The influences of large earthquake signals on the recovery of surface waves from ambient noise cross-correlation functions

Ambient noise tomography(ANT)has been widely used to image crust and upmost mantle structures.ANT assumes that sources of ambient noise are diffuse and evenly distributed in space and the energy of different modes is equipartitioned.At present,the sources of the primary and the secondary microseisms are well studied,but there are only a few on the studies of long-period ambient noise sources.In this study,we study the effects of large earthquake signals on the recovery of surface waves from seismic ambient noise data recorded by seismic stations from the US permanent networks and Global Seismographic Network(GSN).Our results show that large earthquake signals play an important role on the recovery of long-period surface waves from ambient noise cross-correlation functions.Our results are consistent with previous studies that suggest the contribution of earthquake signals to the recovery of surface waves from cross-correlations of ambient noise is dominant at periods larger than 20–40 s.

Composition of high frequency ambient noise from cross-correlation:A case study using a small aperture array

Long-time cross correlation of ambient noise has been proved as a powerful tool to extract Green＇s function between two receivers. The study of composition of ambient noise is important for a better understanding of this method. Previous studies confirm that ambient noise in the long period （3 s and longer） mostly consists of surface wave, and 0.25-2.5 s noise consists more of body waves. In this paper, we perform cross correlation processing at much higher frequency （30-70 Hz） using ambient noise recorded by a small aperture array. No surface waves emerge from noise correlation function （NCF）, but weak P waves emerge. The absence of surface wave in NCF is not due to high attenuation since surface waves are strong from active source, therefore probably the high ambient noise mostly consists of body wave and lacks surface wave. Origin of such high frequency body waves in ambient noise remains to be studied.

Public cloud computing for seismological research:Calculating large-scale noise cross-correlations using ALIYUN

The amount of seismological data is rapidly increasing with accumulating observational time and increasing number of stations, requiring modern technique to provide adequate computing power. In present study, we proposed a framework to calculate large-scale noise crosscorrelation functions(NCFs) using public cloud service from ALIYUN. The entire computation is factorized into small pieces which are performed parallelly on specified number of virtual servers provided by the cloud. Using data from most seismic stations in China, five NCF databases are built. The results show that, comparing to the time cost using a single server, the entire time can be reduced over two orders of magnitude depending number of evoked virtual servers. This could reduce computation time from months to less than 12 hours. Based on obtained massive NCFs, the global body waves are retrieved through array interferometry and agree well with those from earthquakes. This leads to a solution to process massive seismic dataset within an affordable time and is applicable to other large-scale computing in seismological researches.

Time correction of the ocean bottom seismometers deployed at the southwest Indian ridge using ambient noise cross-correlation

Seismic monitoring using ocean bottom seismometers（OBS） is an efficient method for investigating earthquakes in mid-ocean ridge far away from land. Clock synchronization among the OBSs is difficult without direct communication because electromagnetic signals cannot propagate efficiently in water. Time correction can be estimated through global positioning system（GPS） synchronization if clock drift is linear before and after the deployment. However, some OBSs in the experiments at the southwest Indian ridge（SWIR） on the Chinese DY125-34 cruise had not been re-synchronized from GPS after recovery. So we attempted to estimate clock drift between each station pairs using time symmetry analysis（TSA） based on ambient noise cross-correlation. We tested the feasibility of the TSA method by analyzing daily noise cross-correlation functions（NCFs） that extract from the data of another OBS experiment on the Chinese DY125-40 cruise with known clock drift and the same deployment site. The results suggest that the NCFs＇ travel time of surface wave between any two stations are symmetrical and have an opposite growing direction with the date. The influence of different band-pass filters,different components and different normalized methods was discussed. The TSA method appeared to be optimal for the hydrophone data within the period band of 2–5 s in dozens of km-scale interstation distances. A significant clock drift of ~2 s was estimated between OBSs sets through linear regression during a 108-d deployment on the Chinese cruise DY125-34. Time correction of the OBS by the ambient noise cross-correlation was demonstrated as a practical approach with the appropriate parameters in case of no GPS re-synchronization.

