Seismic Polynomial Filter for Fault Detection
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Solution Overview
Problem
Current seismic data processing techniques are sensitive to noise and acquisition footprints, which can suppress valuable information about faults and fractures, leading to the removal of small seismic data features that are associated with latent structures.
Innovation Solution
A computer-implemented method that uses a multidimensional polynomial function to filter seismic data, attenuating noise and acquisition footprints while preserving edge detection attributes, allowing for the calculation of shape and curvature attributes from 3D seismic images without dip-steering, and decomposing seismic images into architectural elements based on these attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current seismic data processing techniques are used, then processing can be performed, but noise and acquisition footprints suppress valuable information about faults and fractures
Solution Approach 1:
The patent segments the seismic data processing by separating the polynomial fitting operation from traditional filtering approaches. By fitting a multidimensional polynomial function to local neighborhoods of seismic data, the method isolates and processes different regions independently, allowing selective attenuation of noise while preserving structural features like faults and fractures.
Solution Approach 2:
The patent changes the parameter representation of seismic data by transforming it into polynomial coefficients through fitting. This parameter transformation allows the processing system to work in a different mathematical space where noise and signal can be more effectively separated, improving detection accuracy while reducing noise impact.
2Object-affected harmful factors
If small seismic data features are removed to reduce noise, then noise level decreases, but valuable geological information about latent structures is lost
Solution Approach 1:
The patent converts the harmful effect of noise into a benefit by using the polynomial fitting process to identify and attenuate noise components while simultaneously enhancing the detection of small seismic features. The fitting operation transforms noise contamination into an opportunity for selective filtering that preserves geological information.
Solution Approach 2:
The patent implements feedback through the iterative polynomial fitting process, where the fitted polynomial coefficients provide information about the local data structure that feeds back into the filtering decision. This feedback mechanism allows the system to distinguish between noise and genuine geological features, preventing information loss while reducing noise.
3Object-affected harmful factors
If traditional filtering methods are applied, then noise is reduced, but edge detection attributes and small features are suppressed
Solution Approach 1:
The patent performs preliminary polynomial fitting before final filtering operations. By establishing the polynomial model first, the system creates a framework that guides subsequent noise attenuation while inherently preserving edges and structural features through the mathematical properties of the polynomial representation.
Solution Approach 2:
The patent replaces traditional mechanical filtering approaches with a mathematical polynomial fitting system. This substitution allows for more sophisticated noise reduction that preserves edge detection attributes, as the polynomial model can adapt to local data variations without the blunt filtering effects of conventional methods.
Data Source
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AI summary
A method includes receiving imagery data; fitting a multidimensional polynomial function to at least a portion of the imagery data to generate one or more values for one or more corresponding parameters of the function; and generating curvature attribute data based at least in part on the fitting.