Seismic Random Noise Separation and Attenuation Based on MVMD and MSSA

Zhang, Yijie; Zhang, Haoran; Yang, Yang; Liu, Naihao; Gao, Jinghuai

doi:10.1109/tgrs.2021.3131655

Cited by 25 publications

(7 citation statements)

References 46 publications

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“…In general, RC 0 is capable of reflecting changes in the periodic components [49]. In this study, we focus on the overall relationship between the periodic components of multiple stations.…”

Section: Excluding the Anomalous Interference From One Single Stationmentioning

confidence: 99%

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

Yu,

Jing,

Wang

et al. 2024

Remote Sensing

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On 5 September 2022, the Ms 6.8 Luding earthquake occurred at 29.59°N and 102.08°E in China. To investigate the variations in geomagnetic signals before the earthquake, this study analyzes the geomagnetic data from nine stations around the epicenter. First, we apply the Multi-channel Singular Spectrum Analysis to reconstruct the periodic components of the geomagnetic data from multiple stations. Second, we employ K-means clustering to rule out the possibility of occasional anomalies caused by a single station. Subsequently, we construct a geomagnetic topology network considering the remaining stations. Network centrality is defined as a measure of overall network connectivity, where the higher the correlation between multiple stations, the greater the network centrality. Finally, we examine the network centrality 45 days before and 15 days after the Luding earthquake. The results show that several anomalies in network centrality are extracted about one week before the earthquake. We further validate the significance of the anomalies in terms of time as well as space and verify the utility of the centrality anomalies through the SEA technique. The anomalies are found to have a statistical correlation with the earthquake event. We consider that this study provides a new way and a novel observational perspective for earthquake precursor analysis of ground-based magnetic data.

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Section: Excluding the Anomalous Interference From One Single Stationmentioning

confidence: 99%

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

Yu,

Jing,

Wang

et al. 2024

Remote Sensing

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“…To solve the unconstrained variational problem after this transformation, the Alternate Direction Method of Multipliers (ADMM) method is used to implement alternate updates [61]. For the algorithm process, please refer to [62]. Then, calculate and the centre frequency to get the decomposed signal components.…”

Section: Signal Noise Reduction and Reconstructionmentioning

confidence: 99%

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Mao,

Zhou,

et al. 2024

Front. Phys.

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To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

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“…MVMD processes TDEM data across multiple channels in unison, extracting IMFs that share identical center frequencies and bandwidths across various channels [30]. This concurrent processing of multichannel TDEM data enhances IMF continuity and aids in extracting spatial data features, effectively reducing noise [31]. This paper proposes a multichannel workflow for denoising TDEM data using MVMD and multivariate detrended fluctuation analysis (MDFA).…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Xing,

Li,

et al. 2024

Remote Sensing

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Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.

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Seismic Random Noise Separation and Attenuation Based on MVMD and MSSA

Cited by 25 publications

References 46 publications

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

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