Multicomponent Filter Operator for Microseismic Noise Attenuation
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Solution Overview
Problem
Microseismic data processing is sensitive to noise quality, which affects the accuracy of microseismic event location determination in passive seismic monitoring, as the signal-to-noise ratio (SNR) of conventional methods is inadequate for precise subterranean property analysis.
Innovation Solution
A method involving a multicomponent filter operator applied to convolved seismic data from multicomponent sensors, combining polarization filtering and adaptive block-thresholding techniques to enhance the SNR while preserving polarization information, and optionally using a Wiener filter for further noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional noise filtering methods are applied to microseismic data, then noise reduction is achieved, but polarization information is lost and signal-to-noise ratio improvement is insufficient
Solution Approach 1:
The filtering process is segmented into distinct stages: polarization filtering to preserve directional information, followed by adaptive block-thresholding to reduce noise. This segmentation allows each stage to focus on specific objectives without compromising the other, thereby maintaining polarization information while improving signal-to-noise ratio.
Solution Approach 2:
The method applies different filtering characteristics to different components of the microseismic data based on their individual properties. The adaptive block-thresholding operator selectively processes each component according to its signal-to-noise characteristics, preserving polarization information in components where it is critical while removing noise where appropriate.
2Object-affected harmful factors
If aggressive noise reduction filters are applied, then noise is reduced, but polarization information is lost
Solution Approach 1:
Polarization filtering is applied as a preliminary action before noise reduction filtering. This preliminary polarization filter establishes the directional characteristics of the signal and preserves polarization information, which then guides subsequent noise reduction operations to target only the noise components while maintaining the polarization-processed signal integrity.
Solution Approach 2:
The polarization filter acts as an intermediary between the raw microseismic data and the final noise-reduced output. It processes the data first to preserve polarization information, then the adaptive block-thresholding filter uses this preserved information as a guide to remove noise without destroying the polarization characteristics.
3Device complexity
If simple filtering operators are used, then processing is simple, but signal-to-noise ratio improvement is insufficient (8-12 dB)
Solution Approach 1:
The method merges multiple filtering techniques into a unified processing pipeline: polarization filtering combined with adaptive block-thresholding filtering. This combination achieves superior signal-to-noise ratio improvement (8-12 dB) by leveraging the complementary strengths of both filters while managing complexity through integrated implementation.
Solution Approach 2:
The adaptive block-thresholding filter dynamically adjusts its parameters based on the signal characteristics and noise levels in the microseismic data. This parameter adaptation allows the filter to optimize its performance for each specific dataset, achieving 8-12 dB signal-to-noise ratio improvement without requiring overly complex fixed-structure filters.
Data Source
AI summary
A method for processing microseismic data, comprises: receiving the microseismic data acquired by one or more multicomponent sensors; convolving the microseismic data with an operator that is applied to all of the components of the microseismic data; and applying a multicomponent filter operator to the convolved microseismic data. The microseismic data may result from human activity or be entirely natural. The filtering preserves the polarity of the received data while improving the signal-to-noise ratio of the filtered data.