Waveform classification method based on dynamic time warping and partitioning algorithm

Abstract

The invention discloses a waveform classification method based on dynamic time warping and partitioning algorithm. A seismic data sample set is determined; according to types of seismic facies in the seismic data sample set, a classification number k of the seismic data sample set is determined; k samples are selected in the seismic data sample set to serve as an original clustering center; on the basis of a DTW distance, sample data which are not selected as the original clustering center are allocated to a cluster in which a corresponding centroid is located; the centroid of the cluster is iteratively updated; whether an iteration number upper limit is reached is judged, if yes, the operation is completed, and k clusters allocated finally are obtained; or, the fourth step is executed continuously according to the updated centroid of the cluster obtained in the fifth step, and re-allocation is carried out. The seismic data are aligned by adopting dynamic time warping, influences of horizon interpretation errors are reduced, and similarities of two channels of seismic data are measured more accurately; the centroid of the cluster is used for serving as the center of the cluster, and compared with the center of the cluster defined by the traditional partitioning algorithm, more accuracy is achieved, and features of the cluster can be better represented.

Description

technical field [0001] The invention belongs to the field of seismic data processing, and in particular relates to a seismic waveform classification technology. Background technique [0002] Seismic waveform is the basic nature of seismic data. It contains all qualitative and quantitative information, such as reflection mode, phase, frequency, and amplitude. reflect the characteristics of the underground structure. The waveform classification method is the most commonly used seismic facies analysis method. By classifying the seismic signal waveforms, the division of seismic facies can be realized. Waveform classification is aimed at seismic data sample sets containing various waveforms, and through appropriate classification or clustering methods, the samples are divided into different categories to achieve the purpose of distinguishing waveform samples. [0003] Waveform classification techniques are divided into cluster analysis and statistical classification. Cluster a...

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Problems solved by technology

However, existing methods for defining the center of a fa

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