Analysis of frequency shifting in seismic signals using Gabor-Wigner transform

A hybrid time-frequency method known as Gabor-Wigner transform (GWT) is introduced in this paper for examining the time-frequency patterns of earthquake damaged buildings. GWT is developed by combining the Gabor transform (GT) and Wigner-Ville distribution (WVD). GT and WVD have been used separately on synthetic and recorded earthquake data to identify frequency shifting due to earthquake damages, but GT is prone to windowing effect and WVD involves ambiguity function. Hence to obtain better clarity and to remove the cross terms (frequency interference), GT and WVD are judiciously combined and the resultant GWT used to identify frequency shifting. Synthetic seismic response of an instrumented building and real-time earthquake data recorded on the building were investigated using GWT. It is found that GWT offers good accuracy for even slow variations in frequency, good time-frequency resolution, and localized response. Presented results confirm the efficacy of GWT when compared with GT and WVD used separately. Simulation results were quantified by the Renyi entropy measures and GWT shown to be an adequate technique in identifying localized response for structural damage detection.

Singularity detection of the thin bed seismic signals with wavelet transform

The location of singularities may be detected by local maxima of the wavelet transform modulus. The digital modeling and focusing process to wavelet transform of the reflecting seismic signals have been done. It has been found that the locations of singularities after wavelet transform are only affected by two factors, their original locations and the seismic wavelet length, which says it does not matter with what shape the wavelet will be. The wavelet length can be determined according to the wavelet transform results and be eliminated thereafter so that we are able to detect thin bed seismic signal with resolution of l/32 wavelength. The singularities have been recovered with improved resolution of the seismic section by real data processing.

Joint Time-Frequency Analysis of Seismic Signals:A Critical Review

This paper presents an evaluation of time-frequency methods for the analysis of seismic signals.Background of the present work is to describe,how the frequency content of the signal is changing in time.The theoretical basis of short time Fourier transform,Gabor transform,wavelet transform,S-transform,Wigner distribution,Wigner-Ville distribution,Pseudo Wigner-Ville distribution,Smoothed Pseudo Wigner-Ville distribution,Choi-William distribution,Born-Jordan Distribution and cone shape distribution are presented.The strengths and weaknesses of each technique are verified by applying them to a particular synthetic seismic signal and recorded real time earthquake data.

Characteristics and interpretation of the seismic signal of a field-scale landslide dam failure experiment

Outburst floods caused by breaches of landslide dams may cause serious damages and loss of lives in downstream areas; for this reason the study of the dynamic of the process is of particular interest for hazard and risk assessment. In this paper we report a field-scale landslide dam failure experiment conducted in Nantou County, in the central of Taiwan.The seismic signal generated during the dam failure was monitored using a broadband seismometer and the signal was used to study the dam failure process.We used the short-time Fourier transform(STFT) to obtain the time–frequency characteristics of the signal and analyzed the correlation between the power spectrum density(PSD) of the signal and the water level. The results indicate that the seismic signal generated during the process consisted of three components: a low-frequency band(0–1.5 Hz), an intermediate-frequency band(1.5–10 Hz) and a highfrequency band(10–45 Hz). We obtained the characteristics of each frequency band and the variations of the signal in various stages of the landslide dam failure process. We determined the cause for the signal changes in each frequency band and its relationship with the dam failure process. The PSD sediment flux estimation model was used to interpret the causes of variations in the signal energy before the dam failure and the clockwise hysteresis during the failure. Our results show that the seismic signal reflects the physical characteristics of the landslide dam failure process. The method and equipment used in this study may be used to monitor landslide dams and providing early warnings for dam failures.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

Seismic Signal and Data Analysis of Rock Media with Vertical Anisotropy

This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.

