Adaptive Seismic Filter Ground Roll Attenuation
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Solution Overview
Problem
Existing seismic data filtering methods struggle to effectively remove ground roll interference, which often coincides with reflections from deep earth layers, leading to masked seismic signals and inefficient data utilization.
Innovation Solution
A computer-implemented adaptive filtering method using a plurality of filter channels with adaptive weights and time windows, configured to minimize a cost function that separates ground roll contributions from reflected signal components and random noise components, allowing for improved attenuation of ground roll in seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional filtering methods are used to remove ground roll, then ground roll attenuation is achieved, but reflected signals from deep layers are also attenuated or distorted
Solution Approach 1:
The patent applies adaptive filtering where filter coefficients are dynamically adjusted based on the statistical properties of the input signal. The filter transitions from static to dynamic operation, allowing it to adapt to changing signal characteristics and differentiate between ground roll and reflected signals in real-time, thus attenuating ground roll while preserving reflected signals.
Solution Approach 2:
The patent changes the parameters of the filtering process by using adaptive algorithms that modify filter coefficients based on signal statistics. Instead of fixed filtering parameters, the system continuously adjusts parameters like filter weights and time windows to optimize the separation between ground roll and reflected signals, improving both ground roll attenuation and signal preservation.
2Object-affected harmful factors
If adaptive filtering with multiple parameters is applied to improve ground roll removal, then filtering performance is enhanced, but computational complexity and processing time increase
Solution Approach 1:
The patent implements adaptive filtering with a finite number of taps and uses truncated data records for adaptation. Instead of processing infinite data or using excessively complex algorithms, the system applies partial action by limiting the adaptation window and filter order, achieving sufficient ground roll attenuation without overwhelming computational requirements.
Solution Approach 2:
The patent optimizes computational complexity by carefully selecting and adjusting parameters such as the number of filter taps, data record length for adaptation, and threshold values. These parameter changes balance filtering performance with processing efficiency, making the adaptive filter practical for real-time seismic data processing.
3Measurement precision
If extensive seismic data is collected for offline analysis, then signal quality can be improved through post-processing, but data storage requirements and processing time increase
Solution Approach 1:
The patent performs ground roll attenuation in real-time during data acquisition using adaptive filtering. Instead of collecting extensive data for later offline processing, the system applies preliminary filtering action at the source, removing ground roll interference before the data is stored or transmitted. This reduces both storage requirements and subsequent processing time while maintaining signal quality.
Solution Approach 2:
The adaptive filter uses the seismic data itself to adapt and optimize filtering parameters in real-time. The system serves itself by using incoming signal statistics to automatically adjust filter coefficients without requiring external intervention or extensive pre-collected reference data, enabling immediate ground roll attenuation with minimal storage requirements.
Data Source
AI summary
Adaptive filtering method to remove ground roll from seismic data. In an M channel adaptive filter, weights Wi are set using an adaptive algorithm based on seeking the minimum in the partial differential of cost function J. The cost function includes an expansion of the primary trace d into d=dg+Δd (where: dg is ground roll contribution and Δd=dsig+dran, where dsig is the reflected signal component and dran is a random noise component) and a corresponding expansion of the reference x into x=xg+Δx (where xg is ground roll contribution and Δx=xsig+xran; where xsig is a reflected signal component and xran is a random noise component). The delta components are included in the denominator of cost function J to provide an optimal solution of the filter coefficients biased by the reflection signal and random noise is removed.