Ground Motion in the Presence of Complex Topography: Earthquake and Ambient Noise Sources

2013 ◽  
Vol 104 (1) ◽  
pp. 451-466 ◽  
Author(s):  
S. Hartzell ◽  
M. Meremonte ◽  
L. Ramirez-Guzman ◽  
D. McNamara
Keyword(s):  
Ground Motion ◽  
Ambient Noise ◽  
Complex Topography ◽  
Noise Sources

A Method for Noise Source Levels Inversion with Underwater Ambient Noise Generated by Typhoon in Deep Ocean

2018 ◽  
Vol 26 (02) ◽  
pp. 1850007 ◽  
Author(s):  
Qiulong Yang ◽  
Kunde Yang ◽  
Shunli Duan
Keyword(s):  
Ambient Noise ◽  
Noise Source ◽  
Surface Wind ◽  
Deep Ocean ◽  
Noise Sources ◽  
Noise Field ◽  
Sea Surface Wind ◽  
Source Level ◽  
The Western Pacific ◽  
Good Agreement

Sea-surface wind agitation can be considered the dominant noise sources whose intensity relies on local wind speed during typhoon period. Noise source levels in previous researches may be unappreciated for all oceanic regions and should be corrected for modeling typhoon-generated ambient noise fields in deep ocean. This work describes the inversion of wind-driven noise source level based on a noise field model and experimental measurements, and the verification of the inverted noise source levels with experimental results during typhoon period. A method based on ray approach is presented for modeling underwater ambient noise fields generated by typhoons in deep ocean. Besides, acoustic field reciprocity is utilized to decrease the calculation amount in modeling ambient noise field. What is more, the depth dependence and the vertical directionality of noise field based on the modeling method and the Holland typhoon model are evaluated and analyzed in deep ocean. Furthermore, typhoons named “Soulik” in 2013 and “Nida” in 2016 passed by the receivers deployed in the western Pacific (WP) and the South China Sea (SCS). Variations in sound speed profile, bathymetry, and the related oceanic meteorological parameters are analyzed and taken into consideration for modeling noise field. Boundary constraint simulated annealing (SA) method is utilized to invert the three parameters of noise source levels and to minimize the objective function value. The prediction results with the inverted noise source levels exhibit good agreement with the measured experiment data and are compared with predicted results with other noise sources levels derived in previous researches.

Long-term structural monitoring of multi-story RC structures, based on data extracted from ambient noise and earthquake vibrations

2021 ◽  
Author(s):  
Alexandru Tiganescu ◽  
Bogdan Grecu ◽  
Iolanda-Gabriela Craifaleanu ◽  
Dragos Toma-Danila ◽  
Stefan-Florin Balan
Keyword(s):  
Monitoring System ◽  
Ambient Noise ◽  
Critical Issue ◽  
Seismic Events ◽  
Noise Sources ◽  
Rc Structures ◽  
Infrastructure Monitoring ◽  
Human Operators ◽  
The Impact

<p>The impact of natural hazards on structures and infrastructures is a critical issue that needs to be properly addressed by both public and private entities. To better cope with seismic hazard and to mitigate the risk, long-term multi-sensor infrastructure monitoring represents a useful tool for acquiring information on their condition and vulnerability. However, the current increasing data volume collected using sensors is not suitable to be processed with classical standalone methods. Thus, automatic algorithms and decision-making frameworks should be developed to use this data, with minimum intervention from human operators. A case-study for the application of advanced methods is focused on the headquarters of the Institute for Atomic Physics, a 11-story reinforced concrete building, located near Bucharest, Romania. The instrumentation scheme consists of accelerometers installed at the basement, at an intermediate floor and at the top of the structure. The data were continuously recorded, starting with December 2013. More than 80 seismic events with moment magnitude, M<sub>W</sub>, larger than 3.8 were recorded during the monitoring period. The current study covers the long-term evolution and variation of dynamic parameters (one value per hour), based on both ambient noise sources and small and medium magnitude seismic events. The seasonal variation of these parameters will be determined, as well as their daily variation and the differences between values obtained from ambient noise and from earthquake-induced vibrations. Other atmospheric parameters (e.g. temperature, precipitation, wind speed) will be considered in future studies. The goal of the PREVENT project, in the framework of which the research is performed, is to collect multi-disciplinary data and to integrate them into a complex monitoring system. The current study achieved the first step, focusing on data from the seismic sensors and setting up the premises for a multi-sensor, multi-parameter, more reliable infrastructure monitoring system.  </p>

