共查询到20条相似文献，搜索用时 15 毫秒
1.
The maximum entropy spectral analysis (MESA) method is applied to synthetic and observed tremor time series using autoregressive processes and recordings from the volcanoes Etna (Sicily) and Merapi (central Java). The MESA analysis can be used to estimate power spectra with sharp peaks from short data records. If the tremor source process can be modelled by an autoregressive process, the MESA method is well-suited for determining the coefficients of the underlying difference equations. As in the standard periodogram method of power spectrum estimation, a mesagram estimate using record segmentation and MESA spectrum averaging reduces the variance of the spectral estimator. In combination with periodogram estimates, mesagram estimates confirm that the tremor source may be modelled as an ensemble of randomly excited resonators. Used together, these estimates provide a valuable method for short-term monitoring of volcanic activity. In addition, they can be applied to the determination of new source parameters such as resonator frequencies, damping coefficients, excitation probabilities, correlation of exciting forces, and resonator coupling and in the pattern recognition of source types. 相似文献
2.
Frequency-wavenumber (f-k) spectra of seismic strong-motion array data are useful in estimating back-azimuth and apparent propagation velocity of seismic waves arriving at the array. Such estimates are required to model wave passage effects while studying spatial variability of strong ground motion. Although periodogram-based spectral estimates are commonly used, practical applications based on them encounter limitations, such as, lack of objective criteria for selecting a proper smoothing window and its associated bandwidth, and relatively large variance of the estimated spectral quantities. We present an alternative spectral estimate based on parametric time series modelling approach. The well-known autoregressive (AR) time series model is used in a system-based approach to estimate the spectral matrix of auto- and cross-spectral densities. Such spectral estimates are found to be smoother than the windowed periodogram estimates, and can directly be used in f-k spectral analysis. We present an example application of the proposed technique using strong-motion data recorded by the SMART-1 array in Taiwan during the January 29 1981 $M_{L}$ 6.3 earthquake. Our results, in terms of back azimuth and apparent propagation velocity, are found to be in excellent agreement with those reported in the literature. 相似文献
3.
Spectral analysis for global navigation satellite system (GNSS) coordinate time series provides a principal tool to understand the intrinsic mechanism that affects tectonic movements. Spectral analysis methods such as the fast Fourier transform, Lomb–Scargle spectrum, evolutionary power spectrum, wavelet power spectrum, etc. are used to find periodic characteristics in time series. Among spectral analysis methods, the chirp Fourier transform (CFT) with less stringent requirements is tested with synthetic and actual GNSS coordinate time series, which proves the accuracy and efficiency of the method. With the length of series only limited to even numbers, CFT provides a convenient tool for windowed spectral analysis. The results of ideal synthetic data prove CFT accurate and efficient, while the results of actual data show that CFT is usable to derive periodic information from GNSS coordinate time series. 相似文献
4.
Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
5.
A smoothness priors-time varying autoregressive (AR) coefficient model method for the modelling of earthquake ground motion is shown. The method yields the instantaneous smoothed values of the AR coefficients and the instantaneous smoothed values of the innovations variance. These results in turn yield estimates of the instantaneous spectral density, the time varying covariance function and a simulation model for the ground motion data. An example of the application of the method to the analysis of an accelogram from the February 1971 San Fernando, California earthquake is shown. 相似文献
6.
A. Ian McLeod K. W. Hipel 《Stochastic Environmental Research and Risk Assessment (SERRA)》1995,9(3):171-205
Current methods of estimation of the univariate spectral density are reviewed and some improvements are made. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements, rather than competes with, the popular ARMA model building approach. A new diagnostic check for ARMA model adequacy based on the nonparametric spectral density is introduced. Additionally, two new algorithms for fast computation of the autoregressive spectral density function are presented. For improving interpretation of results, a new style of plotting the spectral density function is suggested. Exploratory spectral analyses of a number of hydrological time series are performed and some interesting periodicities are suggested for further investigation. The application of spectral analysis to determine the possible existence of long memory in natural time series is discussed with respect to long riverflow, treering and mud varve series. Moreover, a comparison of the estimated spectral densities suggests the ARMA models fitted previously to these datasets adequately describe the low frequency component. Finally, the software and data used in this paper are available by anonymous ftp from fisher.stats.uwo.ca. 相似文献
7.
