Geophysical Noise Reduction via Wavelet Transform and Signal Equalization
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Solution Overview
Problem
Conventional methods for removing incoherent noise from geophysical data are time-consuming, expensive, and imprecise, often affecting the geophysical information by being too aggressive or leaving noise if too conservative.
Innovation Solution
The method involves using two or more sensors to measure the wavefield, transforming the data to equalize signal parts, and selecting the data set with the lowest energy content to form a noise-reduced data set, utilizing integral transforms and wavelet transforms to account for sensor displacements and different aspects of the wavefield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional de-noising procedures are applied to remove incoherent noise, then noise reduction is achieved, but geophysical information may be affected or lost
Solution Approach 1:
The wavefield data is segmented into multiple independent data sets using different sensor combinations. Each data set is processed separately through transformation and noise modeling, allowing selective noise removal while preserving signal integrity in each segment before recombination.
Solution Approach 2:
The patent applies different transformation parameters and noise model parameters to the data sets. By varying transformation parameters (such as different transform types or parameters) and selecting optimal noise model parameters, the system adapts to different noise conditions while preserving geophysical information.
2Object-affected harmful factors
If rigorous de-noising procedures are applied, then noise is reduced, but processing time and cost increase
Solution Approach 1:
The patent performs preliminary transformation of wavefield data into transformed data sets before noise removal. By pre-processing the data through transformation (such as Fourier or wavelet transforms) and creating multiple data sets in advance, the subsequent noise removal becomes more efficient and requires fewer iterative processing steps.
Solution Approach 2:
Multiple copies or representations of the wavefield data are created through different sensor combinations and transformations. These copied data sets are then processed in parallel or selected from, reducing the need for repeated processing of the same data and thereby reducing overall processing time.
3Object-affected harmful factors
If conventional de-noising procedures are applied, then noise is reduced, but precision and accuracy decrease
Solution Approach 1:
The patent implements an iterative process where transformed data sets are processed, noise models are applied, and results are evaluated. Based on the evaluation of noise reduction effectiveness and signal preservation, the process can be iterated with adjusted parameters, providing feedback-driven optimization of processing precision.
Solution Approach 2:
The system adjusts transformation parameters and noise model parameters based on the specific characteristics of the data and noise conditions. By changing parameters such as transform type, transformation parameters, and noise model parameters, the system optimizes the balance between noise removal and signal preservation for each specific case.
Data Source
AI summary
A data set representing features of a geologic formation is formed from two or more signal acquisition data set representing independent aspects of the same wavefield. A wavelet transform is performed on the two or more signal acquisition data sets, and the data sets are further transformed to equalize signal portions of the data sets. Remaining differences in the data sets are interpreted as excess noise and are removed by different methods to improve the signal-to-noise ratio of any resulting data set.