A Fast Seismic Waveform Classification Method Based on Semi-Supervised Algorithm

Abstract

The invention discloses a method for quickly classifying seismic waveforms based on a semi-supervised algorithm, comprising the following steps: S1, obtaining seismic waveform data along horizons, and adopting an SSDR algorithm based on linear transformation to reduce the dimensionality of seismic waveform data; S2, using seismic The label data in the waveform data trains a distance measurement matrix; S3, using the semi-supervised Kmeans classification algorithm to classify the seismic waveform data to generate a seismic phase map. The invention uses a semi-supervised dimension reduction method to process the original seismic waveform data, eliminates redundant data, increases the similarity of the same type of waveform data, and enhances the differences of different types, making the classification results more accurate; using the existing A suitable distance measurement matrix is ​​trained from the well logging data, and the distance measurement method is introduced into the subsequent classification method, and then a weighted semi-supervised Kmeans classification method is proposed, which makes full use of the well logging data and improves the Classification accuracy, and speed up the classification rate.

Description

technical field [0001] The invention belongs to the technical field of seismic data analysis, in particular to a fast seismic waveform classification method based on a semi-supervised algorithm. Background technique [0002] Energy is an indispensable material basis and important guarantee for economic development and social progress. In recent years, the economy has become more and more dependent on energy, and the demand for energy has continued to grow. In order to maintain a stable supply of energy, the oil and gas industry needs to continuously improve the technology of oil and gas reservoir exploration. During the exploration of subtle oil and gas reservoirs, it is very important to use the rich information contained in seismic data to identify sedimentary facies belts for the prediction of subtle oil and gas reservoirs. In the petroleum industry, the method of identifying sedimentary facies using seismic data is called seismic facies identification. The traditional ...

AI Technical Summary

Problems solved by technology

The commonly used dimensionality reduction algorithms PCA and LLE are both unsupervised dimensionality reduction. While reducing redundant seismic waveform data, they can also suppress some noise in seismic waveforms, but usually also make seismic waveforms of different seismic phases very different. are similar, and are finally classified into the same category during the classification process, resulting in inaccurate classification results

[0005] The existing seismic waveform classification methods are all unsupervised classification methods, these methods are driven by the data itself, without taking into account the prior knowledge of drilling, logging, geology, etc., and cannot be combined with the actual logging results

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