Local-scale cross-correlation of seismic noise from the Calico fault experiment

Most studies of seismic noise cross-correlation （NCC） have focused on regional/continental scale imaging using empirical surface-wave Green＇s functions extracted from primary （0.05-0.08 Hz） and secondary （0.1-0.16 Hz） microseisms. In this work, we present the NCC results at higher frequencies （〉0.5 Hz） from 6 months seismic noise recorded by a local array （ - 4 km aperture） deployed along the Calico fault in the Mojave Desert, California. Both fast and slow propagating waves are observed from the NCC record-sections. We compare the NCCs from sensor pairs that share a common sensor with the records of a borehole shot located very close to this common sensor. The result shows a good match of the slow surface-wave arrivals, indicating that the NCC method is able to recover unbiased surface-wave Green＇s functions at local scales. The strong body-wave NCC component is caused by the P waves generated offshore California. Along a SW-NE profile across the fault, we observe apparent P-wave arrivals and their reflections, which can be explained by a low-velocity- zone （LVZ） along the Calico fault. We calculat6 the LVZ width to be - 2.3 kin, and the P-wave velocity reduction within the LVZ to be -35 %. These estimates are consistent with other evidence for a relatively wide LVZ along the Calico fault.

Effects of signal modulation and coloured cross-correlation of coloured noises on the diffusion of a harmonic oscillator

The diffusion in a harmonic oscillator driven by coloured noises ξ（t） and η（t） with coloured cross-correlation in which one of the noises is modulated by a biased periodic signal is investigated. The exact expression of diffusion coefficient d as a function of noise parameter, signal parameter, and oscillator frequency is derived. The findings in this paper are as follows. 1） The curves of d versus noise intensity D and d versus noises cross-correlation time z3 exist as two different phases. The transition between the two phases arises from the change of the cross-correlation coefficient A of the two Ornstein-Uhlenbeck （O-U） noises. 2） Changing the value of T3, the curves of d versus Q, the intensity of colored noise that is modulated by the signal, can transform from a phase having a minimum to a monotonic phase. 3） Changing the value of signal amplitude A, d versus Q curves can transform from a phase having a minimum to a monotonic phase. The above-mentioned results demonstrate that a like noise-induced transition appears in the model.

Stochastic resonance in a gain-noise model of a single-mode laser driven by pump noise and quantum noise with cross-correlation between real and imaginary parts under direct signal modulation

Stochastic resonance （SR） is studied in a gain-noise model of a single-mode laser driven by a coloured pump noise and a quantum noise with cross-correlation between real and imaginary parts under a direct signal modulation. By using a linear approximation method, we find that the SR appears during the variation of signal-to-noise ratio （SNR） separately with the pump noise self-correlation time τ, the noise correlation coefficient between the real part and the imaginary part of the quantum noise λq, the attenuation coefficient γ＇ and the deterministic steady-state intensity I0. In addition, it is found that the SR can be characterized not only by the dependence of SNR on the noise variables of and λq, but also by the dependence of SNR on the laser system variables of γ and I0. Thus our investigation extends the characteristic quantity of SR proposed before.

Fast estimation of distance between two hydrophones using ocean ambient noise in multi-ship scenarios

Accurately estimating the bearing of a target with two hydrophones requires knowing the precise distance between them.However,in practice,it is difficult to measure this distance accurately due to the influence of current.To solve this problem,we propose a method for extracting the time-domain Green's function between two points in multi-ship scenarios and for extracting the time-domain waveform arrival structure between two hydrophones in real-time based on long samples of ship radiation noise cross-correlation.Using the cross-correlation function of the radiated noise from any ship located in the end-fire direction of the two hydrophones,we can estimate the distance between the hydrophones in real-time.To verify the accuracy of our estimation,we compare the result of azimuth estimation with the actual azimuth based on the azimuth estimation of a cooperative sound source in the maritime environment.Our experimental results show that the proposed method correctly estimates the distance between two hydrophones that cannot be directly measured and estimates the position of a cooperative sound source 4 km away with an average deviation of less than 1.2°.

Temporal variation of noise correlation function(NCF) in Beijing and surroundings:Its relation with climate events and implications

Noise correlation function （NCF） was calculated using the data of the Beijing Capital-Area Telemetered Digital Seismograph Network from June 12 to September 12, 2005. Signal-to-noise ratio （SNR） is used to characterize the quality of NCF at each station pair. The SNR （in dB） is shown to be dependent on the separation distance R of the station pair via SNR= A -BlogR. ＇Normalized average SNR＇ for all the station pairs can then be calculated, as represented by the value of SNR taking R = 250 km in the empirical SNR-R relation, to measure the overall quality of the NCF result. The ＇normalized average SNR＇ of the NCF shows temporal variation and is apparently dependent on the root-mean-square （RMS） velocity of the microseism. The result obtained by this experiment provides clues to the explanation of the properties of NCF, such as the dominant mechanism underlying （diffuse wave fields or uncorrelated sources）, and the dependence of SNR on the time length of recordings.

