On low-frequency errors of uniformly modulated filtered white-noise models for ground motions

1988 ◽  
Vol 16 (3) ◽  
pp. 381-388 ◽  
Author(s):  
Erdal Safak ◽  
David M. Boore
Keyword(s):  
White Noise ◽  
Ground Motions ◽  
Low Frequency ◽  
Noise Models

Effect of waste rock inclusions on the seismic stability of an upstream raised tailings impoundment: a numerical investigation

2015 ◽  
Vol 52 (12) ◽  
pp. 1930-1944 ◽  
Author(s):  
Behnam Ferdosi ◽  
Michael James ◽  
Michel Aubertin
Keyword(s):  
Ground Motions ◽  
Low Frequency ◽  
Horizontal Displacement ◽  
Waste Rock ◽  
Seismic Stability ◽  
Environmental Damage ◽  
Total Width ◽  
Tailings Impoundment ◽  
Downstream Slope ◽  
Potential Failure

Over the years, seismic activity has been a relatively common cause of tailings impoundment failure. The flow of liquefied tailings from such ruptures can result in very severe consequences, including loss of life and environmental damage. A co-disposal technique consisting of placing waste rock inclusions in tailings impoundments prior to and during tailings deposition was proposed by the authors. The waste rock is placed to create continuous inclusions within the impoundment, which provide a number of environmental and geotechnical benefits, particularly with respect to seismic stability. The results of numerical simulations previously performed have shown that the UBCSAND model can predict the seismic response of tailings. The UBCSAND constitutive model was used to conduct simulations to evaluate of the use of waste rock inclusions to improve the seismic stability of a tailings impoundment. The evaluation consists of numerical analyses of an actual tailings impoundment as constructed (without inclusions), and then assuming that it was constructed with inclusions, subjected to earthquake loads of various energy contents and with different predominant frequencies. The analyses were conducted in static, seismic, and post-shaking phases. The displacement of the surface of downstream slope of the tailings dyke was recorded during the analyses. The results indicate that the presence of waste rock inclusions can significantly improve the seismic behavior of the impoundment by reducing the displacements of the surface of the downstream slope and the extent of potential failure zones. Also, the results show that in most cases, the influence of a low-frequency earthquake on the displacement of the downstream slope of the tailings dyke is more important than that of a high-frequency earthquake. The performances of the tailings impoundment with different configurations of waste rock inclusions (varying width and center-to-center spacing) were classified based on the average normalized horizontal displacement of the downstream slope (ARx) for a range input ground motions. Charts were then developed to show how ARx is influenced by the total width of inclusions, their spacing, and the input ground motions.

scholarly journals The International Pulsar Timing Array: second data release

2019 ◽  
Vol 490 (4) ◽  
pp. 4666-4687 ◽  
Author(s):  
B B P Perera ◽  
M E DeCesar ◽  
P B Demorest ◽  
M Kerr ◽  
L Lentati ◽  
...  
Keyword(s):  
Gravitational Waves ◽  
High Precision ◽  
Noise Analysis ◽  
Low Frequency ◽  
The North ◽  
Pulsar Timing ◽  
Data Release ◽  
Timing Data ◽  
Noise Models ◽  
Pulsar Timing Array

ABSTRACT In this paper, we describe the International Pulsar Timing Array second data release, which includes recent pulsar timing data obtained by three regional consortia: the European Pulsar Timing Array, the North American Nanohertz Observatory for Gravitational Waves, and the Parkes Pulsar Timing Array. We analyse and where possible combine high-precision timing data for 65 millisecond pulsars which are regularly observed by these groups. A basic noise analysis, including the processes which are both correlated and uncorrelated in time, provides noise models and timing ephemerides for the pulsars. We find that the timing precisions of pulsars are generally improved compared to the previous data release, mainly due to the addition of new data in the combination. The main purpose of this work is to create the most up-to-date IPTA data release. These data are publicly available for searches for low-frequency gravitational waves and other pulsar science.

