3D Nonlinear Seismic Conditioning With Trained Dictionaries
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Solution Overview
Problem
Seismic data collected during surveys often contains undesirable noise and artifacts, which degrade the quality of subsurface images and hinder effective reservoir characterization.
Innovation Solution
A 3D non-linear seismic data conditioning technique using trained dictionaries and 3D complex wavelet transform, followed by sparse non-linear approximation and inverse transform, to reduce noise and preserve valuable seismic attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional linear seismic data conditioning techniques are used, then processing speed is maintained, but noise reduction effectiveness is insufficient
Solution Approach 1:
The patent transforms the seismic data from the time domain to the wavelet domain using 3D complex wavelet transform, changing the parameter space in which noise separation occurs. This domain transformation enables non-linear noise attenuation while preserving processing efficiency through optimized transform algorithms
Solution Approach 2:
The patent replaces traditional linear filtering mechanisms with a non-linear sparse approximation approach using trained dictionaries. This substitution enables more effective noise reduction by capturing non-linear relationships in the seismic data while maintaining computational feasibility through efficient sparse representation
2Measurement precision
If aggressive noise filtering is applied, then signal-to-noise ratio improves, but valuable seismic attributes are lost
Solution Approach 1:
The patent applies different processing characteristics to different components of the seismic data in the wavelet domain. By identifying and selectively attenuating noise components while preserving signal components with distinct local characteristics, the method achieves noise reduction without losing valuable seismic attributes
Solution Approach 2:
The 3D complex wavelet transform serves as an intermediary that decomposes the seismic data into multiple frequency bands and spatial scales. This intermediate representation allows for selective noise attenuation in specific domains while preserving the original signal characteristics through inverse transform
3Object-affected harmful factors
If 3D non-linear processing is implemented, then noise attenuation effectiveness increases, but computational complexity increases
Solution Approach 1:
The patent segments the seismic data processing into distinct stages: forward wavelet transform, dictionary-based sparse approximation with non-linear thresholding, and inverse wavelet transform. This segmentation allows each stage to be optimized independently, managing computational complexity while achieving effective noise attenuation
Solution Approach 2:
The patent pre-computes and stores trained dictionaries from representative seismic data before processing the actual survey data. This preliminary action enables the main processing stage to use these pre-trained models for efficient non-linear noise attenuation without repeating the computationally intensive training process
Data Source
AI summary
Techniques to reduce noise in seismic data by receiving a set of seismic data comprising a plurality of input volumes each inclusive of positional data and at least one additional attribute related to the seismic data, selecting a first input volume of the plurality of input volumes having a first additional attribute related to the seismic data, and generating a pilot volume by selecting a range of input volumes of the plurality of input volumes and stacking input volumes of the range of input volumes with the first input volume. Additionally, generating a trained dictionary based upon transformation of the pilot volume, transforming the first input volume into transformed data, imposing a sparse condition on the transformed data utilizing the trained dictionary to generate sparsified data, and inverse transforming the sparsified data to generate an output data volume as a portion of a set of modified seismic data.