Surface Wave Dispersion Imaging Resolution via Radon Transform
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Solution Overview
Problem
Traditional imaging methods for the dispersion energy spectrum of surface waves produce low-resolution results, which hinders accurate inversion of shear wave velocity structures.
Innovation Solution
An imaging method that processes surface wave data from a space-time domain to a space-frequency domain and then applies a high-resolution Radon transform based on an iterative shrinkage threshold algorithm to achieve a slowness-frequency domain representation, enhancing imaging resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional imaging methods are used for dispersion energy spectrum, then the processing is simple, but the imaging resolution is low
Solution Approach 1:
The patent transforms surface wave data from space-time domain to space-frequency domain via Fourier transform, then to slowness-frequency domain via Radon transform. This dimensional transformation enables high-resolution imaging by mapping data into a domain where dispersion energy can be precisely localized in both spatial and frequency dimensions simultaneously.
Solution Approach 2:
The patent replaces traditional mechanical imaging approaches with an iterative shrinkage threshold algorithm based on compressed sensing theory. This computational approach substitutes conventional signal processing methods, achieving super-resolution beyond the Nyquist limit by exploiting the sparsity of seismic signals in the transformed domain.
2Measurement precision
If traditional imaging methods are used, then the method is easy to implement, but the accuracy of dispersion curve picking is insufficient
Solution Approach 1:
The patent replaces traditional dispersion curve picking methods with an iterative shrinkage threshold algorithm that operates in the slowness-frequency domain. This computational substitution enables precise identification of dispersion curves by exploiting signal sparsity, achieving accurate measurement even in low signal-to-noise ratio conditions where traditional methods fail.
Solution Approach 2:
The patent changes the parameter space from time-domain to frequency-domain to slowness-frequency domain through Fourier and Radon transforms. This parameter transformation allows dispersion curves to be clearly separated from noise in the transformed space, enabling accurate picking by identifying peaks in the dispersion energy spectrum.
3Manufacturing precision
If traditional imaging methods are used, then the processing is straightforward, but the results are low resolution for low signal-to-noise ratio data
Solution Approach 1:
The patent replaces traditional filtering and noise reduction methods with compressed sensing-based iterative shrinkage thresholding. This computational approach distinguishes signal from noise by exploiting the sparsity property of seismic signals in the slowness-frequency domain, achieving high-resolution imaging even when the signal-to-noise ratio is low by reconstructing the signal from incomplete or noisy measurements.
Solution Approach 2:
The patent applies localized processing in the slowness-frequency domain where dispersion energy is concentrated. By focusing computational resources on regions with high dispersion energy and using thresholding to suppress low-energy noise regions, the method achieves local enhancement of signal quality while maintaining overall imaging resolution.
Data Source
AI summary
The embodiment of the present disclosure provides an imaging method for a dispersion energy spectrum of surface waves, an electronic device, and a storage medium. The imaging method includes: obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation; processing the first surface wave data to obtain the second surface wave data, and the second surface wave data corresponding to a space-frequency domain representation; and processing the second surface wave data based on a preset algorithm to obtain a first imaging result, the first imaging result corresponding to a slowness-frequency domain representation.