Obtaining free USArray data by multi-dimensional seismic reconstruction

Chen, Yangkang; Bai, Min; Chen, Yunfeng

doi:10.1038/s41467-019-12405-0

Cited by 82 publications

(32 citation statements)

References 52 publications

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“…When signal‐to‐noise ratios (SNRs) varies from 3 to −1 dB., and calculated the corresponding misfit between the noise interrupted data and the synthetic data simulated by the inversion results. The SNR metric is defined as (Chen and Fomel, 2015; Chen et al ., 2019):

SNR = 10 \log_{10} \frac{false| false| {bold-italicχ}_{ref} {| |}_{2}^{2}}{false| false| {bold-italicχ}_{ref} - bold-italicχ {| |}_{2}^{2}},

where

{boldχ}_{boldref}

denotes the reference data with noise, and

χ

denotes the data without noise.…”

Section: Theorymentioning

confidence: 99%

Pre‐stack seismic inversion based on ‐norm regularized logarithmic absolute misfit function

Huang

Chen

Luo

et al. 2020

Geophysical Prospecting

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Ill‐posedness is one of the most common and intractable issues that arise when solving geophysical inverse problems. Ill‐posedness could be induced by various factors such as noise, band‐limited intrinsic property of seismic data and inappropriate forward operators. Regularization has been proven to be an effective method widely accepted for mitigating the adverse effects of ill‐posedness. Aiming to improve the stability and fidelity of the pre‐stack seismic inversion process, we implement the inversion in a Bayesian framework, with a logarithmic absolute criterion taken as a likelihood function, and an ℓ1−2‐norm metric as a priori constraint. Here, we exploit the linear approximation as the forward operator, and optimize the regularized misfit function by the alternating direction method of multipliers. Applications of the method to synthetic and real data sets yielded improved inversion results in terms of accuracy and resolution, and demonstrated the robustness of the method to noise.

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SNR = 10 \log_{10} \frac{false| false| {bold-italicχ}_{ref} {| |}_{2}^{2}}{false| false| {bold-italicχ}_{ref} - bold-italicχ {| |}_{2}^{2}},

where

{boldχ}_{boldref}

denotes the reference data with noise, and

χ

denotes the data without noise.…”

Section: Theorymentioning

confidence: 99%

Pre‐stack seismic inversion based on ‐norm regularized logarithmic absolute misfit function

Huang

Chen

Luo

et al. 2020

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

show abstract

“…The matrix

boldM

is of size

I \times J

, with

I = false(N_{y} - m + 1 false) false(N_{x} - n + 1 false)

J = m n

. The block Hankel matrix

boldM

is considered to be low rank (Trickett, 2008; Oropeza and Sacchi, 2011; Huang et al ., 2016; Chen et al ., 2019), that is, it can be approximated by a small number of eigen‐images.…”

Section: Theorymentioning

confidence: 99%

Seismic signal enhancement based on the low‐rank methods

Huang

Wang

Chen

2020

Geophysical Prospecting

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Based on the fact that the Hankel matrix constructed by noise-free seismic data is low-rank, low-rank approximation (or rank-reduction) methods have been widely used for removing noise from seismic data. Due to the linear-event assumption of the traditional low-rank approximation method, it is difficult to define a rank that optimally separates the data subspace into signal and noise subspaces. For preserving the most useful signal energy, a relatively large rank threshold is often chosen, which inevitably leaves residual noise. To reduce the energy of residual noise, we propose an optimally damped rank-reduction method. The optimal damping is applied via two steps. In the first step, a set of optimal damping weights is derived. In the second step, we derive an optimal singular value damping operator. We review several traditional low-rank methods and compare their performance with the new one. We also compare these low-rank methods with two sparsity-promoting transform methods. Examples demonstrate that the proposed optimally damped rank-reduction method could get significantly cleaner denoised images compared with the state-of-the-art methods.

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“…(STFT) [8], [9] and wavelet transform (WT) [10], [11]. Unfortunately, both have the limitation, namely that one cannot accomplish the high resolutions in time and frequency domains simultaneously [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

2020

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Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a timefrequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis. INDEX TERMS Fault diagnosis, rolling bearing, time-frequency analysis, high-order synchrosqueezing transform, detrended fluctuation analysis.

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Obtaining free USArray data by multi-dimensional seismic reconstruction

Cited by 82 publications

References 52 publications

Pre‐stack seismic inversion based on ‐norm regularized logarithmic absolute misfit function

Pre‐stack seismic inversion based on ‐norm regularized logarithmic absolute misfit function

Seismic signal enhancement based on the low‐rank methods

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

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