Nonlinear Beamforming for 3D Prestack Seismic Data Enhancement
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Solution Overview
Problem
3D prestack land seismic data is often corrupted by near-surface and overburden conditions, leading to challenges in enhancing signal quality and noise suppression, especially with modern high-channel count sensor systems.
Innovation Solution
The method involves sorting seismic data into cross-spread gathers, applying forward normal-moveout corrections, estimating beamforming parameters for nonlinear traveltime surfaces, performing beamforming by local stacking, and applying inverse NMO corrections to enhance primary reflections while suppressing unwanted events, all without relying on classical assumptions about hyperbolicity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If classical hyperbolicity assumptions are used for stacking, then processing is simplified, but accuracy deteriorates in complex geology with abrupt velocity changes
Solution Approach 1:
The patent applies dynamics by transitioning from static hyperbolic stacking assumptions to dynamic nonlinear beamforming. The method estimates nonlinear traveltime surfaces adaptively for each cross-spread gather, allowing the stacking process to respond to actual complex velocity structures rather than relying on fixed hyperbolic models. This dynamic approach maintains processing feasibility while significantly improving accuracy in complex geology.
Solution Approach 2:
The patent changes the fundamental parameters of the stacking process by replacing hyperbolic moveout assumptions with nonlinear traveltime surfaces. Instead of using constant velocity models, the method estimates variable velocity parameters locally in the cross-spread domain, enabling accurate stacking even when velocity changes abruptly due to faults, folds, or salt bodies.
2Quantity of substance
If high-channel count sensor systems are used, then data quantity increases, but signal-to-noise ratio deteriorates due to near-surface and overburden conditions
Solution Approach 1:
The patent segments the seismic data into cross-spread gathers, organizing traces by source and receiver positions in a transformed domain. This segmentation allows the method to process and enhance specific subsets of data independently, improving the ability to distinguish signal from noise in the presence of near-surface and overburden conditions while utilizing the full high-channel count data volume.
Solution Approach 2:
The patent introduces cross-spread gathers as an intermediary representation between the raw seismic data and the final enhanced output. This intermediate domain facilitates nonlinear beamforming and traveltime surface estimation, enabling effective signal enhancement and noise suppression before final stacking, thus improving signal-to-noise ratio while preserving the benefits of high data quantity.
3Reliability
If nonlinear beamforming is applied, then signal enhancement improves, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into distinct stages: sorting data into cross-spread gathers, estimating beamforming parameters, performing local stacking, and applying inverse NMO corrections. This segmentation allows each stage to be optimized independently and enables parallel processing, reducing overall computational complexity while maintaining the signal enhancement benefits of nonlinear beamforming.
Solution Approach 2:
The patent transforms the data into the cross-spread domain, adding a new dimensional perspective that simplifies the beamforming process. By working in this transformed domain rather than directly in the original seismic domain, the method reduces computational complexity while achieving superior signal enhancement through nonlinear beamforming.
Data Source
AI summary
The disclosure provides systems and methods to enhance pre-stack data for seismic data analysis by: sorting the reflection seismic data acquired from cross-spread gathers into sets of data sections; performing data enhancement on the sets of data sections to generate enhanced traces by: (i) applying forward normal-moveout (NMO) corrections such that arrival times of primary reflection events become more flat, (ii) estimating beamforming parameters including a nonlinear traveltime surface and a summation aperture, (iii) generating enhanced traces that combine contributions from original traces in the sets of data sections, and (iv) applying inverse NMO corrections to the enhanced traces such that temporal rearrangements due to the forward NMO corrections are undone.