Seismic Data Deblending via Multi-Stage Sparse Inversion
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Solution Overview
Problem
Simultaneous source shooting in seismic data acquisition faces challenges in separating sources and eliminating interference noise, leading to underdetermined deblending problems that require additional assumptions and regularization.
Innovation Solution
A multi-stage inversion method using sparse inversion and nonmonotone alternating direction methods to iteratively deblend seismic data, incorporating optimization models and restriction operators to separate and reconstruct interference-free records, allowing for joint deblending and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple sources are activated simultaneously to shorten acquisition time, then productivity increases, but source interference and blending noise increase
Solution Approach 1:
The patent applies segmentation by dividing the blended seismic signal into individual source components through deblending algorithms. The received blended signal is segmented into contributions from different sources using sparse inversion and iterative reweighted least squares methods, allowing separation of interfering sources while maintaining the productivity benefits of simultaneous shooting.
Solution Approach 2:
The patent converts the harmful blending noise into useful information by using the interference patterns themselves as constraints in the deblending process. The known source waveforms and timing information are used to model the blending process, and this model is then inverted to separate the sources, turning the previously harmful interference into a structured problem that can be solved mathematically.
2Measurement precision
If traditional seismic exploration methods use higher fold to improve data quality, then measurement precision increases, but acquisition cost and time increase
Solution Approach 1:
The patent changes the fundamental parameter of data acquisition from traditional single-source sequential shooting to multi-source simultaneous shooting. This parameter change allows achieving the same or better data quality with fewer total shots and reduced acquisition time, as the deblending process recovers signals that would traditionally require multiple passes to obtain.
Solution Approach 2:
The patent replaces the mechanical approach of increasing fold (more physical passes over the same area) with a computational approach using sparse inversion and compressed sensing algorithms. Instead of physically acquiring more data through repeated measurements, the system uses mathematical optimization to recover high-quality signals from fewer, simultaneously acquired measurements.
3Reliability
If deblending problem is solved using traditional methods with extra assumptions and regularization, then a unique solution can be obtained, but device complexity and processing complexity increase
Solution Approach 1:
The patent applies dynamics by using iterative algorithms that adaptively adjust processing parameters during the deblending process. The iterative reweighted least squares method dynamically updates the weight matrix based on the current solution estimate, allowing the processing complexity to be managed through adaptive refinement rather than static, overly complex preprocessing assumptions.
Data Source
AI summary
A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.