Seismic Surface Wave Filtering via Spatial Velocity Characterization
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Solution Overview
Problem
Current seismic data processing methods fail to accurately separate surface waves from body waves due to incomplete separation of velocities and frequencies, aliasing, and spatial variability of surface wave velocities, leading to incomplete removal of surface waves and potential harm to signal reflections.
Innovation Solution
The method involves characterizing the spatial variability of surface wave velocities across a seismic survey area, estimating local dispersion curves, and applying filtering criteria to remove surface waves, which accounts for changes in velocities as a function of 2-D space and frequency, allowing for precise separation of surface and body waves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If classical Fourier or dip-separation methods are used to remove surface waves, then surface wave removal is achieved, but reflector energy is removed along with surface wave energy due to incomplete separation between velocities and frequencies
Solution Approach 1:
The patent applies local quality by estimating surface wave dispersion curves at multiple specific locations across the survey area and using these location-specific curves for filtering. This allows the filtering characteristics to vary spatially, adapting to local variations in surface wave properties while preserving reflector energy that has different propagation characteristics at each location.
Solution Approach 2:
The patent implements dynamics by making the filtering criteria adaptive through spatial variation. Instead of using a single static filtering parameter, the system dynamically adjusts the dispersion curve estimation and filtering characteristics based on the specific location and local surface wave properties, enabling more precise separation of surface waves from body waves.
2Object-generated harmful factors
If Fourier methods are used for surface wave filtering, then surface waves can be separated from body waves, but aliasing occurs making it difficult to remove surface waves accurately
Solution Approach 1:
The patent applies preliminary action by performing dispersion curve estimation at multiple locations before applying the filtering process. This preliminary characterization of surface wave properties at specific points allows the system to understand the local surface wave behavior and use this information to guide the filtering, avoiding aliasing issues that arise when applying uniform filtering without prior local analysis.
3Object-generated harmful factors
If adaptive filtering is used to maximize noise suppression, then surface waves are reduced, but the method requires a priori assumptions about body wave and surface wave propagation characteristics
Solution Approach 1:
The patent implements self-service by having the system automatically estimate surface wave dispersion curves from the seismic data itself at multiple locations, without requiring external input or a priori assumptions about wave propagation characteristics. The system uses the data to characterize its own properties and generate appropriate filtering criteria, eliminating the need for complex manual setup while maintaining effective surface wave suppression.
4Productivity
If surface wave filtering is applied without accounting for spatial variability, then processing is simplified, but filtering accuracy decreases due to velocity variations across the survey area
Solution Approach 1:
The patent applies local quality by estimating surface wave dispersion curves at multiple specific locations across the survey area and using these location-specific curves for filtering. This allows the filtering characteristics to vary spatially, adapting to local variations in surface wave properties while preserving reflector energy that has different propagation characteristics at each location.
Solution Approach 2:
The patent implements segmentation by dividing the survey area into multiple locations and estimating surface wave properties at each segment. This segmentation allows the system to handle spatial variability by processing different regions with their specific local characteristics, improving overall filtering accuracy while maintaining computational efficiency through targeted analysis at key locations.
Data Source
AI summary
Embodiments use seismic processing methods that account for the spatial variability of surface wave velocities. Embodiments analyze surface wave properties by rapidly characterizing spatial variability of the surface waves in the seismic survey data (302). Filtering criteria are formed using the spatial variability of the surface waves (204). The filtering criteria can then be used to remove at least a portion of the surface waves from the seismic data (206, 319). The rapid characterization involves estimating a local group velocity of the surface waves by cross-correlation of the analytic signals (302).