Curvelet Template Adaptation for Noise Subtraction and Signal Preservation
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Solution Overview
Problem
Existing geophysical data processing methods face challenges in adapting template datasets to accurately match target datasets due to spatially and temporally varying prediction errors, leading to issues with noise attenuation and signal preservation, particularly in identifying hydrocarbon-containing geologic features.
Innovation Solution
The method employs complex-valued, directional, multiresolution transforms (CDMTs) to adapt template datasets by adjusting expansion coefficients within controlled ranges, allowing for precise phase and magnitude modifications to better match target data coefficients, thereby improving noise subtraction and signal preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the template adaptation is not adequately constrained, then noise subtraction performance is improved, but signal components may be damaged due to overfitting
Solution Approach 1:
The patent applies parameter changes by transforming the template and target data into a new domain (e.g., frequency-wavenumber domain or curvelet domain) where the adaptation parameters can be adjusted independently. This allows controlling the degree of adaptation through domain transformation parameters, enabling better noise subtraction while preserving signal characteristics through selective parameter modification in the transformed space.
Solution Approach 2:
The patent introduces an intermediary transformation domain as a mediator between the original template and target data. By operating in this intermediate domain (such as frequency-wavenumber or curvelet domain), the adaptation process can selectively modify noise components while leaving signal components intact, thus resolving the contradiction between noise subtraction accuracy and signal preservation.
2Reliability
If the template adaptation is overly constrained, then signal preservation is improved, but noise subtraction performance deteriorates due to underfitting
Solution Approach 1:
The patent uses parameter changes in the opposite direction - by transforming to a domain where signal and noise have distinct characteristics, it can apply minimal constraints in that domain while still achieving effective noise subtraction. The domain transformation parameters enable flexible control over the adaptation degree, allowing signal preservation with adequate noise subtraction.
Solution Approach 2:
The patent applies dimensionality change by moving from the time-space domain to a frequency-wavenumber domain or curvelet domain. This dimensional transformation reveals different characteristics of signal and noise components, allowing the adaptation process to distinguish between them more effectively and achieve both signal preservation and noise subtraction accuracy.
3Ease of operation
If straightforward subtraction of predicted noise templates is used, then processing simplicity is maintained, but adaptation to spatially and temporally varying prediction errors is insufficient
Solution Approach 1:
The patent introduces a transformation domain as an intermediary that enables adaptive matching without significantly complicating the processing workflow. The transformation to frequency-wavenumber or curvelet domain provides a systematic way to handle spatially and temporally varying prediction errors while maintaining operational simplicity through automated domain transformation and inverse transformation steps.
Solution Approach 2:
The patent applies parameter changes by modifying the template in the transformed domain to match the target data characteristics. This systematic parameter adjustment in the transformed space automatically adapts to spatially and temporally varying prediction errors, improving matching accuracy while keeping the processing approach relatively simple and systematic.
Data Source
AI summary
Method for adapting a template to a target data set. The template may be used to remove noise from, or interpret noise in, the target data set. The target data set is transformed (550) using a selected complex-valued, directional, multi-resolution transform (‘CDMT’) satisfying the Hubert transform property at least approximately. An initial template is selected, and it is transformed (551) using the same CDMT. Then the transformed template is adapted (560) to the transformed target data by adjusting the template's expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set. Multiple templates may be simultaneously adapted to better fit the noise or other component of the data that it may be desired to represent by template.