Wiener Filter for Seismic Displacement Velocity Stability
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Solution Overview
Problem
Existing seismic data processing techniques face instability when calculating particle displacement and velocity from acceleration data due to noise, leading to high amplitude low frequency noise masking the response after integration.
Innovation Solution
The use of a deterministic Wiener filter that approximates a Wiener filter, with selected damping factors, is applied to stabilize the calculation of particle displacement and velocity data from noisy acceleration data, reducing noise amplification and providing reliable amplitudes and phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If integration is applied to acceleration data to calculate particle displacement and velocity, then the response information is obtained, but noise amplification occurs leading to high amplitude low frequency noise masking the response
Solution Approach 1:
A deterministic Wiener filter is applied to the acceleration data before integration to pre-reduce noise. This preliminary filtering action prevents noise amplification during the subsequent integration process, allowing accurate calculation of particle displacement and velocity while suppressing harmful low frequency noise components.
Solution Approach 2:
The deterministic Wiener filter acts as an intermediary between the noisy acceleration data and the integration process. It mediates by selectively attenuating noise frequencies while preserving signal frequencies, enabling the integration to produce accurate displacement and velocity data without being overwhelmed by noise amplification.
2Object-affected harmful factors
If damping factors are applied to reduce noise amplification, then noise suppression is achieved, but calculation stability must be maintained
Solution Approach 1:
The deterministic Wiener filter employs parameter optimization to select appropriate damping factors that balance noise suppression with calculation stability. By carefully tuning the filter parameters, the system achieves effective noise reduction while maintaining stable and reliable calculations of particle displacement and velocity from acceleration data.
Data Source
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AI summary
Techniques are described for determining particle displacement or particle velocity data from particle acceleration measurements. In an embodiment, an apparatus comprises an interface configured to received particle acceleration data, one or more processors, and one or more storage media. The one or more storage media store instructions for determining one or more of particle velocity data or particle displacement data, based upon the particle acceleration data, by processing the particle acceleration data using a filter that at least approximates a Wiener filter and that uses one or more damping factors selected to provide stability in the presence of noise in the particle acceleration data.