Spectral Composition of Concurrent Noise Affects Neuronal Sensitivity to Interaural Time Differences of Tones in the Dorsal Nucleus of the Lateral Lemniscus

2007 ◽  
Vol 98 (5) ◽  
pp. 2705-2715 ◽  
Author(s):  
Ida Siveke ◽  
Christian Leibold ◽  
Benedikt Grothe
Keyword(s):  
White Noise ◽  
Response Rate ◽  
Spectral Composition ◽  
Low Frequency ◽  
Maximal Response ◽  
Lateral Lemniscus ◽  
Interaural Time Differences ◽  
Binaural Cues ◽  
Dorsal Nucleus ◽  
Monaural Stimulation

We are regularly exposed to several concurrent sounds, producing a mixture of binaural cues. The neuronal mechanisms underlying the localization of concurrent sounds are not well understood. The major binaural cues for localizing low-frequency sounds in the horizontal plane are interaural time differences (ITDs). Auditory brain stem neurons encode ITDs by firing maximally in response to “favorable” ITDs and weakly or not at all in response to “unfavorable” ITDs. We recorded from ITD-sensitive neurons in the dorsal nucleus of the lateral lemniscus (DNLL) while presenting pure tones at different ITDs embedded in noise. We found that increasing levels of concurrent white noise suppressed the maximal response rate to tones with favorable ITDs and slightly enhanced the response rate to tones with unfavorable ITDs. Nevertheless, most of the neurons maintained ITD sensitivity to tones even for noise intensities equal to that of the tone. Using concurrent noise with a spectral composition in which the neuron's excitatory frequencies are omitted reduced the maximal response similar to that obtained with concurrent white noise. This finding indicates that the decrease of the maximal rate is mediated by suppressive cross-frequency interactions, which we also observed during monaural stimulation with additional white noise. In contrast, the enhancement of the firing rate to tones at unfavorable ITD might be due to early binaural interactions (e.g., at the level of the superior olive). A simple simulation corroborates this interpretation. Taken together, these findings suggest that the spectral composition of a concurrent sound strongly influences the spatial processing of ITD-sensitive DNLL neurons.

Effect of Ge-Nanoislands on the Low-Frequency Noise in Si/SiOx/Ge Structures

2013 ◽  
Vol 854 ◽  
pp. 21-27 ◽  
Author(s):  
N.P. Garbar ◽  
Valeriya N. Kudina ◽  
V.S. Lysenko ◽  
S.V. Kondratenko ◽  
Yu.N. Kozyrev
Keyword(s):  
High Surface ◽  
Low Frequency ◽  
Noise Model ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Physical Processes ◽  
Noise Sources ◽  
Noise Component ◽  
First Time ◽  
Noise Models

Low-frequency noise of the structures with Ge-nanoclusters of rather high surface density grown on the oxidized silicon surface is investigated for the first time. It was revealed that the 1/f γ noise, where γ is close to unity, is the typical noise component. Nevertheless, the 1/f γ noise sources were found to be distributed nonuniformly upon the oxidized silicon structure with Ge-nanoclusters. The noise features revealed were analyzed in the framework of widely used noise models. However, the models used appeared to be unsuitable to explain the noise behavior of the structures studied. The physical processes that should be allowed for to develop the appropriate noise model are discussed.

scholarly journals Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods

2020 ◽  
Vol 10 (18) ◽  
pp. 6210
Author(s):  
Ruihao Zheng ◽  
Chen Xiong ◽  
Xiangbin Deng ◽  
Qiangsheng Li ◽  
Yi Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Ground Motions ◽  
Low Frequency ◽  
Time History ◽  
Time History Analysis ◽  
Machine Learning Algorithms ◽  
Learning Networks ◽  
History Analysis ◽  
Deep Layers

This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R2 = 0.8737) compared with that of the BPNN (R2 = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.

Spatial Variability of Source and Attenuation Characteristics in Large Ground-Motion Datasets

2020 ◽  
Author(s):  
Sreeram Reddy Kotha ◽  
Graeme Weatherill ◽  
Dino Bindi ◽  
Fabrice Cotton
Keyword(s):  
Spatial Variability ◽  
Ground Motion ◽  
Ground Motions ◽  
Low Frequency ◽  
Strong Motion ◽  
Response Spectra ◽  
Large Datasets ◽  
Earthquake Scenarios ◽  
Crustal Earthquakes ◽  
Geographical Regions

