Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Liu, Yanping; Zhou, Yan

doi:10.1111/1365-2478.12947

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…It can be used to restore a part of energy loss as: where f is the frequency of the seismic signal, t is the spreading time, Q represents Quality Factor quantitatively depicting the absorption attenuation, and ω0 and ω are reference angular velocity at 1 Hz (ω0=2π) and angular velocities, respectively. When the amplitude at a certain frequency has decayed greater, a compensation function (Equation 10) can be used to restore the part of the signal decaying at that frequency range (Liu et al, 2013):…”

Section: Absorption Attenuation Compensationmentioning

confidence: 99%

Real-time Monitoring and Analysis of Debris Flow Events: Insight from seismic signal features and dynamic flow characteristics

Yan,

Zeng,

Cui

et al. 2023

Preprint

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Abstract. Debris flows are among the most dangerous natural hazards worldwide because they start abruptly, move quickly, and transport large boulders, causing great loss of life and infrastructure. The most important approach to preventing and mitigating debris flows is through monitoring and early warning. In recent years, environmental seismology has emerged as a powerful method for monitoring debris flows because it allows non-contact observation over large areas and can provide extensive information on debris flow dynamics. However, further research is required on combining debris flow imagery with seismic signal analysis, incorporating information from post-disaster surveys, and the inversion of seismic signals into dynamic parameters of debris flows. Here, we aim to explore the basic parameters, development process, and magnitude of debris flows based on seismic signal analysis combined with other information recorded in real time during the formation and development of three debris flows in Wenchuan, China. The analysis involves three stages. First, we compensate for the energy loss of the seismic signal due to the absorption attenuation effect and restore the signal to an unchanged state as far as possible. Second, we identify the start and end time of the debris flow from the seismic signal, analyze the rainfall data to determine that the debris flow was triggered by the test rain, and determine that changes in the energy and frequency ranges of the seismic signal are highly consistent with the development of the debris flow. Third, a comprehensive analysis of debris flow images, the power spectral density (PSD) of the seismic signal, and forward modeling of the PSD of the seismic signal of the debris flow are used to reveal the relationship between the seismic signal and the development process of the debris flow and clarify the feasibility of debris flow analysis from the time-frequency characteristics of the seismic signal. Debris flow exhibits the characteristics of fast excitation and slow recession. Using the cross-correlation algorithm and verifying Manning's formula, a maximum velocity of 7.027 m/s was calculated for the second debris flow. A comparison of the frequency characteristics of the seismic signal allowed the relative magnitude of the three debris flows to be assessed. The study provides a theoretical basis and a case study exemplar for the reconstruction of the debris flow process and peak velocity estimation using debris flow seismology, offering a framework for upscaling debris flow monitoring networks and the determination of early warning thresholds.

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Section: Absorption Attenuation Compensationmentioning

confidence: 99%

Real-time Monitoring and Analysis of Debris Flow Events: Insight from seismic signal features and dynamic flow characteristics

Yan,

Zeng,

Cui

et al. 2023

Preprint

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show abstract

“…Literature [ 30 ] indicates that a mathematic morphological algorithm is a powerful tool for image processing. The algorithm processes images using morphological transforms according to the local shape features of images by appropriate structure elements [ 31 ]. Thus, the main shape features of images are preserved while filtering small background noise.…”

Section: Data Processingmentioning

confidence: 99%

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Chen

Zhang

Zhu

et al. 2021

Sensors

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Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

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Bridge acceleration data denoising based on long short-term memory

Zhou,

Zeng,

Cui

et al. 2025

Mechanical Systems and Signal Processing

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Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Cited by 7 publications

References 38 publications

Real-time Monitoring and Analysis of Debris Flow Events: Insight from seismic signal features and dynamic flow characteristics

Real-time Monitoring and Analysis of Debris Flow Events: Insight from seismic signal features and dynamic flow characteristics

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Bridge acceleration data denoising based on long short-term memory

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