Seismic data storage method and seismic data reading method

Abstract

The invention provides a seismic data storage method and a seismic data reading method. The seismic data storage method mainly comprises the following steps of distributed storage node and octree segmentation parameter configuration, block encoding and storage node distribution, segmentation of seismic data generation sub-blocks, transmission of the sub-blocks to the storage nodes, transmission ofindex files to the storage nodes, and loading and use of distributed seismic data. According to the seismic data storage method, the expenditure of a stand-alone storage space can be reduced, and thedata loss risk is reduced through backup; block storage of an octree structure is achieved; and the seismic data storage method has the advantages on file access speed for attribute calculation and three-dimensional rendering of similar areas.

Description

technical field [0001] The invention relates to the software development field of computer technology, in particular to a data storage method. Background technique [0002] With the development of seismic acquisition and electronic scanning technology, the magnitude of acquired seismic data increases rapidly. Although the performance of computers used to process data continues to improve, the explosive growth of data size still outpaces the development speed of memory. From storage, loading to display, it is necessary to re-examine the existing mainstream file storage formats, and develop a series of core technologies that support large data volumes such as rendering and big data mining. With the rapid data update brought by a series of new technologies such as 3D seismic data acquisition and oil and gas field development, the efficient storage, processing and analysis of 3D data volumes under the background of big data has become more and more complex. [0003] If we can ...

AI Technical Summary

Problems solved by technology

[0005] However, due to the method of directly storing the entire large data file, cluster-oriented large file access has high requirements for storage and execution environments, expensive network interconnection equipment, limited geographical connection distance, and requires professional maintenance and access. Speed ​​bottlenecks and other issues

In particular, a single large file I / O node not only becomes very slow due to a large amount of data exchange, but also has practical problems such as single point of failure and easy cluster paralysis

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