Downhole microseismic signal-to-noise ratio enhancement via strip matching shearlet transform

Li, Juan; Ji, Shuai; Li, Yue; Qian, Zhihong; Lu, Weili

doi:10.1088/1742-2140/aa93af

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Shearlet transform is a multi-scale and multi-direction geometric analysis method, 18,19 which is widely used in the field of image and signal processing. 20 It is a multi-scale and multidirectional signal-based analysis. By constructing multi-scale and multi-directional basis function decompositions and localizing the decomposition and representation of the signal, additional shear coefficients are introduced.…”

Section: B Shearlet Transform Algorithm Flowmentioning

confidence: 99%

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

Liao,

Xia,

Liao

et al. 2024

AIP Advances

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To solve the problems of noise coverage defect and low contrast between the defect and the background of ZrO2 ceramic bearing balls, a surface defect extraction algorithm based on shearlet transform image enhancement for ZrO2 ceramic bearing balls is proposed. According to the shape characteristics of ceramic bearing balls, the surface defect image acquisition platform is built to collect and analyze surface defect images. Gaussian filtering weakens the scatter-particle noise in the image, and the threshold corrects the coefficient generated by the shearlet transform. After shearlet transform, the relatively low-frequency and high-frequency parts appear. The low-frequency part reflects the edge information of defects, and the high-frequency part reflects the edge and texture information of defects. Thus, the integrity of the defect is ensured, and an enhanced surface defect image is obtained. The gray histogram of the enhanced image is observed. The optimal threshold is selected by the histogram threshold segmentation method, and the process of defects being completely extracted from the background is realized. Experimental results showed that the extraction rates of pits, scratches, and cracks in ZrO2 ceramic bearing balls’ surface images are 95.00%, 92.50%, and 92.50%, respectively.

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Section: B Shearlet Transform Algorithm Flowmentioning

confidence: 99%

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

Liao,

Xia,

Liao

et al. 2024

AIP Advances

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show abstract

“…We add random Gaussian noise to each component of the noiseless trace. We define the signal-to-noise ratio (SNR) as (Li et al, 2018;Zhang et al, 2020): We note that the choice of Gaussian noise has a quantifiable consequence on the power spectra of the signals, as it is known that additive white noise has an expected constant power in Fourier space (see e.g. Haykin, 2001;Papoulis et al, 2002).…”

Section: Inferencementioning

confidence: 99%

“…)where sij refers to the j-th component of the i-th trace and sij to the corresponding noisy trace, N = 8000 is the number of training data and N keep = 2001 (1000 in the ISO case) is the number of time components. FollowingLi et al (2018), we set SNR= 33 dB, which corresponds to a standard deviation of the Gaussian noise of σ = 10.0, 0.3 and 3.5 for ISO, DC and CLVD, respectively, in the same arbitrary units as the seismograms' amplitude. We show examples of noiseless and noisy signals in Fig.5.…”

mentioning

confidence: 99%

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

Piras,

Mancini,

Joachimi

et al. 2021

Preprint

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Bayesian inference applied to microseismic activity monitoring allows for principled estimation of the coordinates of microseismic events from recorded seismograms, and their associated uncertainties. However, forward modelling of these microseismic events, necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques, to emulate the forward model and thus accelerate Bayesian inference. In this paper, we improve on previous work, which considered only sources with isotropic moment tensor. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows for the complete and fast retrieval of the event coordinates for any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hour on a commercial laptop, while yielding accurate results using less than 10 4 training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. This work lays the foundations for the efficient localisation and characterisation of any recorded seismogram, thus helping to quantify human impact on seismic activity and mitigate seismic hazard.

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A Novel Remote Sensing Image Enhancement Method Using Unsharp Masking in NSST Domain

2018

J Indian Soc Remote Sens

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Downhole microseismic signal-to-noise ratio enhancement via strip matching shearlet transform

Cited by 5 publications

References 16 publications

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

A Novel Remote Sensing Image Enhancement Method Using Unsharp Masking in NSST Domain

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