<p>Ground-Motion Models (GMMs) characterize the random distributions of ground-motions for a combination of earthquake source, wave travel-path, and the effected site’s geological properties. Typically, GMMs are regressed over a compendium of strong ground-motion recordings collected from several earthquakes recorded at multiple sites scattered across a variety of geographical regions. The necessity of compiling such large datasets is to expand the range of magnitude, distance, and site-types; in order to regress a GMM capable of predicting realistic ground-motions for rare earthquake scenarios, e.g. large magnitudes at short distances from a reference rock site. The European Strong-Motion (ESM) dataset is one such compendium of observations from a few hundred shallow crustal earthquakes recorded at a several hundred seismic stations in Europe and Middle-East.</p><p>We developed new GMMs from the ESM dataset, capable of predicting both the response spectra and Fourier spectra in a broadband of periods and frequencies, respectively. However, given the clear tectonic and geological diversity of the data, possible regional and site-specific differences in observed ground-motions needed to be quantified; whilst also considering the possible contamination of data from outliers. Quantified regional differences indicate that high-frequency ground-motions attenuate faster with distance in Italy compared to the rest of Europe, as well as systematically weaker ground-motions from central Italian earthquakes. In addition, residual analyses evidence anisotropic attenuation of low frequency ground-motions, imitating the pattern of shear-wave energy radiation. With increasing spatial variability of ground-motion data, the GMM prediction variability apparently increases. Hence, robust mixed-effects regressions and residual analyses are employed to relax the ergodic assumption.</p><p>Large datasets, such as the ESM, NGA-West2, and from KiK-Net, provide ample opportunity to identify and evaluate the previously hypothesized event-to-event, region-to-region, and site-to-site differences in ground-motions. With the appropriate statistical methods, these variabilities can be quantified and applied in seismic hazard and risk predictions. We intend to present the new GMMs: their development, performance and applicability, prospective improvements and research needs.</p>

Low-Frequency Seiche in a Large Bay

2015 ◽  
Vol 45 (3) ◽  
pp. 716-723 ◽  
Author(s):  
Jamie MacMahan
Keyword(s):  
White Noise ◽  
Low Frequency ◽  
Sea Surface ◽  
Monterey Bay ◽  
Short Term ◽  
Infragravity Waves ◽  
Nodal Structure ◽  
Nodal Pattern ◽  
Resonant Forcing

AbstractShort-term observations of sea surface elevations η along the 10-m isobath and long-term observations inside and outside of a large bay (Monterey Bay, CA) were obtained to describe the nodal structure of the modes 0–3 seiches within the bay and the low-frequency (<346 cpd) seiche forcing mechanism. The measured nodal pattern validates previous numerical estimates associated with a northern amplitude bias, though variability exists across the modal frequency band, particularly for modes 0 and 1. Low-frequency oceanic η white noise within seiche frequency bands (24–69 cpd) provides a continuous resonant forcing of the bay seiche with a η2 (variance) amplification of 16–40 for the different modes. The temporal variation of the oceanic η white noise is significantly correlated (R2 = 0.86) at the 95% confidence interval with the bay seiche η that varies seasonally. The oceanic η white noise is hypothesized as being from low-frequency, free, infragravity waves that are forced by short waves.

Signal de-noising method for vibration signal of flood discharge structure based on combined wavelet and EMD

2015 ◽  
Vol 23 (15) ◽  
pp. 2401-2417 ◽  
Author(s):  
Jianwei Zhang ◽  
Qi Jiang ◽  
Bin Ma ◽  
Yu Zhao ◽  
Lianghuan Zhu
Keyword(s):  
White Noise ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Vibration Signal ◽  
Arch Dam ◽  
Elastic Model ◽  
Flood Discharge ◽  
Reconstructed Signal ◽  
Discharge Structure ◽  
Filtering Effect

A new de-noising method combining Wavelet threshold and empirical mode decomposition (EMD) (WTEMD for short) is proposed to improve the precision of de-noising performance for vibration signal of flood discharge structure in low signal to noise ratio (SNR). White noise is partially filtered out by decomposing the vibration signal with wavelet. Then conducting the further EMD on wavelet reconstructed signal to obtain Intrinsic Mode Function (IMF), through analyzing spectrum diagram of every IMF component, low-frequency waterflow noise and the rest of high-frequency white noise are filtered out, regarding SNR and root mean square error (RMSE) as evaluation index for noise reduction effect. The novelty of this method is that it can reduce the endpoint effect of EMD. By comparing the filtering effect of WTEMD with other methods on simulation signals, study shows that, WTEMD has a higher precision and a better de-noising effect. The dominant vibration information of dam structure is achieved by using WTEMD in Laxiwa arch dam hydro-elastic model and Three Gorges Dam, which can provide the basis for safe operation and on-line monitoring of the dam structure. This method can effectively solve the problem of dominant information extraction for large flood discharge structure.

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