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1.
Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela.  相似文献   

2.
In order to analyze observed seismicity in central Japan and Venezuela, we applied a new method to identify semi-periodic sequences in the occurrence times of large earthquakes, which allows for the presence of multiple periodic sequences and/or events not belonging to any sequence in the time series. We also explored a scheme for diminishing the effects of a sharp cutoff magnitude threshold in selecting the events to analyze. A main four-event sequence with probability P c  = 0.991 of not having occurred by chance was identified for earthquakes with M ≥ 8.0 in central Japan. Venezuela is divided, from West to East, into four regions; for each of these, the magnitude ranges and identified sequences are as follows. Region 1: M ≥ 6.0, a six-event sequence with P c  = 0.923, and a four-event sequence with P c  = 0.706. Region 2: M ≥ 5.6, a five-event sequence with P c  = 0.942. Region 3: M ≥ 5.6, a four-event sequence with P c  = 0.882. Region 4: M ≥ 6.0, a five-event sequence with P c  = 0.891. Forecasts are made and evaluated for all identified sequences having four or more events and probabilities ≥0.5. The last event of all these sequences was satisfactorily aftcast by previous events. Whether the identified sequences do, in fact, correspond to physical processes resulting in semi-periodic seismicity is, of course, an open question; but the forecasts, properly used, may be useful as a factor in seismic hazard estimation.  相似文献   

3.
Miao Li  Zhi Chen  Dejuan Meng  Chongyu Xu 《水文研究》2013,27(20):2934-2943
Non‐parametric methods including Mann–Kendall (M–K) test, continuous wavelet transform (CWT) and discrete wavelet transform analysis are applied in this paper to detect the trend and periodic trait of precipitation data series in Beijing area where the data set spans nearly 300 years from 1724 to 2009. First, the trend of precipitation variables is elaborated by the M–K test (Sequential M–K test). The results show that there is an increasing trend (the value of this trend is 1.98) at the 5%‐significance level and there are not turning points in the whole data series. Then, CWT and wavelet variance are used to check for significant periodic characteristics of data series. In the plots of wavelet transform coefficients and figure of wavelet variance, some periodic events affect the trend of the annual total precipitation series in Beijing area. 85‐year, 35‐year and 21‐year periodic events are found to be the main periodic series of long‐term precipitation data, and they are all statistically significant. Moreover, the results of non‐parametric M–K test are exhibited on seven different combinations of discrete wavelet components. D5 (32‐year periodicity) periodic component is the effective and significant component on data. It is coincident with the result (35‐year periodic event as one part of main periodicity) by using CWT analysis. Moreover, approximation mode shows potential trend of the whole data set because it is the residuals as all periodicities are removed from data series. Thus, the mode A + D5 is responsible for producing a real basic structure of the trend founded on the data. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

4.
多次波偏移中的假象主要来自于不同地震事件之间的互相关,由于这种互相关满足成像条件,很难直接在偏移过程中去除.但是对于准确的速度模型,真实的成像结果在角度域内应该是平直的.根据这个判断准则,可以在角度域内移除多次波偏移中的假象.本文以数据自相关偏移为例,提出了在单程波多次波偏移中移除假象的主要流程:首先在在单程波偏移过程中高效地提取角度域共成像点道集,然后对角度域共成像点道集应用高分辨率的抛物线型Radon变换,用合适的切除函数处理后,反变换回到角度域,最后叠加各个角度成分,得到偏移结果.Marmousi模型的合成数据测试表明,这种方法可以很好地压制多次波偏移过程中产生的假象,有效地提高成像结果的信噪比.  相似文献   

5.
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   

6.
Time–frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert–Huang transform to investigate time–frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at ~0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (~2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of ~4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes with similar evolution of trend components.  相似文献   