Random vibration of hysteretic systems under Poisson white noise excitations

Hysteresis widely exists in civil structures,and dissipates the mechanical energy of systems.Research on the random vibration of hysteretic systems,however,is still insufficient,particularly when the excitation is non-Gaussian.In this paper,the radial basis function(RBF)neural network(RBF-NN)method is adopted as a numerical method to investigate the random vibration of the Bouc-Wen hysteretic system under the Poisson white noise excitations.The solution to the reduced generalized Fokker-PlanckKolmogorov(GFPK)equation is expressed in terms of the RBF-NNs with the Gaussian activation functions,whose weights are determined by minimizing the loss function of the reduced GFPK equation residual and constraint associated with the normalization condition.A steel fiber reinforced ceramsite concrete(SFRCC)column loaded by the Poisson white noise is studied as an example to illustrate the solution process.The effects of several important parameters of both the system and the excitation on the stochastic response are evaluated,and the obtained results are compared with those obtained by the Monte Carlo simulations(MCSs).The numerical results show that the RBF-NN method can accurately predict the stationary response with a considerable high computational efficiency.

Multiquadric Radial Basis Function Approximation Scheme for Solution of Total Variation Based Multiplicative Noise Removal Model

This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.

Influence of Noise on Time Evolution of Intensity Correlation Function

Using the linear approximation method, we have studied how the correlation function C(t) of the laser intensity changes with time in the loss-noise model of the single-mode laser driven by the colored pump noise with signal modulation and the quantum noise with cross-correlation between the real and imaginary parts. We have found that when the pump noise self-correlation time τ changes, (I) in the case ofτ < 1, the C(t) vs. T curve experiences a changing process from the monotonous descending to monotonous rise, and finally to the appearance of a maximum; (ii) in the case of τ > 1, the curve only exhibits periodically surging with descending envelope. When τ < 1 and τ does not change, with the increase of the pump noise intensity P, the curve experiences a repeated changing process, that is, from the monotonous descending to the appearance of a maximum, then to monotonous rise, and finally to the appearance of a maximum again. With the increase of the quantum noise intensity Q, the curve experiences a changing process from the monotonous rise to the appearance of a maximum, and finally to the monotonous descending. The increase of the quantum noise with cross-correlation between the real and imaginary parts will lead to the fall of the whole curve, but not affect the form of the time evolution of C(t).

Simulation study of the two-time intensity correlation function of a two-mode laser system with both pump and quantum noises

This paper investigates the two-time intensity correlation function of a two-mode ring laser system subjected to both pump and quantum noises by stochastic simulation. It finds that the decay rate of the intensity correlation function of one mode gets faster with decreasing values of relevant parameters, i.e., the coupling constant ξ, the cross-correlation coefficient A, the difference of the pump parameters Aa and the pump parameter al; however, its variations get complex in the other mode when relevant parameters are changed. The investigating results also show that the effects of the mode competition on intensity correlation function are obvious.

Effects of Noise Pollution on Functions of the Liver and Kidney of Rats

To study the effects of noise pollution on the functions of the liver and kidney of rats, a total of 40 male SPF Wistar rats were randomly divided into a control group and three experimental groups. The rats in the experimental groups were respectively stimulated with 38, 55 and 70 dB noise for 15 days, and the levels of blood components were determined by an automatic biochemical analyzer. The results showed that in compari-son with the control group, the level of the blood glucose in the experimental groups increased by 23.53%, 52.94% and 88.24%, respectively, and the differences were statistically significant （P〈0.01）. The levels of triglyceride in the blood rose by 20.83%, 38.54% and 79.68%, respectively, and the differences were also statistically significant （P〈0.01）. The level of globulin in the blood increased by 16.49%, 21.13% and 51.78%, and the level of albumin in the blood rose by 9.51 %, 12.67% and 17.89%, respectively. The level of total bilirubin in the blood increased by 27.04%, 41.63% and 73.67%, respectively, and the differences were statistically significant （P〈0.01）. The level of creatinine in the blood rose by 9.72%, 10.21% and 20.99%, respectively. The level of amylase in the blood reduced by 6.6%, 13.05% and 23.89%, respectively. The level of creatine kinase in the blood decreased by 19.81%, 27.37% and 36.81 %, respectively, and the level of urea in the blood reduced by 11.19%, 12.77% and 19.26%, respectively. The results revealed that noise pollution could significantly affect the levels of blood components and the functions of the liver and kidney of rats.

