A compressive sensing approach to the high-resolution linear Radon transform: application on teleseismic wavefields

Aharchaou, Mehdi; Levander, A.

doi:10.1093/gji/ggw307

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Generally, the above formulas need to play an antioverlapping effect and need to provide relatively high resolution in the transform domain. In the process of matrix inversion, singularities often appear, especially for the lowfrequency band [30]. For the stability of the forward transformation process, a damping parameter is usually added.…”

Section: Transmission Line Fault Handling Based On Ant Colony and Radmentioning

confidence: 99%

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Liu

2021

Security and Communication Networks

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After a transmission line fails, quickly and accurately find the fault point and deal with it, which is of great significance to maintaining the normal operation of the power system. Aiming at the problems of low accuracy of traditional traveling wave fault location methods and many affected factors, this paper relies on distributed traveling wave monitoring points arranged on transmission lines to study methods to improve the accuracy of traveling wave fault location on transmission lines. First, when a line fails, a traveling wave signal that moves to both ends will be generated and transmitted along the transmission line. We use the Radon transform algorithm to process the traveling wave signal. Then, this paper uses ant colony algorithm to analyze and verify the location and extent of transmission line faults and then optimizes high-precision collection and processing. Finally, the simulation distance measurement is carried out on double-terminal transmission lines and multiterminal transmission lines (T-shaped lines) with branches. The results show that, for double-ended transmission lines, the algorithm increases the speed of matrix calculations and at the same time makes the fault location error of the transmission grid still maintain the improved effect.

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Section: Transmission Line Fault Handling Based On Ant Colony and Radmentioning

confidence: 99%

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Liu

2021

Security and Communication Networks

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“…Therefore, we do not expect that the SWCCP technique proposed here is effective to separate the LAB/midlithospheric discontinuity from crustal multiples without any modification. A recent study [Aharchaou and Levander, 2016] suggested that compressive sensing technique can significantly raise the slowness resolution in slant stacking; therefore, we plan to continue exploring the SWCCP technique with a focus on lithospheric structures.…”

Section: Discussionmentioning

confidence: 99%

An investigation on slowness‐weighted CCP stacking and its application to receiver function imaging

Guan

Niu

2017

Geophysical Research Letters

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Common conversion point (CCP) stacking of receiver functions is a widely used technique to image velocity discontinuities in the mantle. The CCP imaging technique assumes that receiver functions are composed solely of P to S conversions at velocity boundaries, whose depths can be mapped out through their arrival times. The multiple reflections at shallow boundaries with large velocity contrasts, such as the base of unconsolidated sediments and the Moho, can lead to artificial structures in the CCP images. We develop a refined CCP stacking method that uses relative slowness as a weighting factor to suppress the multiples (slowness‐weighted CCP stacking, SWCCP). We conduct extensive numerical tests with synthetic data to seek the best weighting scheme and to verify the robustness of the images. We apply this technique to receiver function data of NECESSArray in China and the transportable array in western U.S. and find that most of the events in the depth range of 200–400 km shown in the regular CCP images are eliminated. The SWCCP images, on the other hand, reveal a clear negative event under some parts of the two arrays, indicating the presence of low velocity layer above the 410 km discontinuity, which was reported by previous studies.

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“…Searching for a solution to Equation ( 14), one should solve a set of equations with a non-quadratic system matrix. The solution of the normal Equation ( 14) can be interpreted in formalism of the direct reconstruction method based on discrete Radon transform (DRT) and inversion of the discrete Radon transform (IDRT) techniques [38][39][40]. Using the Radon transform scheme along with the matrix formalism implies ordinary matrix operations have equivalent Radon transform operations.…”

Section: Reconstruction Algorithmmentioning

confidence: 99%

Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network

Pałczyńska

Masnicki

Mindykowski

2020

Sensors

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The contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed from under-sampled incoherent linear measurements. This paper highlights the use of the discrete Radon transform (DRT) techniques in the CS scheme. In the reconstruction algorithm section, a fast algorithm based on the inverse DRT is presented, in which a few randomly sampled projections of the input signal are used to correctly reconstruct the original signal. However, DRT requires a very large set of measurements that can defeat the purpose of compressive data acquisition. To acquire the wideband data below the Nyquist frequency, the K-rank-order filter is applied in the sparse transform domain to extract the most significant components and accelerate the convergence of the solution. While most CS research efforts focus on random Gaussian measurements, the Bernoulli matrix with different values of the probability of ones is applied in the presented algorithm. Preliminary results of numerical simulation confirm the effectiveness of the algorithm used, but also indicate its limitations. A significant advantage of the proposed approach is the speed of analysis, which uses fast Fourier transform (FFT) and inverse FFT (IFFT) algorithms widely available in programming environments. Moreover, the data processing algorithm is quite simple, and therefore memory usage and burden of the data processing load are relatively low.

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A compressive sensing approach to the high-resolution linear Radon transform: application on teleseismic wavefields

Cited by 10 publications

References 32 publications

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

An investigation on slowness‐weighted CCP stacking and its application to receiver function imaging

Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network

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