7.
Aftershocks induced by a large mainshock can cause additional damage to structures and infrastructure, hampering building reoccupation and restoration activities in a post‐disaster situation. To assess the nonlinear damage potential due to aftershocks, this study investigates the effects of aftershocks by using real as well as artificially generated mainshock–aftershock sequences. The real mainshock–aftershock sequences are constructed from the Pacific Earthquake Engineering Research Center—Next Generation Attenuation database for worldwide shallow crustal earthquakes; however, they are deemed to be incomplete because of missing records. To supplement incomplete real dataset, artificial sequences are generated on the basis of the generalized Omori's law, and a suitable aftershock record selection procedure is then devised to simulate time‐series data for mainshock–aftershock sequences. The results from nonlinear dynamic analysis of inelastic single‐degree‐of‐freedom systems using real and artificial sequences indicate that the incremental effects of aftershocks on peak ductility demand using the real sequences are relatively minor and that peak ductility demand estimates based on the generalized Omori's law are greater, particularly in the upper tail, than those for the real sequences. The results based on the generalized Omori's law also suggest that the aftershock effects based on the real sequences might underestimate the aftershock impact because of the incompleteness of the real dataset. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

8.
This paper deals with Rayleigh–Schuster’s hodographs, intended for the detailed investigation of changes in the phase of quasiperiodic signals in time series. The hodographs are also known as the phasor-walkout method. A procedure of conditional vector normalization is proposed: it takes into account the vector amplitude for each period under consideration. The procedure considerably improves the robustness and stability of the hodograph approach to changes in the character of the processed data distributions and to various defects in the data. For example, when analyzing the earthquake catalog, the procedure strongly diminishes the influence of the event clustering caused, in particular, by swarms of earthquakes with comparable magnitude and the aftershock sequences of strong earthquakes. At the first stage, we calculate the vector sums (resulting vectors) for each period under investigation throughout the time series duration. For example, investigating diurnal periodicity of earthquakes, we first calculate the resulting vectors for each day of the observation. The further analysis of resulting vectors for each period throughout the time series duration can be performed with different procedures. We compare three procedures for normalization of the obtained resulting vectors, which are as follows: (1) th traditional one, preserving the real signal amplitude; (2) that with reduction of the obtained resulting vectors to the unit vector (phasor); and (3) that with conditional vector normalization, taking into account the amplitude of resulting vectors for each period throughout the time series duration. The third procedure diminishes the possible instability in some special distributions of the investigated data when the resulting vector for a period is close to zero. The procedures are compared using model signals and samples from real earthquake catalogs. All the procedures used give close results when processing random time series.  相似文献   

9.
The purpose of this study is to determine the possible trends in annual total precipitation series by using the non-parametric methods such as the wavelet analysis and Mann-Kendall test. The wavelet trend (W-T) analysis is for the first time presented in this study. Using discrete wavelet components of measurement series, we aimed to find which periodicities are mainly responsible for trend of the measurement series. We found that some periodic events clearly affect the trend of precipitation series. 16-yearly periodic component is the effective component on Bal?kesir annual precipitation data and is responsible for producing a real trend founded on the data. Also, global wavelet spectra and continuous wavelet transform were used for analysis to precipitation time series in order to clarify time-scale characteristics of the measured series. The effects of regional differences on W-T analysis are checked by using records of measurement stations located in different climatic areas. The data set spans from 1929 to 1993 and includes precipitation records from meteorological stations of Turkey. The trend analysis on DW components of the precipitation time series (W-T model) clearly explains the trend structure of data.  相似文献   

10.
A preliminary statistical analysis of the space-time distribution of small seismic events in the volcanic area of Phlegraean Fields, south-central Italy, was done on the basis of a catalogue of earthquakes recorded by the local seismic stations in the period January 1, December 31, 1983.The non-random character of the sequence has been tested by matching the observed time-dstribution of seismic events with the theoretical Poisson process.A clustered occurrence of seismic events seems to be the main cause of the departure from a Poisson process as the inter-arrival times distribution clearly shows.Furthermore, the non-random behaviour of the events space-time distribution mainly due to quiescient and clustered recursive seismic phases could be studied by using the method proposed byVon Seggern et al. (1981). The analysis and the space-time diagrams confirm the swarm-type character of the entire seismic sequence.  相似文献   