Weak Seismic Signal Extraction Based on the Curvelet Transform

Seismic signal denoising is a key step in seismic data processing.Airgun signals are easy to be interfered with by noise when it travels a long distance due to the weak energy of active source signal of the airgun.Aiming to solve this problem,and considering that the conventional Curvelet transform threshold processing method does not use the seismic spectrum information,we independently process the Curvelet scale layer corresponding to valid data based on the characteristics of the Curvelet transform of multi-scale,multi-direction and capable of expressing the sparse seismic signals in order to fully excavate the information features.Combined with the Curvelet adaptive threshold denoising the algorithm,we apply the Curvelet transform to denoising seismic signals while retaining the weak information in the signal as much as possible.The simulation experiments show that the improved threshold denoising method based on Curvelet transform is superior to the frequency domain filtering,wavelet denoising and traditional Curvelet denoising method in detailed information extraction and signal denoising of low SNR signals.The calculation accuracy of the relative wave velocity variation of underground medium is improved.

Signal characteristics of coal and rock dynamics with micro-seismic monitoring technique

In this study,differences of signal characteristics between mine shocks and coal and gas outbursts in coal mines were examined with the micro-seismic monitoring technique and time–frequency analysis.The duration of the mine shock is short while the coal and gas outburst lasts longer.The outburst consists of three stages:the pre-shock,secondary shock and main shock stage,respectively.The velocity amplitude of the mine shock is between 10^(-)5and 10^(-3)m/s,which is higher than that of the outburst with the same energy level.In addition,in both cases,the correlation between the velocity amplitude and energy is positive while the correlation between the signal frequency band distribution and energy is negative.The signal frequency band of the high energy mine shock is distributed between 0 and 50 Hz,and the low energy mine shock is between 50 and 100 Hz.The fractal characteristics of mine shocks were studied based on a fractal theory.The box dimensions of high energy mine shocks are lower than the low energy ones,however,the box dimensions of outbursts are higher than that of mine shocks with the same energy level.The higher box dimensions indicate more dangerous dynamic events.

Improving Airgun Signal Detection with Small-Aperture Seismic Array in Yunnan

Repeating airgun sources are eco-friendly sources for monitoring the changes in the physical properties of subsurface mediums,but their signals decay quickly and are buried in the noises soon after traveling short distances.Stacking waveforms from different airgun shots recorded by a single seismic station(shot stacking)is the most popular technique to detect weak signals from noisy backgrounds,and has been widely used to process the data of Fixed Airgun Signal Transmission Stations(FASTS)in China.However,shot stacking sacrifices the time resolution in monitoring to recover a qualified airgun signal by stacking many shots at distance stations,and also suffers from persistent local noises.In this paper,we carried out several small-aperture seismic array experiments around the Binchuan FAST Station(BCFASTS)in Yunnan Province,China,and applied the array technique to improve airgun signal detection.The results show that seismic array processing combining with shot stacking can suppress seismic noises more efficiently,and provide better signal-to-noise ratio(SNR)and coherent airgun signals with less airgun shots.This work suggests that the array technique is a feasible and promising tool in FAST to increase the time resolution and reduce noise interference on routine monitoring.

Seismic electric signals in seismic prone areas

The Varotsos-Alexopoulos-Nomicos （VAN） method of short-term earthquake prediction was introduced in the 1980s. The VAN method enables estimation of the epicenter, magnitude and occurrence time of an impending earthquake by observing transient changes of the electric field of the Earth termed seismic electric signals （SES）. Here, we present a few examples of SES observed in various earthquake prone areas worldwide.

