Seismic Observation and Analysis Based on Three-Component Fiber Optic Seismometer

Yang, Yue; Wang, Zhongmin; Chang, Tianying; Yu, Miao; Jun-jie, Chen; Zheng, Guodong; Cui, H. L.

doi:10.1109/access.2019.2961963

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…An early warning of optical equipment is to identify external vibrations from ground burial lines. It has the advantages of strong protection against electromagnetic interference, wide range of control, good electrical insulation, and high sensitivity [1]. However, when monitoring the vibration signal, a false alarm can a ect the control results.…”

Section: Introductionmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. TSVD-SCN performs SVD decomposition on the hidden layer output of the network and sets a threshold to remove the smaller singular values, so as to reduce the number of conditions of the hidden layer output matrix and improve the network recognition rate. This paper uses the method of duty cycle, average amplitude difference function, and FFT to calculate the energy duty cycle for feature extraction and uses TSVD-SCN algorithm to classify and recognize different intrusion vibration feature vectors. The experimental results show that the root mean square errors of TSVD-SCN and SCN networks are significantly less than RVFL. After the hidden layer node L = 20 , the training error decline speed of RVFL tends to be gentle. When LRVFL = L max , the learning effect is the best, and RMSERVFL = 0.3 . With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. Conclusion. The accuracy of the algorithm model proposed in this paper is higher than that of the SCN model. It can accurately identify the types of optical fiber intrusion signals, which is of great significance to improve the classification accuracy of the SCN network in practical applications.

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Section: Introductionmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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“…This sensor can be used, for example, as an accelerometer to measure low-frequency vibrations [ 32 , 33 ]. The Michelson fiber-optic interferometer as a seismic sensor can also be found in articles regarding various applications, such as the vibration measurement of oil wells [ 34 ] or general use in seismic applications [ 35 , 36 , 37 , 38 ]. In [ 35 ], a prototype device for seismological applications was developed.…”

Section: State Of the Artmentioning

confidence: 99%

“…It is a proof-of-concept technology without in-depth analysis of measured data and frequency analysis. The authors of the paper [ 38 ] developed a system to measure non-anthropogenic sources of vibration in three directions, who installed their equipment in the seismic observation cave at the Earthquake Administration Bureau.…”

Section: State Of the Artmentioning

confidence: 99%

Comparative In Situ Study of Dynamic Load Generated by Gravel Piles Measured by a Fiber-Optic Interferometer

Stolárik

Kepák

Pinka

et al. 2022

Sensors

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Currently, all the technology used for seismic monitoring is based on sensors in the electrical domain. There are, however, other physical principles that may enable and fully replace existing devices in the future. This paper introduces one of these approaches, namely the field of fiber optics, which has great potential to be fully applied in the field of vibration measurement. The proposed solution uses a Michelson fiber-optic interferometer designed without polarization fading and with an operationally passive demodulation technique using three mutually phase-shifted optical outputs. Standard instrumentation commonly used in the field of seismic monitoring in geotechnical engineering was used as a reference. Comparative measurements were carried out during the implementation of gravel piles, which represents a significant source of vibration. For the correlation of the data obtained, the linear dependence previously verified in laboratory measurements was used. The presented results show that the correlation is also highly favorable (correlation coefficient in excess of 0.9) from the values measured in situ, with an average deviation for the oscillation velocity amplitude of the optical sensor not exceeding 0.0052.

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“…In recent years, experimental fiber optic seismic monitoring systems have been deployed in seafloor seismic observation networks in the United States [1], Japan [2,3], France [4], Belgium [5], and Canada [6]. A few years ago, we developed a three-component fiber optic interferometric system, and deployed it both in the shallow waters (<100 m) of South China Sea and the land-based Seismic Observatory of Jilin Province, Northeast China to monitor earthquakes [7,8].…”

Section: Introductionmentioning

confidence: 99%

Shallow seafloor seismic wave monitoring using 3-component fiber optic interferometric accelerometer

Chang¹,

Yang²,

Luo³

et al. 2021

Meas. Sci. Technol.

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A fiber optic interferometric seismic monitoring system developed by us was deployed on the shallow seafloor near the Wuchang oceanic monitoring station off the Hainan Island, in the South China Sea, in 2017. In the past three plus years, it has recorded more than 40 teleseismic activities of magnitude 6.0+. We analyze these shallow seafloor observed seismic signals monitored by the fiber optic interferometric three-component accelerometer from the perspective of analyzing the underwater ambient seismic noise and the attenuation of the noise in the shallow seafloor seismic signals. The ambient seismic noise produced by wind, wave, shipping, and on-shore manmade activities admixed in the seismic signal is analyzed and discussed in detail. And the attenuation of such noise in the shallow seafloor seismic data using a hybrid denoising algorithm is demonstrated by applying it to three representative seismic events having different distances and magnitudes, with results showing that the noise in the shallow seafloor seismic signal is effectively reduced, and the ratio of the maximum acceleration value of seismic wave to the noise level is improved by more than 32 dB, rendering the seismic waves clearly observable.

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Seismic Observation and Analysis Based on Three-Component Fiber Optic Seismometer

Cited by 14 publications

References 19 publications

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Comparative In Situ Study of Dynamic Load Generated by Gravel Piles Measured by a Fiber-Optic Interferometer

Shallow seafloor seismic wave monitoring using 3-component fiber optic interferometric accelerometer

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