Variable Length Spatial Filtering for Seismic Noise Attenuation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Seismic data acquisition in marine environments faces challenges in separating low-frequency seismic signals from noise, as noise is stronger at lower frequencies and seismic signals are weaker, with large wavelengths, making it difficult to achieve effective noise attenuation and signal preservation.
Innovation Solution
The technique involves spatial filtering with variable length filters that change based on frequency, using finite impulse response (FIR) filters and adaptive noise attenuation, decomposing signals into subbands, and applying autoregressive modeling to mitigate edge effects and reconstruct signals, ensuring better noise attenuation and signal preservation across different frequency ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If spatial filtering is applied to attenuate noise in seismic data, then noise attenuation is improved, but signal preservation deteriorates due to loss of seismic signal energy
Solution Approach 1:
The patent applies different filter lengths at different spatial locations and frequency bands. Longer filter lengths are used in regions with strong noise and shorter filter lengths where the signal is weak, allowing localized optimization of noise attenuation while preserving signal energy in different areas of the seismic data
Solution Approach 2:
The filter length is made variable and adaptive rather than fixed. The filtering parameters are dynamically adjusted based on the local characteristics of the seismic data, including frequency content and noise levels, enabling the system to adapt to changing signal and noise conditions across different time and space domains
2Object-affected harmful factors
If longer filter lengths are used to improve noise attenuation at low frequencies, then noise attenuation is improved, but resolution deteriorates due to increased smoothing
Solution Approach 1:
The seismic data is decomposed into multiple frequency bands or time windows, allowing different filter lengths to be applied to different segments. This segmentation enables the use of longer filters for noise attenuation in frequency bands where noise dominates, while shorter filters preserve resolution in bands where signal detail is critical
Solution Approach 2:
Different filtering characteristics are applied to different frequency components and spatial regions. The filter length varies locally based on the specific noise and signal characteristics of each frequency band and time window, optimizing both noise attenuation and resolution preservation in a localized manner
3Loss of energy
If variable length spatial filtering is applied to preserve seismic signals, then signal preservation is improved, but computational complexity increases
Solution Approach 1:
The filter length is dynamically adjusted based on local signal and noise characteristics rather than being uniformly applied. This dynamic adaptation allows the system to use shorter filters where possible, reducing computational complexity, while only using longer filters where the signal characteristics warrant the additional computational cost
Solution Approach 2:
Instead of applying maximum filter length uniformly across all data, the system applies partial filtering with variable lengths only where necessary. This selective approach reduces overall computational complexity by avoiding excessive filtering in regions where it is not needed, while still achieving signal preservation where critical
Data Source
AI summary
A technique includes spatially filtering a signal that is derived from a seismic acquisition. The filtering is associated with a filter length, and the filtering includes varying the filter length with frequency. The filtering may be used in connection with adaptive noise attenuation, which is applied to decomposed subbands. Furthermore, the filtering may be applied during the reconstruction of the signal from the subbands.