Window-Based Predictor Selection for Seismic Data Compression
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Solution Overview
Problem
Current seismic data compression techniques are inadequate for efficiently processing and storing the increasing volumes of complex geological data, as they fail to maximize compression ratios and processing speeds effectively.
Innovation Solution
The method employs window-based selection from multiple prediction functions to compress floating-point data using a combination of default and alternate prediction algorithms, determining the best predictor set based on performance metrics and encoding residuals for improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression techniques are used on seismic data, then processing and storage are simplified, but compression ratios and processing speeds are insufficient
Solution Approach 1:
The patent segments the data processing into distinct phases: prediction phase (generating predicted values from previous data points), residual calculation phase (computing differences between actual and predicted values), and encoding phase (compressing the residuals). This segmentation allows each phase to be optimized independently, achieving high compression ratios while maintaining manageable algorithmic complexity.
Solution Approach 2:
The patent applies prediction algorithms in advance to generate predicted values before the actual compression process. By performing this preliminary action, the residual values (differences between actual and predicted data) become smaller and more compressible, significantly improving the overall compression ratio without proportionally increasing complexity.
2Quantity of substance
If data volume increases to handle complex geologies, then data accuracy is maintained, but storage and processing costs increase
Solution Approach 1:
The patent changes the representation parameters of the data by transforming original seismic data into residual values through prediction. This parameter transformation reduces the entropy and variability of the data, allowing for more efficient storage and processing while maintaining the same information content and accuracy.
Solution Approach 2:
The patent extracts and removes the predictable components from the seismic data by using prediction algorithms. Only the unpredictable residual portions need to be stored and processed, effectively separating the redundant information from the essential information, thereby reducing storage and processing requirements.
3Productivity
If multiple prediction algorithms are evaluated, then compression efficiency is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by evaluating multiple prediction algorithms but only selecting and applying the best-performing one for each data segment. This approach achieves the benefits of multiple algorithm evaluation (improved compression efficiency) while avoiding the full computational cost of implementing all algorithms simultaneously, thus reducing overall computational overhead.
Data Source
AI summary
Data compression with window-based selection from multiple prediction functions is provided. A predefined default predictor and a plurality of other predictors are applied to a floating point number to generate a plurality of predictions. A compression metric over a collection of floating point numbers is evaluated for the default predictor and the plurality of other predictors. Based on the compression metric, (i) the floating point number is encoded using the predefined default predictor, or (ii) the collection of floating point numbers is encoded using one of the other predictors. Stored indexes indicate which predictor was used for the encoding. A set of predictors out of a larger set of predictors can be determined for a specific data set based on a performance-based ranking. The default predictor and the alternate predictors can be represented as ensembles of predictors. Decompression involves evaluating which predictor was used for the encoding and optionally, whether an ensemble was used for the encoding.