11.
The development of high resolution LiDAR digital terrain models (DTMs) has enabled the exploration of the statistical signature of morphology on curvature distributions. This work analyzes Minimum Curvature distributions to identify the statistical signature of two types of LiDAR‐DTM errors (outliers and striping artifacts) in the derived estimates, rather than morphology itself. The analysis shows the importance of modeling these errors correctly, in relation to the scale of analysis and DTM resolution, in order to have reliable curvature estimates. Nine DTMs of different morphological areas are considered, and grouped into a training dataset (without errors) and a test dataset (with errors). In the training dataset, the original DTMs are considered as true values; errors are then applied to these data. Minimum Curvature is computed at multiple scales from each DTM: changes in curvature distributions due only to morphology and scale are characterized from the original data; error effects are then identified from the datasets with simulated errors, and validated against the test dataset. The analysis shows that outliers and striping artifacts can be realistically simulated by heavily left tailed distributions. For DTMs without errors, the scale‐dependent change in curvature distribution is primarily controlled by real morphology. When DTMs include errors, curvature distributions become controlled by these errors, whose propagation depends on error distribution, error spatial correlation, and the scale of analysis. This study shows that the curvature distributions are impacted upon differently by striping artifacts and outliers, and that these are clearly distinguishable from the signal of morphological features: a scale‐dependent change in curvature distribution can therefore be interpreted as the signature of these specific errors, rather than morphology. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

12.
Analysis of earthquake catalogues on 14 world regions has revealed a distinct diurnal periodicity of seismic events in all of them. The amplitude of the diurnal variations usually decreases with an increase in earthquake energy, although in some regions, the time series of strong earthquakes also demonstrate diurnal periodicity. Earthquakes are more frequent at night. The acrophase of the course of diurnal seismicity correlates with geographic longitude. The Rayleigh — Schuster hodographs of diurnal periodicity demonstrate sharp changes (kinks) in the vicinity of the equinox and solstice moments. The annual hodograph of the diurnal periodicity of earthquakes is distinctly divided by the equinox moments into segments with different slopes. The defined segments differ in the amplitude and acrophase of the course of diurnal seismicity. The data imply influence of the mutual positions of the Sun and Earth on seismicity in different world regions. Possible mechanisms responsible for such influence are discussed.  相似文献   

13.
We have analyzed the three most commonly used geomagnetic polarity time scales that cover the past 100 m.y.. In the three cases, different spectral analyses show a 13–16 m.y. periodicity in the rate of reversal occurrence. To test whether this periodicity is real or simply arises from a random generator we have compared these polarity time scales with a large number of synthetic sequences produced by a random process, characterized by a linear time variation of its mean activity. Geomagnetic and generated sequences were regularly sampled by using sliding windows, anf then the Fourier spectra of the obtained frequency signals were compared. This test shows that the detected periodicity is presumably not a simple statistical fluctuation of an aperiodic generator, and consequently that a long-term periodicity in the geodynamo must be seriously considered.  相似文献   

