Seismic Data Reconstruction Using Weighted Shallow and Deep Streamer Interpolation
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Solution Overview
Problem
Seismic surveys face challenges in reconstructing low-frequency data, particularly in marine environments, where shallow streamers struggle with low signal-to-noise ratios at frequencies below 7.5 Hz, while deep streamers excel at these frequencies but are less densely spaced, leading to incomplete data sets.
Innovation Solution
The technique involves interpolating seismic measurements by combining data from shallow and deep streamers, with greater weight assigned to deep streamer data for lower frequencies, using methods like Yen's interpolation algorithm and error filtering to enhance signal quality across the frequency spectrum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shallow streamers are used for seismic measurements, then high frequency data quality is maintained, but low frequency signal-to-noise ratio deteriorates
Solution Approach 1:
The patent combines data from both shallow streamers (which provide high frequency quality) and deep streamers (which provide low frequency quality) through an interpolation process. The system merges these two data sources to create a composite data set that achieves both high frequency precision and low frequency reliability simultaneously.
Solution Approach 2:
The patent applies different weighting strategies to different frequency components of the seismic data. Specifically, it uses high frequency data from shallow streamers with higher weight for high frequency reconstruction, while using low frequency data from deep streamers with higher weight for low frequency reconstruction, optimizing the quality characteristics for each frequency band locally.
2Reliability
If deep streamers are used for low frequency measurements, then low frequency signal-to-noise ratio improves, but spatial sampling density decreases
Solution Approach 1:
The patent merges the sparse low frequency data from deep streamers with the dense high frequency data from shallow streamers. By combining these two data sets and applying appropriate interpolation and weighting, the system recovers the spatial sampling density while preserving the low frequency signal quality.
Solution Approach 2:
The patent uses an interpolation algorithm as an intermediary process to bridge the gap between the sparse deep streamer data and the dense shallow streamer data. This intermediary process fills in the spatial gaps while maintaining the low frequency signal characteristics from the deep streamers.
3Ease of operation
If only shallow streamers are deployed, then data acquisition simplicity is maintained, but low frequency data completeness deteriorates
Solution Approach 1:
The patent performs preliminary processing of the shallow streamer data by interpolating and combining it with deep streamer data before final reconstruction. This preliminary action of data fusion and interpolation prevents loss of low frequency information while maintaining the operational simplicity of using primarily shallow streamers.
4Reliability
If only deep streamers are deployed, then low frequency data quality improves, but high frequency data quality deteriorates
Solution Approach 1:
The patent applies frequency-dependent weighting where deep streamer data is weighted more heavily for low frequency components while shallow streamer data is weighted more heavily for high frequency components. This local optimization of data quality for different frequency bands resolves the contradiction between low frequency reliability and high frequency precision.
Data Source
AI summary
A technique includes obtaining first data indicative of seismic measurements acquired by seismic sensors of a first set of towed streamers and obtaining second data indicative of seismic measurements acquired by seismic sensors of a second set of towed streamers. The second set of towed streamers is towed at a deeper depth than the first set of towed streamers. The technique includes interpolating seismic measurements based on the first and second data. The interpolation includes assigning more weight to the second data than to the first data for lower frequencies of the interpolated seismic measurements.