Surface-Wave Noise Removal in Seismic Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional seismic data processing methods fail to effectively remove surface-wave noise without damaging reflection signals, particularly in complex and laterally varying subsurface environments, limiting the ability to determine detailed physical structures and properties of subsurface earth regions.
Innovation Solution
A method that predicts and subtracts surface-wave noise by estimating surface-consistent transfer functions in the frequency domain, which characterize the filtering effects of surface-wave propagation, allowing for the separation of surface-wave noise from reflection signals without requiring fine spatial sampling or assuming simple ground-roll behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional digital signal-processing filters are applied to remove surface-wave noise, then noise reduction is achieved, but reflection signal integrity deteriorates
Solution Approach 1:
The patent applies preliminary surface-wave attenuation before migration to reduce noise that would otherwise migrate into reflection signals. By removing surface waves early in the processing sequence, the subsequent migration step operates on cleaner data, preventing noise contamination of the final image while preserving reflection signal integrity.
Solution Approach 2:
The patent segments the processing sequence into distinct stages: surface-wave attenuation followed by migration. This segmentation allows each step to address specific aspects of the data independently, enabling effective noise removal without compromising the reflection signals that will be imaged in the subsequent migration step.
2Object-affected harmful factors
If conventional filtering methods are used to remove surface waves, then noise is reduced, but imaging quality deteriorates
Solution Approach 1:
Surface-wave attenuation is performed as a preliminary step before migration. This timing is critical because it removes noise that would otherwise be migrated into the final image and contaminate the reflection signals. The preliminary removal preserves imaging quality by ensuring clean data enters the migration process.
Solution Approach 2:
The patent employs dynamic, laterally varying attenuation parameters that adapt to local surface-wave characteristics. This dynamic approach maintains accuracy across complex subsurface environments where surface-wave behavior changes laterally, ensuring high imaging quality throughout the survey area rather than relying on uniform filtering.
3Ease of operation
If uniform attenuation parameters are applied across the survey area, then processing simplicity is maintained, but accuracy deteriorates in complex environments
Solution Approach 1:
The patent implements local quality by allowing attenuation parameters to vary laterally across the survey area. Each location can have optimized parameters tailored to its specific surface-wave characteristics, improving attenuation accuracy in complex environments. The system maintains ease of operation through automated parameter estimation from the data itself, eliminating the need for manual parameter specification at each location.
Data Source
AI summary
The invention is a method to predict surface-wave waveforms (306) and subtract them (307) from seismic data. Prediction is done by estimating a set of surface-consistent components (transfer functions in the frequency domain or impulse responses in time domain) that best represent changes in the waveforms for propagation along the surface from source to receiver (303). The prediction uses a mathematical expression, or model, of the earth's filtering effects, both amplitude and phase, as a function of frequency. The desired surface-consistent components are model parameters, and model optimization is used to solve for the surface-consistent components. The surface-consistent components may include filter transfer functions for each source location, each receiver location, and for propagation (302) through each region (301) of the surface that exhibits lateral variation.