Wavelet Cross-Correlation for Seismic Virtual Source Denoising
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Solution Overview
Problem
Virtual source redatuming in seismic data processing is hindered by noise degradation due to multiples, scattering waves, and ground-roll noises, which affects the quality of the data and requires complex pre-processing steps.
Innovation Solution
The method employs wavelet cross-correlation in the time-frequency and time-frequency-wavenumber domains to separate seismic components, using soft-threshold filtering and inverse wavelet transformation to suppress noise and enhance signal resolution without requiring a near-surface model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual source redatuming is performed using conventional cross-correlation methods, then the processing can be implemented, but the data quality is degraded by noise from multiples, scattering waves, and ground-roll
Solution Approach 1:
The patent transforms the cross-correlation operation from the conventional time domain to the time-frequency-wavenumber (t-f-k) domain by applying wavelet transform in the time-frequency domain and then Fourier transform in the wavenumber domain. This dimensional transformation enables selective filtering of noise components (multiples, scattering waves, ground-roll) while preserving signal components, thereby improving data quality without requiring complex pre-processing steps
2Productivity
If conventional virtual source redatuming is performed, then processing can proceed, but complex pre-processing steps are required to mitigate noise
Solution Approach 1:
The patent merges the denoising function into the virtual source redatuming process itself by performing cross-correlation in the t-f-k domain. The wavelet cross-correlation operation inherently separates signal from noise through its time-frequency localization properties, eliminating the need for separate pre-processing steps such as multiple elimination, ground-roll filtering, and scattering wave mitigation that are required in conventional approaches
3Reliability
If wavelet cross-correlation in t-f-k domain is performed, then noise is effectively attenuated and data quality is improved, but computational complexity increases
Solution Approach 1:
The patent segments the seismic data processing into distinct transform domains (time domain, time-frequency domain, time-frequency-wavenumber domain) and applies appropriate filtering operations in each. The wavelet transform segments the signal in time-frequency space, enabling selective attenuation of noise components while preserving signal components, thereby achieving high data quality with manageable computational complexity through structured domain decomposition
Data Source
AI summary
Seismic shot gather data is received from a computer data store for processing. The received seismic shot gather data is separated into downgoing and upgoing wavefields, a time-frequency-wavenumber (t-f-k) three-dimensional (3D) data cube comprising multiple time-frequency (t-f) slices is formed. The downgoing wavefields are wavelet transformed from a time (t) domain to a t-f domain and the upgoing wavefields are wavelet transformed from the t domain to the t-f domain. A wavelet cross-correlation is performed between the downgoing wavefields in the t-f domain and the upgoing wavefields in a t-f-k domain to generate wavelet cross-correlated data. Soft-threshold filtering if performed for each t-f slice of the t-f-k 3D data cube. An inverse wavelet transform is performed to bring wavelet cross-correlated data from the t-f-k domain to a time-receiver (t-x) domain. All seismic shots of the received seismic shot gather data are looped over and the wavelet cross-correlated data is stacked as a virtual source gather.