Elastic Wave Data Interpolation and Extrapolation for Spatial Aliasing
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Solution Overview
Problem
In elastic wave exploration data processing, missing data due to mechanical issues, topographical restrictions, or expense limitations, combined with spatial aliasing from wide receiver spacing, complicates data processing.
Innovation Solution
A method employing machine learning to select and train interpolation and extrapolation models using elastic wave traces, restoring missing data and generating additional traces to fill gaps, thereby reducing data processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If wide receiver spacing is used in elastic wave exploration, then the area of coverage and cost efficiency are improved, but spatial aliasing occurs making data processing difficult
Solution Approach 1:
The patent applies preliminary action by performing interpolation and extrapolation processing on elastic wave data before subsequent data processing steps. Missing traces are restored and additional traces are generated in advance, so that subsequent processing can proceed without the complications of spatial aliasing and data gaps, thus reducing overall data processing complexity while maintaining wide receiver spacing benefits
2Loss of information
If machine learning models are trained to restore missing elastic wave traces, then data completeness is improved, but computational time and processing resources increase
Solution Approach 1:
The patent performs interpolation to restore missing traces and extrapolation to generate additional traces as preliminary processing steps. By completing these data restoration tasks before main processing, the system eliminates the need for repeated handling of incomplete data, thereby reducing total computational time despite the initial training cost
Solution Approach 2:
The patent creates copies of existing elastic wave traces through interpolation and extrapolation to reconstruct missing data and generate additional traces. This copying approach allows the system to recover complete datasets without requiring re-acquisition, balancing data completeness with computational efficiency
Data Source
AI summary
Provided is a method of processing elastic wave data, the method including selecting some elastic wave traces as a first label from among a plurality of elastic wave traces received without at least some elastic wave traces missing from whole elastic wave data, training an interpolation model on a machine learning basis by using at least two or more of remaining elastic wave traces except for the first label and the first label, restoring the at least some elastic wave traces missing from the whole elastic wave data by using the trained interpolation model, training an extrapolation model on a machine learning basis by using an elastic wave trace selected as a second label from among a plurality of elastic wave traces included in whole restored elastic wave data and at least two or more of remaining elastic wave traces except for the second label, and generating an additional elastic wave trace, which have not been included in the whole elastic wave data, by using the trained extrapolation model.