scholarly journals Fault detection by reflected surface waves based on ambient noise interferometry

2021 ◽  
pp. 100035
Author(s):  
G.U. Ning ◽  
Z.H.A.N.G. Haijiang ◽  
Nori NAKATA ◽  
G.A.O. Ji
Keyword(s):  
Fault Detection ◽  
Surface Waves ◽  
Ambient Noise ◽  
Noise Interferometry

scholarly journals Lithospheric structure beneath the northern Central Andean Plateau from the joint inversion of ambient noise and earthquake‐generated surface waves

2016 ◽  
Vol 121 (11) ◽  
pp. 8217-8238 ◽  
Author(s):  
Kevin M. Ward ◽  
George Zandt ◽  
Susan L. Beck ◽  
Lara S. Wagner ◽  
Hernando Tavera
Keyword(s):  
Surface Waves ◽  
Ambient Noise ◽  
Joint Inversion ◽  
Lithospheric Structure

Seismic imaging at a mineral-deposit scale using high-frequency surface waves (0.5–24 Hz) in ambient noise wavefield

2020 ◽  
Author(s):  
Y. Xu ◽  
S. Lebedev ◽  
R. Bonadio ◽  
T. Meier ◽  
C. Bean ◽  
...  
Keyword(s):  
Surface Waves ◽  
High Frequency ◽  
Ambient Noise ◽  
Seismic Imaging ◽  
Mineral Deposit

Extracting Long-Period Surface Waves and Free Oscillations Using Ambient Noise Recorded by Global Distributed Superconducting Gravimeters

2020 ◽  
Vol 91 (4) ◽  
pp. 2234-2246
Author(s):  
Hang Li ◽  
Jianqiao Xu ◽  
Xiaodong Chen ◽  
Heping Sun ◽  
Miaomiao Zhang ◽  
...  
Keyword(s):  
Surface Waves ◽  
Group Velocity ◽  
Ambient Noise ◽  
Velocity Dispersion ◽  
Group Velocity Dispersion ◽  
Dispersion Curves ◽  
Free Oscillations ◽  
Long Period ◽  
Superconducting Gravimeters ◽  
Noise Data

Abstract Inversion of internal structure of the Earth using surface waves and free oscillations is a hot topic in seismological research nowadays. With the ambient noise data on seismically quiet days sourced from the gravity tidal observations of seven global distributed superconducting gravimeters (SGs) and the seismic observations for validation from three collocated STS-1 seismometers, long-period surface waves and background free oscillations are successfully extracted by the phase autocorrelation (PAC) method, respectively. Group-velocity dispersion curves at the frequency band of 2–7.5 mHz are extracted and compared with the theoretical values calculated with the preliminary reference Earth model. The comparison shows that the best observed values differ about ±2% from the corresponding theoretical results, and the extracted group velocities of the best SG are consistent with the result of the collocated STS-1 seismometer. The results indicate that reliable group-velocity dispersion curves can be measured with the ambient noise data from SGs. Furthermore, the fundamental frequency spherical free oscillations of 2–7 mHz are also clearly extracted using the same ambient noise data. The results in this study show that the SG, besides the seismometer, is proved to be another kind of instrument that can be used to observe long-period surface waves and free oscillations on seismically quiet days with a high degree of precision using the PAC method. It is worth mentioning that the PAC method is first and successfully introduced to analyze SG observations in our study.

Feature Selection for Audio-Based Fault Detection in Pumps

2020 ◽  
Author(s):  
Victor O. Adegboye ◽  
Jason H. Rife
Keyword(s):  
Fault Detection ◽  
Ambient Noise ◽  
Area Under The Curve ◽  
Mel Frequency Cepstral Coefficients ◽  
Common Features ◽  
Performance Metric ◽  
Audio Recordings ◽  
Selection For ◽  
Industrial Machine

Abstract Whilst extensive work has been done on fault detection in bearings using sound, very little has been accomplished with other machine components and machinery partly due to the scarcity of datasets. The recent release of the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset opens the opportunity for research into malfunctioning machines like pumps, fans, slide rails, and valves. In this paper, we compare common features from audio recordings to investigate which best support the classification of malfunctioning pumps. We evaluate our results using the Area Under the Curve (AUC) as a performance metric and determine that the log mel spectrum is a very useful feature, at least for this dataset, but that other features can enhance detection performance when ambient noise is present (improving AUC from 0.88 to 0.94 in one case). Also, we find that mel Frequency Cepstral Coefficients (MFCC) perform substantially poorer as features than a sampled mel spectrogram.

Ambient Noise Radiation by “Soliton” Surface Waves

1988 ◽  
pp. 325-335
Author(s):  
R. H. Mellen ◽  
D. Middleton
Keyword(s):  
Surface Waves ◽  
Ambient Noise ◽  
Noise Radiation ◽  
Soliton Surface

A linear algorithm for ambient seismic noise double beamforming without explicit crosscorrelations

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. F1-F8
Author(s):  
Eileen R. Martin
Keyword(s):  
Correlation Functions ◽  
Seismic Noise ◽  
Ambient Noise ◽  
Computational Cost ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Noise Correlation ◽  
Dense Array ◽  
Near Surface ◽  
Noise Interferometry

Geoscientists and engineers are increasingly using denser arrays for continuous seismic monitoring, and they often turn to ambient seismic noise interferometry for low-cost near-surface imaging. Although ambient noise interferometry greatly reduces acquisition costs, the computational cost of pair-wise comparisons between all sensors can be prohibitively slow or expensive for applications in engineering and environmental geophysics. Double beamforming of noise correlation functions is a powerful technique to extract body waves from ambient noise, but it is typically performed via pair-wise comparisons between all sensors in two dense array patches (scaling as the product of the number of sensors in one patch with the number of sensors in the other patch). By rearranging the operations involved in the double beamforming transform, I have developed a new algorithm that scales as the sum of the number of sensors in two array patches. Compared to traditional double beamforming of noise correlation functions, the new method is more scalable, easily parallelized, and it does not require raw data to be exchanged between dense array patches.

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