Earthquake attribute cluster method and apparatus

Abstract

The invention discloses an earthquake attribute cluster method and apparatus. The earthquake attribute cluster method employs earthquake attribute clustering based on a rapid K-mean value, a preclustering category number and earthquake attribute data to be clustered are input, category labels which the earthquake attribute data belongs to are output, and during processing, according to a triangle inequality principle, a part of unnecessary calculation for calculating the distance from each earthquake attribute data to a category center to update the category label which each earthquake attribute data belongs to for every cyclic iteration by use of a conventional K-mean value method is saved. Besides, the method can also comprise performing Gauss normalization processing on each earthquake attribute data before the earthquake attribute data to be clustered is input and rejecting wrongly recorded original time sequence signal abnormal values in the earthquake attribute data before the Gauss normalization processing is carried out. According to the invention, cluster analysis of batch earthquake attribute data can be finished within short time, and a basis for further detailed geological analysis is provided for explorers.

Description

technical field [0001] The invention relates to the technical field of geophysical exploration, in particular to a seismic attribute clustering method and device. Background technique [0002] In oil and gas exploration, only after a full understanding and familiarity with the underground geological conditions can we make a judgment on the oil and gas storage conditions in the exploration area. An important means of obtaining geological information is to analyze various seismic attribute data obtained after mathematical transformation of seismic data. Seismic attribute data are usually pre-stack or post-stack seismic data, parameters such as geometric shape, kinematic characteristics, and dynamic characteristics of seismic waves. Through the study of these parameters, the structure, lithology, and fluid characteristics of the underground medium in the exploration area can be obtained, and then the oil and gas storage information can be inferred. The process of inferring su...

AI Technical Summary

Problems solved by technology

But the K-means algorithm is very slow when clustering massive seismic data

SOM (Self Organizing Feature Maps, self-organizing feature map neural network) attribute clustering does not need to specify the number of clusters in advance, the number of clusters and the clustering results are output together as the result, but it requires a high storage space cost and gives The result is also slower, usually slower than the original K-means method, especially for the clustering of millions or more seismic attributes, which cannot be completed by computers with general hardware configurations

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