Source-structure trade-offs in noise correlation functions in 3-D

2020 ◽  
Author(s):  
Korbinian Sager ◽  
Christian Boehm ◽  
Victor Tsai
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Hessian Matrix ◽  
Seismic Wave Propagation ◽  
Source Distribution ◽  
Earth Structure ◽  
Noise Correlation ◽  
Noise Sources ◽  
Trade Offs ◽  
Source Structure

<p>Noise correlation functions are shaped by both noise sources and Earth structure. The extraction of information is thus inevitably affected by source-structure trade-offs. Resorting to the principle of Green’s function retrieval deceptively renders the distribution of ambient noise sources unimportant and existing trade-offs are typically ignored. In our approach, we consider correlation functions as self-consistent observables. We account for arbitrary noise source distributions in both space and frequency, and for the complete seismic wave propagation physics in 3-D heterogeneous and attenuating media. We are therefore not only able to minimize the detrimental effect of a wrong (homogeneous) source distribution on 3D Earth structure by including it as an inversion parameter, but also to quantify underlying trade-offs.</p><p>The forward problem of modeling correlation functions and the computation of sensitivity kernels for noise sources and Earth structure are implemented based on the spectral-element solver Salvus. We extend the framework with the evaluation of second derivatives in terms of Hessian-vector products. In the context of probabilistic inverse problems, the inverse Hessian matrix in the vicinity of an optimal model with vanishing first derivatives and under the assumption of Gaussian statistics can be interpreted as an approximation of the posterior covariance matrix. The Hessian matrix therefore contains all the information on resolution and trade-offs that we are trying to retrieve. We investigate the geometry of trade-offs and the effect of the measurement type. In addition, since we only invert for sources at the surface of the Earth, we study how potential scatterers at depth are mapped into the inferred source distribution.</p><p>A profound understanding of the physics behind correlation functions and the quantification of trade-offs is essential for full waveform ambient noise inversion that aims to exploit waveform details for the benefit of improved resolution compared to traditional ambient noise tomography.</p>

The other ocean: probing Europa’s interior with natural ambient noise sources

2001 ◽  
Vol 109 (5) ◽  
pp. 2371-2371
Author(s):  
Aaron M. Thode ◽  
Michele Zanolin ◽  
Sunwoong Lee ◽  
Purnima Ratilal ◽  
Josh Wilson ◽  
...  
Keyword(s):  
Ambient Noise ◽  
The Other ◽  
Noise Sources

Multichannel myopic deconvolution using ambient noise sources

2014 ◽  
Vol 135 (4) ◽  
pp. 2360-2360
Author(s):  
Ning Tian ◽  
Justin Romberg ◽  
Karim Sabra
Keyword(s):  
Ambient Noise ◽  
Noise Sources

scholarly journals An adapted eigenvalue-based filter for ocean ambient noise processing

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. KS29-KS38 ◽  
Author(s):  
Guoli Wu ◽  
Hefeng Dong ◽  
Ganpan Ke ◽  
Junqiang Song
Keyword(s):  
Covariance Matrix ◽  
Ambient Noise ◽  
Green's Functions ◽  
Green’S Functions ◽  
Homogeneous Distribution ◽  
Noise Sources ◽  
Data Set ◽  
Recording Time ◽  
Receiver Array ◽  
Sample Covariance

Accurate approximations of Green’s functions retrieved from the correlations of ambient noise require a homogeneous distribution of random and uncorrelated noise sources. In the real world, the existence of highly coherent, strong directional noise generated by ships, earthquakes, and other human activities can result in biases in the ambient-noise crosscorrelations (NCCs). We have developed an adapted eigenvalue-based filter to attenuate the interference of strong directional sources. The filter is based on the statistical model of the sample covariance matrix and can separate different components of the data covariance matrix in the eigenvalue spectrum. To improve the effectiveness and make it adaptable for different data sets, a weight is introduced to the filter. Then, the NCCs can be calculated directly from the filtered data covariance matrix. This approach is applied to a 1.02 h data set of ambient noise recorded by a permanent reservoir monitoring receiver array installed on the seabed. The power spectral density indicates that the noise recordings were contaminated by strong directional noise over nearly half of the whole observation period. Beamforming and crosscorrelation results indicate that the interference still exists even after applying traditional temporal and spectral normalization techniques, whereas the adapted eigenvalue-based filter can significantly attenuate it and help to obtain improved crosscorrelations. The approach makes it possible to retrieve reliable approximations of Green’s functions over a much shorter recording time.