Retrieving Drill Bit Seismic Signals Using Surface Seismometers

Seismic while drilling （SWD） is an emerging horehole seismic imaging technique that uses the downhole drill-bit vibrations as seismic source. Without interrupting drilling, SWD technique can make near-real-time images of the rock formations ahead of the bit and optimize drilling operation, with reduction of costs and the risk of drilling. However, the signal to noise ratio （SNR） of surface SWD-data is severely low for the surface acquisition of SWD data. Here, we propose a new method to retrieve the drill-bit signal from the surface data recorded by an array of broadband seismometers. Taking advantages of wavefield analysis, different types of noises are identified and removed from the surface SWD-data, resulting in the significant improvement of SNR. We also optimally synthesize seis- mic response of the bit source, using a statistical cross-coherence analysis to further improve the SNR and retrieve both the drill-bit direct arrivals and reflections which are then used to establish a reverse vertical seismic profile （RVSP） data set for the continuous drilling depth. The subsurface images derived from these data compare well with the corresponding images of the three-dimension surface seismic survey cross the well.

Ability to detect weak effective seismic signals by utilizing chaotic vibrator system

Taking the advantage of CVS (Chaotic Vibrator System) sensitivity of large-scale periodic phase-state response to quasi-periodic or periodic signals,a series of numerical experiments were made to understand the ability of CVS to detect weak effective seismic signals in the common-shot seismic record distorted by strong stochastic noise. The re-sults demonstrate that the large-scale periodic phase-states of CVS are correlated with the signal composition of the quasi-periodic wavelet sequence constructing from horizontal moveout of seismic events,noise strength and the noise distortion de-gree to signal. For the same kind of events,the higher the noise distortion degree is,the lower the detectable SNR can be reached by CVS. For seismic data with the same noise distortion degree,the closer the scanning seismic velocity (the trial moveout ve-locity) approaches to the accurate velocity,the higher the detectable SNR can be reached by CVS. More-over,the truncating scanning velocities form an asymmetric belt,which indirectly makes CVS achieve a large-scale periodic phase-state and then the ratio of wavelet distortion coefficients in events can be a biggish variable scope.

Joint observation of long period tremor signals with broadband seismometer,tiltmeter and gravimeter

We report here the observation result of joint observation of long period tremor signals with broadband seismome-ter,tiltmeter and gravimeter at the HUST(Huazhong University of Science and Technology)station.The observed data were compared and analyzed.Since 2005,the several tens of abnormal tremor signals which are weak,com-plex and duration of 2 to 3 days have been synchronously recorded by the different instruments.The tremor signals have the periodic domain in the range of 3 to 5 minutes,20 to 30 minutes and even more than 1 hour.The observa-tion shows such tremors are a physical existence.The analysis indicates that a part of the tremors caused by the typhoon from the western Pacific Ocean.These tremors have a close relationship with wind velocity of typhoon and distance between the typhoon center and the station.Except these,the cause of others is still unclear.

Wavelet analysis and its application to signal processing

The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.

基于区域阈值模型的地震信号凸集投影高效重建方法

地震信号重建广泛应用的凸集投影(POCS)算法大都采用线性或指数阈值模型,虽然计算效率高,但由于难以完全消除缺失信号泄露引起的噪声,重建效果不佳。为此,提出了一种基于区域阈值模型的POCS地震信号重建方法,将数值阈值转化为区域阈值,将区域滤波窗口作为阈值进行迭代更新。其核心思想是根据时—空域缺失地震信号的频率—波数(F⁃K)谱分布范围,在每次POCS重建迭代时按照一定规律选取固定大小的矩形或扇形区域作为阈值,将区域内和区域外的变换系数分别保留和置零,以尽可能地保留有效信号的变换系数,构建了地震信号POCS重建的矩形与扇形区域阈值模型。数值试验结果表明:相比于F⁃K域指数阈值模型的POCS重建,F⁃K域区域阈值模型对连续缺失信号的重建精度更高;相比于扇形区域阈值模型,矩形区域阈值模型的重建精度和计算效率均略高;与曲波域指数阈值模型的POCS重建相比,F⁃K域区域阈值模型的重建精度相当,但计算效率提高了约90%。