Electromagnetic current meters (EMCMs) are frequently used to gather turbulent velocity records in rivers and estuaries. Experience has shown that, on occasion, the output of these sensors can be affected by contamination from various noise sources. These noises may be limited to narrow bands of frequencies and thus fail to produce conspicuous increases in observed signal variance. Such ‘narrow-band’ noises can be difficult to identify from simple inspection of signal traces or variance levels, yet degrade estimates of turbulence statistics, in particular covariances (used to calculate Reynolds shear stress). This paper demonstrates the usefulness of spectral analysis to detect and characterize narrow-band noise components in turbulent flow records. Statistical principles underlying the use of spectral analysis for noise detection are briefly reviewed. Examples of u and v velocity spectra and cospectra are then presented from actual EMCM velocity records from flume and field deployments that were found to be contaminated by such noises. The sensitivity of the shear stress estimates to even minor noise levels is demonstrated. The use of spectral analysis to correct variance (turbulence intensity) and covariance (shear stress) estimates obtained from records contaminated by narrow-band noise is also illustrated. 相似文献
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9.
Although for many years it was thought that amplitude scaling of acceleration time series to reach a target intensity did not introduce any bias in the results of nonlinear response history analyses, recent studies have showed that scaling can lead to an overestimation of deformation demands with increasing scale factors. Some studies have suggested that the bias can be explained by differences in spectral shape between the response spectra of unscaled and scaled records. On the basis of these studies, some record selection procedures assume that if records are selected using spectral-shape-matching procedures, amplitude scaling does not induce any bias on the structural response. This study evaluates if bias is introduced on lateral displacement demands and seismic collapse risk estimates even when spectral shape is carefully taken into consideration when selecting ground motions. Several single-degree-of-freedom and multiple-degree-of-freedom systems are analyzed when subjected to unscaled and scaled ground motions selected to approximately match the mean and the variance of the conditional spectrum at the target level of intensity. Results show that an explicit consideration of spectral shape is not enough to avoid a systematic overestimation of lateral displacement demands and collapse probabilities as the scale factor increases. Moreover, the bias is observed in practically all cases for systems with strength degradation and it increases with decreasing period and decreasing lateral strength relative to the strength required to remain elastic. Key reasons behind the bias are presented by evaluating input energy, causal parameters, and damaging pulse distributions in unscaled and scaled ground motion sets. 相似文献
10.
Spectral density estimates for distant whistlers in subranges of 8–12, 12–20, 20–30, and 30–40 kHz, based on data from stations located at distances of 250–300 km between each other, during the preparation of M = 5–6.2 earthquakes are obtained, and spectral density ratios are analyzed. It is shown that a stable decrease in spectral density ratios is observed for high frequency bands for a couple of stations which are the closest to and the farthest from the epicenter during a period of several days before an event (up to 2–3 weeks before it). Another peculiarity found is an increase in spectral density ratios several hours before an earthquake. These features can be used as short-term precursors. 相似文献
11.
John F. Hermance 《Physics of the Earth and Planetary Interiors》1973,7(3):349-364
The processing of magnetotelluric data involves concepts from electromagnetic theory, time series analysis and linear systems theory for reducing natural electric and magnetic field variations recorded at the earth's surface to forms suitable for studying the electrical properties of the earth's interior.The electromagnetic field relations lead to either a scalar transfer impedance which couples an electric component to an orthogonal magnetic component at the surface of a plane-layered earth, or a tensor transfer impedance which couples each electric component to both magnetic components in the vicinity of a lateral inhomogeneity.A number of time series spectral analysis methods can be used for estimating the complex spectral coefficients of the various field quantities. These in turn are used for estimating the nature of the transfer function or tensor impedance. For two dimensional situations, the tensor impedance can be rotated to determine the principal directions of the electrical structure.In general for real data, estimates of the apparent resistivity are more stable when calculated from the tensor elements rather than from simple orthogonal field ratios (Cagniard estimates), even when the fields are measured in the principal coordinates. 相似文献
12.