COMPUTATION OF NOISE DIRECTIONALITY, COHERENCE AND ARRAY RESPONSE IN RANGE DEPENDENT MEDIA WITH CANARY

2001 ◽  
Vol 09 (02) ◽  
pp. 327-345 ◽  
Author(s):  
C. H. HARRISON ◽  
R. BRIND ◽  
A. COWLEY
Keyword(s):  
Analytical Solutions ◽  
Ambient Noise ◽  
Numerical Algorithms ◽  
Noise Model ◽  
Test Cases ◽  
Noise Sources ◽  
Source Density ◽  
Or Groups

The ambient noise model CANARY calculates noise coherence and array noise response by treating the noise sources as surface distributions rather than points. This assumption leads to simplification of the propagation, even in range-dependent environments, and by allowing variations in the source density one can represent distant storms or groups of ships. Included is a description of the numerical algorithms used to calculate coherence. Some recently developed analytical solutions for uniform source distributions and uniformly sloping seabed3 are used as test cases for CANARY. Some additional examples demonstrate CANARY's performance in more realistic environments and conditions including wind and shipping sources, and comparisons are made with the noise model RANDI.

Beamforming Reliability of DAS Ambient Noise Data and Wave Modes Identification

2021 ◽  
Author(s):  
Yumin Zhao ◽  
Yunyue Elita Li
Keyword(s):  
Ambient Noise ◽  
Amplitude Ratio ◽  
Propagation Direction ◽  
Love Waves ◽  
Motor Vehicles ◽  
Noise Sources ◽  
Rayleigh And Love Waves ◽  
Noise Data ◽  
Campus Drive ◽  
Wave Modes

<p>Ambient noise generated by the anthropological activities in the urban environments may contain both Rayleigh and Love waves. Due to the differences in the physics of Rayleigh and Love waves, a pre-knowledge of the wave modes in the cross-correlogram is essential for an accurate inversion of the subsurface velocity model. Several studies (Martin and Biondi, 2017; Martin et al., 2017; Luo et al., 2020) demonstrated that only Rayleigh waves can be extracted by cross-correlation if the virtual source is colinear with the DAS array based on the assumption that the ambient noise sources are random and uniformly distributed. However, in realistic cases, ambient noise sources may come from a certain direction (e.g., Dou et al., 2017; Zhang et al., 2019). Moreover, the source propagation direction should be resolved and used to correct the apparent dispersion curves. Zhao et al. (2020) and van den Ende et al. (2020) proposed that beamforming results are not always reliable due to the measurements of DAS.</p><p>Based on the synthetic DAS ambient noise data recorded by a near “L” shape array (Source-West corner of the Stanford DAS-1 array), we prove that beamforming can resolve the source direction when the ambient sources are mainly coming from one direction. Two important processing procedures are that: check the polarity in the data and apply polarity flip on one part of the data; apply amplitude normalization on the data if strong amplitude difference exits in the data. Based on the source direction, the coordinate of the DAS array, and amplitude ratio of the data recorded by the two segments of the DAS array, we propose an inversion method to calculate the amplitude ratio of the Rayleigh and Love waves generated by the ambient sources.</p><p>We apply the method to two 100-second DAS ambient noise data recorded by the Stanford DAS-1 array. We first resolve the source propagation direction from the two data. The results indicate that the ambient noise in the data were mainly generated by the motor vehicles running on the Campus Drive in the northwest of the array. Then we invert for the Rayleigh and Love waves amplitude ratio using the proposed method. The ratios for the two data are 0.2 and 0.13, respectively. The results suggest that the ambient noise generated by motor vehicles running on the northwest corner of the Campus Drive mainly contain Love waves.</p>

Sign in / Sign up

Export Citation Format

Share Document