联合VMD和Shearlet变换的去噪方法研究

地震数据中的噪声信号会很大程度上降低地震数据的信噪比,对后续的处理和解释工作造成一定的难度,因此研究一种去噪方法具有十分重要的意义。本文构建一种变分模态分解(Variational Mode Decomposition,VMD)和Shearlet变换联合去噪的方法,首先通过VMD将地震数据分解为一系列不同中心频率分布的模态分量(Intrinsic Mode Function,IMF),然后使用Shearlet变换对含噪的IMF分量进行去噪并对处理后的分量进行重构,最终达到去噪目的。该方法引入基于稀疏表示的地震数据去噪方法,兼顾VMD和Shearlet变换的优点,可以有效去除噪声。经过合成信号、模型及实际数据测试结果表明,本文方法处理合成信号结果显示,与VMD和小波变换方法相比,信噪比分别提高1.69、1.87,均方误差数值上减少近一半,在去除噪声的同时能更好地保留地震数据特征,提高地震资料的信噪比。

基于CEEMDAN-INHT的地下洞室爆破振动时频分析应用研究

爆破地震波信号采集会因监测环境、测试系统等因素导致实测信号中混有噪声,噪声的存在将导致信号希尔伯特-黄变换(Hilbert-Huang Transform,HHT)时频分析结果失真。原因有二:其一是经验模态分解(Ensemble Empirical Mode,EMD)处理含噪爆破地震波信号会得到具有模态混淆现象的固有模态函数(Intrinsic mode function,IMF)分量;其二是Hilbert变换受Bedrosian定理的约束在处理模态混淆分量时会产生负值瞬时频率,从而造成巨大的分析误差。为获得真实的爆破振动属性需对HHT进行改进,在EMD中添加自适应噪声信号得到自适应补充集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)算法;再对CEEMDAN得到的IMF进行归一化Hilbert变换,得到改进归一化Hilbert变换(Improved Normalized Hilbert Transform,INHT)。通过上述两步可建立CEEMDAN-INHT时频分析算法,为验证该算法可有效提高含噪爆破地震波振动信号时频分析精度,进行HHT和CEEMDAN-INHT含噪仿真振动信号时频分析对比研究。最后将CEEMDAN-INHT用于某地下洞室爆破地震波信号时频分析中,发现该算法能有效克服EMD固有的模态混淆现象,同时得到反映真实爆破振动属性的时-频-能特征参数,对从频率-能量的角度进行洞室爆破开挖共振分析,实现爆破地震波危害控制具有一定的现实意义。

基于改良U-Net卷积神经网络的复杂地质构造智能识别

浅地层中的地质异常体给地下工程带来了极大安全隐患,可能导致经济和生命损失.浅层地震法是开展施工场地勘察的一种无损高效的手段.但是地震勘探在浅地层问题中面临着信噪比低、信号衰减强、波场复杂等问题,结果存在多解性和主观性.针对地震勘探问题,提出了一种改良的卷积神经网络地质速度模型预测模型,提供了一种无需初始速度模型的浅层地质模型反演方案,形成了一套完整的浅层地震勘探信号处理处理流程.在训练样本方面,采用了随机地质模型方法构建多种类地质模型,并形成了地质模型-地震信号数据库.在传统U-Net卷积神经网络上进行了改良,以更好地适应浅地层弹性波叠前信号数据的反演任务.结果表明,神经网络的反演结果直观准确该模型能够精确地预测出地层分界线、褶皱、起伏、断层滑移线等的位置和大小等参数,所采用的SSIM和PSNR两个定量化评价指标均表示,所提出的改良神经网络可以实现高精度反演.预测结果与真实模型相比较,得到的SSIM平均值为0.91,PSNR平均值为39.0.同时该神经网络模型能够向三维问题扩展,能够极大地提高地震信号处理的效率和解译精度.

Study on automatic recognition of the first motion in a seismic event

In this paper, we have studied the waveforms of background noise in a seismograph and set up an AR model to characterize them. We then complete the modeling and the automatic recognition program. Finally, we provide the results from automatic recognition and the manual recognition of the first motion for 25 underground explosions.