14.
Missing early aftershocks following relatively large or moderate earthquakes can cause significant bias in the analysis of seismic catalogs. In this paper, we systematically address the aftershock missing problem for five earthquake sequences associated with moderate-size events that occurred inland Japan, by using a stochastic replenishing method. The method is based on the notion that if a point process (e.g., earthquake sequence) with time-independent marks (e.g., magnitudes) is completely observed, it can be transformed into a homogeneous Poisson process by a bi-scale empirical transformation. We use the Japan Meteorological Agency (JMA) earthquake catalog to select the aftershock data and replenish the missing early events using the later complete part of each aftershock sequence. The time windows for each sequence span from 6 months before the mainshock to three months after. The semi-automatic spatial selection uses a clustering method for the epicentral selection of earthquakes. The results obtained for the original JMA catalog and replenished datasets are compared to get insight into the biases that the missing early aftershocks may cause on the Omori-Utsu law parameters’ estimation, characterizing the aftershock decay with time from the mainshock. We have also compared the Omori-Utsu law parameter estimates for two datasets following the same mainshock; the first dataset is the replenished sequence, while the second dataset has been obtained by waveform-based analysis to detect early aftershocks that are not recorded in the JMA catalog. Our results demonstrate that the Omori-Utsu law parameters estimated for the replenished datasets are robust with respect to the threshold magnitude used for the analyzed datasets. Even when using aftershock time windows as short as three days, the replenished datasets provide stable Omori-Utsu law parameter estimations. The p-values for all the analyzed sequences are about 1.1 and c-values are significantly smaller compared to those of original datasets. Our findings prove that the replenishment method is a fast, reliable approach to address the missing aftershock problem.  相似文献   

15.
对于常规的逆时定位成像方法,成像结果中强震源的成像值通常远大于并且会掩盖弱震源;同时,成像结果中假象的压制与消除也一直是该技术中颇受关注且比较难解决的问题.对此,本文结合了混合成像条件与高通滤波,从图像对比度的角度加强定位成像效果.提出了反传检波点随机选择的方法,通过重复进行随机选择与随机分组,从而得到不同震源的、包括一些冗余在内的更多信息,通过对信息的融合以提高定位可靠性.提出了筛选模型的概念,将成像过程中各点的波场反传序列引入震源判断标准,构建函数以大致量化震源存在的可能性,结合阈值,构造出由0和1组成"筛选模型",对成像结果进行通过性选择,以此消除假象并提高震源识别的正确性.通过简单模型和复杂模型,验证了本文提出方法的有效性以及对各类干扰因素的适应性与抵抗性.  相似文献   

16.
The M?≥?7 earthquakes that occurred in the Taiwan region during 1906–2006 are taken to study the possibility of memory effect existing in the sequence of those large earthquakes. Those events are all mainshocks. The fluctuation analysis technique is applied to analyze two sequences in terms of earthquake magnitude and inter-event time represented in the natural time domain. For both magnitude and inter-event time, the calculations are made for three data sets, i.e., the original order data, the reverse-order data, and that of the mean values. Calculated results show that the exponents of scaling law of fluctuation versus window length are less than 0.5 for the sequences of both magnitude and inter-event time data. In addition, the phase portraits of two sequent magnitudes and two sequent inter-event times are also applied to explore if large (or small) earthquakes are followed by large (or small) events. Results lead to a negative answer. Together with all types of information in study, we make a conclusion that the earthquake sequence in study is short-term corrected and thus the short-term memory effect would be operative.  相似文献   

17.
A 15-min periodicity of seismic events in the catalog of earthquakes in Greece is discovered. It is most vividly expressed in the time series of weak and shallow events; however, it also occurs in the series of rather strong representative and deep earthquakes.  相似文献   

18.
A data analysis method is proposed to cluster and explore spatio-temporal characteristics of the 22 years of precipitation data (1982–2003) for Taiwan. The wavelet transform self-organizing map (WTSOM) framework combines the wavelet transform (WT) and a self-organizing map (SOM) neural network. WT is used to extract dynamic and multiscale features of the non-stationary precipitation time-series, and SOM is applied to objectively identify spatially homogeneous clusters on the high-dimensional wavelet-transformed feature space. Haar and Morlet wavelets are applied in the data preprocessing stage to preserve the desired characteristics of the precipitation data. A two-level SOM neural network is applied to identify clusters in the wavelet space in the clustering stage. The performance of clustering is evaluated using silhouette coefficients. The results indicate that singularities or sharp transitions are more significant than changes in the periodicity or data structure in the spatial–temporal precipitation data. The WTSOM results show that six clusters are optimal for both Haar and Morlet wavelet functions, but their corresponding geographic locations are different. The geographic locations of clusters based on the Haar wavelet, which captures the occurrence of extreme hydrological events, appear in blocks while those classified by the Morlet wavelet, which indicates periodicity changes and describes fine structures, appear in strips that cross the island of Taiwan. Principal component analysis is applied to the precipitation data of each cluster. The first principal components explain 62–90% of the total variation of data. Characteristics of precipitation data for each cluster are explored using scalogram analysis. The results show that both extreme hydrological events and periodicity changes appear in the spatial and temporal precipitation data but with different characteristics for each cluster. Recognizing homogeneous hydrologic regions and identifying the associated precipitation characteristics improves the efficiency of water resources management in adapting to climate change, preventing the degradation of the water environment, and reducing the impact of climate-induced disasters. Measures for countering the stress of precipitation variation for water resources management are provided.  相似文献   