梁建文 《地震学报(英文版)》2005,18(2):226-238
Introduction Up to now,simulation of non-stationary ground motion processes in engineering usually is bygenerating stationary ground motion processes,then making them non-stationary by an enve-lope function(Amin and Ang,1968);and the envelope function is usually calculated based on sta-tistical analysis of ground motion records,and the stationary ground motion processes are simu-lated using filtering method,spectral representation method,or time domain method,etc.Because the envelope function … 相似文献
13.
Ana Teresa Mendoza-Rosas Servando De la Cruz-Reyna 《Journal of Volcanology and Geothermal Research》2008
The probabilistic analysis of volcanic eruption time series is an essential step for the assessment of volcanic hazard and risk. Such series describe complex processes involving different types of eruptions over different time scales. A statistical method linking geological and historical eruption time series is proposed for calculating the probabilities of future eruptions. The first step of the analysis is to characterize the eruptions by their magnitudes. As is the case in most natural phenomena, lower magnitude events are more frequent, and the behavior of the eruption series may be biased by such events. On the other hand, eruptive series are commonly studied using conventional statistics and treated as homogeneous Poisson processes. However, time-dependent series, or sequences including rare or extreme events, represented by very few data of large eruptions require special methods of analysis, such as the extreme-value theory applied to non-homogeneous Poisson processes. Here we propose a general methodology for analyzing such processes attempting to obtain better estimates of the volcanic hazard. This is done in three steps: Firstly, the historical eruptive series is complemented with the available geological eruption data. The linking of these series is done assuming an inverse relationship between the eruption magnitudes and the occurrence rate of each magnitude class. Secondly, we perform a Weibull analysis of the distribution of repose time between successive eruptions. Thirdly, the linked eruption series are analyzed as a non-homogeneous Poisson process with a generalized Pareto distribution as intensity function. As an application, the method is tested on the eruption series of five active polygenetic Mexican volcanoes: Colima, Citlaltépetl, Nevado de Toluca, Popocatépetl and El Chichón, to obtain hazard estimates. 相似文献
14.
Carlos Emilio Moyano
Ana Marí
a Osella 《Physics of the Earth and Planetary Interiors》1990,60(1-4):62-67The ability of the Maximum Entropy Spectral Estimate (MEM) to handle magnetotelluric signals was investigated. To this end simulations of naturally occurring signals and real data were spectral analyzed both by a standard FFT technique and by MEM. The method developed for the simulation of the time series, provides accurate amplitudes and phase, and turns out to be a good test procedure for spectral analysis methods, as they apply to MT signal processing. The final estimation of the apparent resistivity was better achieved by the FFT technique. Oscillations were observed in the estimation of the apparent resistivities by MEM. 相似文献
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16.
The impact on a large-scale sea level pressure field to the regional mean sea level changes of the German Bight is analysed. A multiple linear regression together with an empirical orthogonal function analysis is used to describe the relationship between the sea level pressure and the regional mean sea level considering the time period 1924–2001. Both, the part of the variability and of the long-term trend that can be associated with changes in the sea level pressure, are investigated. Considering the whole time period, this regression explains 58?% of the variance and 33?% of the long-term trend of the regional mean sea level. The index of agreement between the regression result and the observed time series is 0.82. As a proxy for large-scale mean sea level changes, the mean sea level of the North East Atlantic is subsequently introduced as an additional predictor. This further improves the results. For that case, the regression explains 74?% of the variance and 87?% of the linear trend. The index of agreement rises to 0.92. These results suggest that the sea level pressure mainly accounts for the inter-annual variability and parts of the long-term trend of regional mean sea level in the German Bight while large-scale sea level changes in the North East Atlantic account for another considerable fraction of the observed long-term trend. Sea level pressure effects and the mean sea level of the North East Atlantic provide thus significant contributions to regional sea level rise and variability. When future developments are considered, scenarios for their future long-term trends thus need to be comprised in order to provide reliable estimates of potential future long-term changes of mean sea level in the German Bight. 相似文献
17.
