Seismic Data Chunk Compression via Distributed Context
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Solution Overview
Problem
Current data compression techniques for seismic data in distributed computing environments face challenges in achieving high compression ratios while maintaining reliability and efficiency, particularly in processing large seismic datasets that require parallel access and independent compression of file chunks without degrading compression performance.
Innovation Solution
The method involves dividing seismic files into chunks, distributing them across computing nodes for independent content-aware compression and decompression, using a compression descriptor to store global information and enable massive parallelism, and employing algorithms like minimal-length and alignment compression to process headers and samples efficiently, allowing transparent access to compressed data as if it were uncompressed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If seismic data is compressed using conventional techniques in distributed computing environments, then processing and storage efficiency is improved, but compression ratios degrade and reliability decreases
Solution Approach 1:
The patent divides seismic data files into multiple independent chunks, each of which can be compressed separately using conventional techniques. This segmentation allows distributed computing nodes to process different chunks in parallel, improving processing efficiency while maintaining the ability to achieve high overall compression ratios through the coordination of multiple compression passes and the use of a file context that preserves global data patterns across chunks.
2Speed
If file chunks are compressed independently in parallel, then processing speed is improved, but compression performance degrades
Solution Approach 1:
The patent performs a preliminary analysis pass over the entire seismic data file to build a file context that captures global patterns and statistics. This preliminary action enables subsequent parallel compression of individual chunks to achieve high compression ratios without requiring repeated full-file analysis, thus maintaining both processing speed and compression performance.
Solution Approach 2:
The patent implements a feedback mechanism where compression results from individual chunks are aggregated and used to refine the overall compression strategy. The file context is updated based on chunk compression outcomes, allowing the system to adapt and optimize compression parameters across distributed nodes, ensuring high overall compression ratios are achieved through coordinated parallel processing.
3Volume of stationary object
If distributed compression is implemented, then storage scalability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal file context structure that serves multiple functions: it stores global patterns for compression optimization, maintains metadata for distributed coordination, and provides reference information for decompression. This multi-functional design reduces system complexity by eliminating the need for separate management structures for each function, while still enabling scalable distributed compression across multiple storage nodes.
4Quantity of substance
If sequential compression methods are used, then compression ratios are maintained, but processing time increases
Solution Approach 1:
The patent segments the seismic data file into multiple independent chunks that can be compressed in parallel using conventional techniques. By dividing the overall compression task into smaller parallelizable units while maintaining a global file context for coordination, the system achieves compression ratios equivalent to sequential methods but with significantly reduced processing time through parallel execution across multiple computing nodes.
Data Source
AI summary
Distributed content-aware compression and decompression techniques are provided for data, such as seismic data. Data is compressed by obtaining a file chunk of a plurality of file chunks of a larger file, a start offset of the file chunk, and a data descriptor indicating how the file chunk should be processed based on characteristics of the larger file. Headers in the file chunk are compressed using a substantially lossless header compression technique to obtain compressed headers. Samples in the file chunk are compressed using a substantially lossless sample compression technique to obtain compressed samples. The compressed headers and compressed samples are packed into a single bit stream comprising a compressed version of the file chunk. The compression can be performed in a physical or logical layer of storage nodes of a file system or in compute nodes of a computing cluster. The compression can be executed on demand by an external agent and/or in a background mode by a storage operating system.