19.
海水与空气间的强波阻抗差使得海洋地震资料普遍发育自由表面相关多次波,如何利用好多次波所携带的有效信息已成为提高海洋地震资料成像品质的新突破点.基于面炮偏移的一次波与多次波同时成像方法能够避免多次波预测精度的影响,但是,正向传播的震源子波与反向延拓的自由表面相关多次波所产生的干涉假象严重制约了该技术的应用,本文提出了一种基于单程波偏移算子,可在成像域压制干涉假象的一次波与多次波同时成像方法.其中包含了三个步骤:第一,传统单程波偏移成像方法中的震源子波替换为一次波、多次波与震源子波,初始上行延拓波场为一次波与多次波,基于单程波算子的波场延拓与互相关成像条件的应用得到包含干涉假象的一次波与多次波同时成像;第二,以子波为震源,自由表面相关多次波为记录,按照传统单程波偏移成像方法得到干涉假象;第三,基于最小二乘匹配滤波算法,将第一步的成像结果与第二步的干涉假象进行匹配相减,得到干涉假象衰减后的一次波与多次波同时成像,避开了由于实际资料子波无法准确提取而造成一次波与多次波对成像能量级的不一致性.Sigsbee2B模型测试验证了本方法的有效性,并在我国某探区深海实际资料处理中得到了成功应用,深层基底得到了清晰刻画,并且照明均衡度明显改善.  相似文献   

20.
Seismic and infrasonic observations of signals from a sequence of near-surface explosions at a site on the Kola Peninsula have been analyzed. NORSAR’s automatic network processing of these events shows a significant scatter in the location estimates and, to improve the automatic classification of the events, we have performed full waveform cross-correlation on the data set. Although the signals from the different events share many characteristics, the waveforms do not exhibit a ripple-for-ripple correspondence and cross-correlation does not result in the classic delta-function indicative of repeating signals. Using recordings from the ARCES seismic array (250 km W of the events), we find that a correlation detector on a single channel or three-component station would not be able to detect subsequent events from this source without an unacceptable false alarm rate. However, performing the correlation on each channel of the full ARCES array, and stacking the resulting traces, generates a correlation detection statistic with a suppressed background level which is exceeded by many times its standard deviation on only very few occasions. Performing f-k analysis on the individual correlation coefficient traces, and rejecting detections indicating a non-zero slowness vector, results in a detection list with essentially no false alarms. Applying the algorithm to 8 years of continuous ARCES data identified over 350 events which we confidently assign to this sequence. The large event population provides additional confidence in relative travel-time estimates and this, together with the occurrence of many events between 2002 and 2004 when a temporary network was deployed in the region, reduces the variability in location estimates. The best seismic location estimate, incorporating phase information for many hundreds of events, is consistent with backazimuth measurements for infrasound arrivals at several stations at regional distances. At Lycksele, 800 km SW of the events, as well as at ARCES, infrasound is detected for most of the events in the summer and for few in the winter. At Apatity, some 230 km S of the estimated source location, infrasound is detected for most events. As a first step to providing a Ground Truth database for this useful source of infrasound, we provide the times of explosions for over 50 events spanning 1 year.  相似文献   

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