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In order to meet the requirements of mobile marine seismometers to observe and record seismic signals, a study of fast and accurate seismic signal recognition was carried out. This paper introduces the use of the wavelet analysis method for seismic signal processing and recognition, and compares and analyzes the abilities of different wavelet basis...
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Context 1
... X k represents the data in the short time window, Y k represents the data in the longtime window, and µ is the preset trigger threshold; when the ratio R is greater than µ, the signal in the short time window is judged as the seismic signal, and the time of the first data point in STA is determined as the arrival time of the earthquake, as shown in Figure 5. ...
Context 2
... í µí± represents the data in the short time window, í µí± represents the data in the long-time window, and í µí¼ is the preset trigger threshold; when the ratio R is greater than í µí¼, the signal in the short time window is judged as the seismic signal, and the time of the first data point in STA is determined as the arrival time of the earthquake, as shown in Figure 5. ...
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Citations
... Only in recent years have the advancements in signal processing methods and detection techniques revitalized the study of ship's electric field signals. So far, in addition to the most common noise reduction methods [11,12] based on timefrequency analysis, wavelet thresholding denoising and low-pass filter, researchers have also proposed many other feature enhancement and denoising methods for shaft-rate electirc field signal. Birsan [13] used wavelet coherence analysis to determine whether the shaft-rate signals are superimposed with other induced electromagnetic fields through the phase difference between different components of the signals on the basis of time-frequency analysis. ...
The presence of complex electromagnetic noise in the ocean significantly impacts the accuracy of ship shaft-rate electric field signal detection, necessitating the development of an effective denoising method to enhance detection precision. Nevertheless, traditional denoising methods encounter issues like low frequency resolution, challenging threshold configuration, and mode mixing. This study introduces a method that integrates variational mode decomposition (VMD) with multi-window spectral subtraction (MSS). The intrinsic mode functions (IMFs) of noisy signals are extracted using VMD, and the noise components within different IMFs are identified. The spectral features of both signal and noise within different IMFs are leveraged to eliminate noise signals via MSS. Subsequently, the denoised components of IMFs are rearranged to derive the denoised ship shaft-rate electric field signals, achieving noise reduction across various frequency bands. Following validation using simulation signals and empirical data, the noise reduction efficacy of VMD-MSS surpasses that of alternative methods, demonstrating robust performance even at low signal-to-noise ratios. The marine electromagnetic noise is effectively suppressed in the empirical data, while preserving the characteristics of ship’s shaft-rate signals, thereby validating the method’s efficacy and demonstrating its practical engineering value.
... The algorithm optimizes the acoustic signal characteristic representation based on the short-term spectrum of the signal and extracts the correlation information between the front and back frames, which makes up for the defect that the common acoustic target recognition method cannot make full use of the time history information of the target signal. Jiang et al. [14] presented a seismic signal recognition model utilizing wavelet analysis. Their study delved into the application of wavelet analysis for processing and recognizing seismic signals. ...
To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part’s threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m.
... In order to meet the requirements of mobile marine seismometers to observe and record seismic signals and solve the contradiction between high speed and high accuracy of seismic signal recognition methods, Jiang et al. [15] developed a fast and accurate identification model of seismic signals, according to wavelet analysis. They successfully verified the feasibility of this method by identifying natural seismic P-waves from the data observed by the Dolphin seismometer. ...
Oceans cover approximately 71% of the planet’s surface, and 97% of the Earth’s water is contained within the ocean [...]