Average Power Function of Noise and Its Applications in Seasonal Time Series Modeling and Forecasting

This paper presents a new method of detecting multi-periodicities in a seasonal time series. Conventional methods such as the average power spectrum or the autocorrelation function plot have been used in detecting multiple periodicities. However, there are numerous cases where those methods either fail, or lead to incorrectly detected periods. This, in turn in applications, produces improper models and results in larger forecasting errors. There is a strong need for a new approach to detecting multi-periodicities. This paper tends to fill this gap by proposing a new method which relies on a mathematical instrument, called the Average Power Function of Noise (APFN) of a time series. APFN has a prominent property that it has a strict local minimum at each period of the time series. This characteristic helps one in detecting periods in time series. Unlike the power spectrum method where it is assumed that the time series is composed of sinusoidal functions of different frequencies, in APFN it is assumed that the time series is periodic, the unique and a much weaker assumption. Therefore, this new instrument is expected to be more powerful in multi-periodicity detection than both the autocorrelation function plot and the average power spectrum. Properties of APFN and applications of the new method in periodicity detection and in forecasting are presented.

Ambient Noise Tomography, Green’s Function and Earthquakes

Green’s function is well-known, among others, in the application of ambient noise tomography methodologies that may demonstrate the potential of hydrocarbon entrapment in the study area. Here it is also shown to be of key importance in identifying the fractal dimension in the unified scaling law for earthquakes as well as in studying an explicit relationship of a future strong earthquake epicenter to the average earthquake potential score. Such studies are now in progress.

Unbiased parameter estimation of continuous-time system based on modulating functions with input and output white noises

An efficient unbiased estimation method is proposed for the direct identification of linear continuous-time system with noisy input and output measurements.Using the Gaussian modulating filters,by numerical integration,an equivalent discrete identification model which is parameterized with continuous-time model parameters is developed,and the parameters can be estimated by the least-squares (LS) algorithm.Even with white noises in input and output measurement data,the LS estimate is biased,and the bias is determined by the variances of noises.According to the asymptotic analysis,the relationship between bias and noise variances is derived.One equation relating to the measurement noise variances is derived through the analysis of the LS errors.Increasing the degree of denominator of the system transfer function by one,an extended model is constructed.By comparing the true value and LS estimates of the parameters between original and extended model,another equation with input and output noise variances is formulated.So,the noise variances are resolved by the set of equations,the LS bias is eliminated and the unbiased estimates of system parameters are obtained.A simulation example by comparing the standard LS with bias eliminating LS algorithm indicates that the proposed algorithm is an efficient method with noisy input and output measurements.

Salt and Pepper Noise Filter Based on GA-BP Algorithm Noise Detector

基于噪声检测的中值滤波器已广泛用于消除图像中的椒盐噪声，然而在高噪声密度情况下，对噪声像素的定位不准确很容易造成图像边缘的模糊。本文提出了一种基于GA-BP的椒盐噪声滤波算法，克服了这一缺陷。算法首先用遗传算法优化的BP网络对图像中的噪声像素定位，然后引入保边函数和PRP算法求目标函数的极值进而实现图像的去噪处理。实验结果表明，该算法比传统滤波算法效果有明显改善，且具有良好的泛化性、鲁棒性和自适应性。

Comparison of ground truth location of earthquake from InSAR and from ambient seismic noise: A case study of the 1998 Zhangbei earthquake

Because ambient seismic noise provides estimated Green’s function （EGF） between two sites with high accuracy, Rayleigh wave propagation along the path connecting the two sites is well resolved. Therefore, earthquakes which are close to one seismic station can be well located with calibration extracting from EGF. We test two algorithms in locating the 1998 Zhangbei earthquake, one algorithm is waveform-based, and the other is traveltime-based. We first compute EGF between station ZHB （a station about 40 km away from the epicenter） and five IC/IRIS stations. With the waveform-based approach, we calculate 1D synthetic single-force Green’s functions between ZHB and other four stations, and obtain traveltime corrections by correlating synthetic Green’s functions with EGFs in period band of 10–30 s. Then we locate the earthquake by minimizing the differential travel times between observed earthquake waveform and the 1D synthetic earthquake waveforms computed with focal mechanism provided by Global CMT after traveltime correction from EGFs. This waveform-based approach yields a location which error is about 13 km away from the location observed with InSAR. With the traveltime-based approach, we begin with measuring group velocity from EGFs as well as group arrival time on observed earthquake waveforms, and then locate the earthquake by minimizing the difference between observed group arrival time and arrival time measured on EGFs. This traveltime-based approach yields accuracy of 3 km, Therefore it is feasible to achieve GT5 （ground truth location with accuracy 5 km） with ambient seismic noises. The less accuracy of the waveform-based approach was mainly caused by uncertainty of focal mechanism.

Reconstruction of sub cross-correlation cancellation technique for unambiguous acquisition of BOC(kn, n) signals

In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.