation algorithm combining Gabor filtering and SVR, fully utilizes the feature image obtained by Gabor feature extraction and the regression reconstruction ability of SVR, and obtains clear and complete seismic images.

Description

technical field [0001] The invention belongs to the field of seismic data processing, in particular to a seismic data interpolation method combined with Gabor feature extraction and support vector regression. Background technique [0002] Seismic exploration is the most effective exploration method in petroleum exploration, but field seismic data acquisition is affected by complex geological conditions and acquisition environment, which tends to make the acquired seismic data incomplete or irregularly distributed. Seismic data interpolation technology is used to solve the negative impact caused by insufficient spatial sampling in the process of data acquisition. A dense seismic record is necessary for many subsequent seismic processing steps. [0003] At present, traditional seismic data interpolation methods are mainly divided into the following categories: prediction-based methods, such as Spitzs f-x prediction filtering method; low-rank-based methods, such as singular sp...

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Problems solved by technology

Sparse transformation-based methods can only achieve good performance in certain sparse domains

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