V. L. Barsukov V. S. Serebrennikov A. A. Belyaev Yu. A. Bakaldin R. V. Arsenyeva 《Pure and Applied Geophysics》1984,122(2-4):157-163
The discrimination of significant earthquake precursors from background noise is treated as a multistep problem of pattern recognition. Statistical characteristics of helium-content recorded in short time intervals are used as informative parameters. The set of calculated characteristics includes estimations of the mean, the variance, and the results of spectral analysis of the investigated time series. The selection of significant parameters and the rigorous estimations of time shifts between geochemical and seismic series are carried out by analyzing their cross-covariance function. It is established that the most informative characteristics of a hydrothermal system are related to the dynamic fluctuations of the geochemical parameters. The final phase of prediction is based on the application of a method of statistical discovery of images. A method of earthquake-time prediction is suggested. By using this method, we may determine the 10-day interval during which an earthquake may occur two months in advance. The prediction may be improved by increasing the frequency of sampling and by improving the precision of analytical measurements, both of which can be achieved by automation of monitoring devices. Deployment of uniform monitoring networks is needed in regions designated for special prediction tasks. 相似文献
18.
In this study, we investigated the temporal variability of dissolved oxygen and water temperature in conjunction with water level fluctuations and river discharge in the Narew lowland river reach. For this purpose, high resolution hydrologic and water quality time series have been used. Spectral analyses of time series using continuous wavelet transform scheme have been applied in order to identify characteristic scales, its duration, and localisation in time. The results of wavelet analysis have shown a great number of periodicities in time series at the inter-annual time scale when compared to the classical Fourier analysis. Additionally, wavelet coherence revealed the complex nature of the relationship between dissolved oxygen and hydrological variables dependent on the scale and localisation in time. Hence, the results presented in this paper may provide an alternative representation to a frequency analysis of time series. 相似文献
19.
基于光子计数探测器的能谱CT,可以同时采集多个能谱通道的投影数据,并获得相应能量范围内物质的吸收特征,可以有效应用于物质识别与材料分解。主成分分析是一种很好的多元数据分析技术,可以用于处理多能谱CT数据。本文分别在投影域和图像域对能谱CT数据进行主成分分析,并对分析结果做出系统比较。为了减少噪声的影响,提高能谱CT图像的彩色表征性能,提出双域滤波与像素值平方相结合的方法,用于含噪声的主成分图像去噪,然后将所选取的主成分图像映射到RGB颜色通道。实验结果表明,无论是在投影域还是图像域进行主成分分析,都可以获取清晰的CT图像,识别出物质的不同成分。相较于在图像域的主成分分析方法,在投影域进行主成分分析能够保留物质的更多细节,获取更清晰的彩色CT图像。 相似文献
20.
D. Labat C. T. Hoang J. Masbou A. Mangin I. Tchiguirinskaia S. Lovejoy D. Schertzer 《水文研究》2013,27(25):3708-3717
Karstic watersheds are highly complex hydrogeological systems that are characterized by a multiscale behaviour corresponding to the different pathways of water in these systems. The main issue of karstic spring discharge fluctuations consists in the presence and the identification of characteristic time scales in the discharge time series. To identify and characterize these dynamics, we acquired, for many years at the outlet of two karstic watersheds in South of France, discharge data at 3‐mn, 30‐mn and daily sampling rate. These hydrological records constitute to our knowledge the longest uninterrupted discharge time series available at these sampling rates. The analysis of the hydrological records at different levels of detail leads to a natural scale analysis of these time series in a multifractal framework. From a universal class of multifractal models based on cascade multiplicative processes, the time series first highlights two cut‐off scales around 1 and 16 h that correspond to distinct responses of the aquifer drainage system. Then we provide estimates of the multifractal parameters α and C1 and the moment of divergence qD corresponding to the behaviour of karstic systems. These results constitute the first estimates of the multifractal characteristics of karstic spingflows based on 10 years of high‐resolution discharge time series and should lead to several improvements in rainfall‐karstic springflow simulation models. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献