Feature Particles Tracking for Moving Objects

Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations. However, micro-seismic signals are commonly contaminated by various types of complex noise, which can hinder micro-seismic accurate P-wave pickup and analysis. In this study, we propose the Multiscale Dilated Convolutional Attention denoising method, referred to as MSDCAN, to eliminate complex noise interference. The MSDCAN denoising model consists of an encoder, an improved attention mechanism, and a decoder. To effectively capture the neighborhood features and multiscale features of the micro-seismic signal, we construct an initial dilated convolution block and a multiscale dilated convolution block in the encoder, and the encoder focuses on extracting the relevant feature information, thus eliminating the noise interference and improving the signal-to-noise ratio (SNR). In addition, the attention mechanism is improved and introduced between the encoder and decoder to emphasize the key features of the micro-seismic signal, thus removing the complex noise and further improving the denoising performance. The MSDCAN denoising model is trained and evaluated using micro-seismic data from Stanford University. Experimental results demonstrate an impressive increase in SNR by 11.237 dB and a reduction in root mean square error (RMSE) by 0.802. Compared to the denoising results of the DeepDenoiser, CNN-denoiser and Neighbor2Neighbor methods, the MSDCAN denoising model outperforms them by enhancing the SNR by 2.589 dB, 1.584 dB and 2dB, respectively, and reducing the RMSE by 0.219, 0.050 and 0.188, respectively. The MSDCAN denoising model presented in this study effectively improves the SNR of micro-seismic signals, offering fresh insights into micro-seismic signal denoising methodologies.

Displacement measurement is of great significance to monitor the crack variation and ensure the health of building structures. Aiming at the problems of low sensitivity and high temperature error of fiber Bragg grating (FBG) displacement sensors in displacement monitoring, this paper presents an adjustable cantilever beam displacement sensor with the FBGs as the sensing element. The sensor adds double FBGs on the relative surfaces of the equal-strength cantilever beam, which increases the bending deformation on the FBG of the beam surface to improve the sensitivity and realize the temperature compensation of the sensor. By adding an adjustable external rod structure between a flexible spring and a fixed foot stand, the sensor can regulate the range of initial crack width for different occasions. A theoretical analysis of the displacement sensor is performed, and the simulation analysis and optimization design for the structural parameters of the cantilever beam elastic sensitive element are implemented by adopting SolidWorks and ANSYS software. Finally, a displacement testing platform is constructed to test its performance. Experimental results show that this design has a high sensitivity coefficient of 39.47 pm/mm and a temperature coefficient of 1.04 pm/°C in the range of initial crack width from 0 to 110 mm or from 0 to 130 mm depending on different monitoring situations. Furthermore, good linearity, hysteresis delay, repeatability, and temperature compensation performance have also been demonstrated.

In order to enhance the sensitivity improvement of crack meter in ruins rescue site, this paper proposes a novel crack meter with a V-shape structure. The mathematical analysis showed that there is a negative correlation between sensitivity of proposed crack and angle of V-shape structure. The finite element analysis using the ANSYS Workbench illustrates that sensitivity of proposed crack meter has rapid reduce with angle of V-shape structure of 14°–28°. Experimental findings show that the proposed V-shape structure crack meter has a higher sensitivity than the traditional one. With 14° V-shape structure crack meter, the sensitivity was enhanced to 1.16 mV/(mm/V), with an increase of 2.3 times over the traditional crack meter sensitivity of 0.35 mV/(mm/V).

The magnetic field structure is a key factor that affects performance of the magneto-electric geophone. In order to enhance the magnetic field homogeneity and magnetic induction intensity of the magnetic field structure, this paper proposes a new magnetic field structure. It consists of two cylindrical permanent magnets: an H-type magnetic boot and an external magnetic yoke. The proposed magnetic field structure can broaden the range of a uniform magnetic field and increase the magnetic field intensity of working air-gap. To confirm the validity of the design, the finite element analysis and real measurement experiments were conducted. The finite element simulations using the ANASYS Electromagnetics Suite 17.2.0 showed that the air-gap magnetic induction intensity is increased and the work space with a uniform magnetic field is broadened. Meanwhile, the output voltage of the coil is increased, and the harmonic distortion rate of output voltage is reduced. According to the real measurement experimental results, compared with the traditional magnetic field structure, the uniform range of the magnetic field is improved 23% in the entire air-gap path, and the magnetic induction intensity enhances 24% over the proposed new magnetic field structure.

Seismic noise separation and suppression is an important topic in seismic signal processing to improve the quality of seismic data recorded at monitoring stations. We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements K and α in VMD. Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. It is worth to be noted that the Kurtosis comparison method can select the IMFs ensuring to preserve valid seismic signal. Finally, the denoised seismic signal is restored by the effective IMFs. The experimental results from synthetic and real field seismic data show that compared with several denoising methods, the proposed method can obtain higher signal-to-noise ratio (SNR) with increasement of 27.78% and lower root mean square error (RMSE) with improvements of 78.82% under the same level of structural similarity (SSIM) which prove the validity and effectiveness of the GWO-VMD method for both separating random noise and preserving valid seismic signal.