Geodetic evidence on segmentation of Cascadia subduction zone based on episodic tremors and slips using multivariate harmonic analysis of GPS time series

Episodic tremor and slip (ETS) events with different recurrence intervals have been observed in abundance all along the Cascadia subduction zone margin. Analysis of seismic records as well as Global Positioning System (GPS) time series of the Pacific Northwest Geodetic Array (PANGA) has suggested three distinct coherent zones for the occurrence of these events. In this paper multivariate harmonic estimation has been deployed for further analysis of the segmentation in this area. Raw time series of 43 permanent GPS stations have been used for this purpose. The GPS stations have been geographically divided into three distinct groups including those in the northern, middle and southern parts of the study area. After the reduction of time series for the linear trend as well as annual and semiannual effects, the data series of each group has been analyzed using the multivariate harmonic estimation technique. Subsequently, different combinations of GPS stations including the stations located in the southern, northern and middle zones have been analyzed. Furthermore, the northern and middle, southern and middle as well as the northern and southern zone pair combinations have also been analyzed. The statistical measure devised for identifying the significant frequencies suggests common periods that are consistent with the recurrence intervals of the ETS events already reported for each of the above three geographic zones. Moreover, the method can provide geodetic evidence, in addition to geophysical ones, on the segmentation of ETSs, provided that the adopted time series are of a sufficient length. The geodetic evidence obtained in this research is consistent with the recurrence intervals as well as the boundaries obtained by the analysis of seismic records. Contrary to univariate harmonic estimation, multivariate approach using spatio-temporal correlation of the GPS time series is capable to detect those ETSs whose impacts on the time series are weak.     

Technologies for analyzing geophysical time Series: Part 1. Software requirements

Time series of geophysical monitoring data are very specific. They can contain complicated irregular variations and nonstationary effects of various kinds. Time-varying noise, numerous gaps in the data, trends, technical drawbacks, and other hardly predictable features are often present in these time series. These peculiarities demand special methods and procedures for acquisition and analysis of the data. The first part of the paper discusses the peculiarities of the software for such time series, and a brief review of the methods for solving the task is given. In the second part of the paper, the universal software package WinABD developed by the authors for multivariable analysis of geophysical time series will be described. The technologies realized in the software package and intended for analysis of similar signals will be considered.     

多维分形奇异功率谱分析与应用

地球物理信号中普遍含有噪声，消除噪声是地球物理信号处理中的关键技术之一.奇异功率谱分析（SSA）是在状态空间（又称相空间）中研究（系统）动力学、非线性科学与混沌现象的方法.本文在状态空间中通过SSA分解，研究、应用地球物理序列的尺度不变性进行多维分形滤波：通过在状态空间的SSA分解，构造了经验正交函数系（EOF）；在EOF子空间中定义了两种尺度与测度后，发现了两种测度与尺度皆在多个尺度范围内存在尺度不变性；利用这种尺度~测度的尺度不变性，设计、实现了多维分形奇异功率谱(MSSA)滤波模型；处理解释了大洋钻探（ODP）1143A孔岩芯自然反射性（NGR）资料；Fourier功率谱分析结果证明，MSSA能有效地压制噪声，提取有用信号.研究得出，嵌入维数对MSSA基本无影响（小于1/1000），多维分形滤波器（MSSA）能有效压制噪声或提取有用信号.     

地震前兆观测资料的分形处理方法

本文从自仿射分形的角度探讨地震兆汾观测资料的分形处理方法。在对自仿射分形的分维量测方法进行对比分析的基础上，给出了处理地震前兆观测资料较为适用的分形方法和满足计算需要的样本点数，从对部分水氡和地电阻率资料进行了处理分析中，得到了有益的启示：地震前兆时序观测资料可用分形方法处理分析，分维值可反映观测资料的结构变化特征，其作为一个无量纲量为综合对比分析各类前兆现象，探讨地震前兆场的复杂性特征提供了有效     

Quantitative geophysical investigations of granular aquifers

Groundwater flow and storage in granular aquifers are much more amenable to analysis than in fissured reservoirs. The hydrological parameters used to describe the behaviour of a granular aquifer have been seen to be related to geophysical parameters in diverse water-bearing formations. This means that certain geophysical survey techniques are potentially useful as quantitative hydrogeological tools. Yet the study of these basic relationships and their subsequent field application have been disjointed and lacking in overal co-ordination. Consequently these quantitative hydrogeophysical procedures have not been applied to maximum advantage. The role of geophysics in quantitative studies of granular aquifers is reviewed in terms of the prediction of effective porosity and intergranular permeability from geophysical measurement. The potential usefulness of these methods manifests itself through the degree of correlation between hydrological and geophysical parameters observed from laboratory studies, borehole logging investigations or field survey data. Their application is exemplified through an ordered series of hydrogeophysical case histories. It is contended that the potential of geophysical methods in quantitative studies of this kind has by no means been fully exploited.     

Earthquake precursory research in western Himalaya based on the multi-parametric geophysical observatory data

The opening of cracks and influx of fluids in the dilatancy zone of impending earthquake is expected to induce short-term changes in physical/chemical/hydrological properties during earthquake build-up cycle, which should be reflected in time-varying geophysical fields. With this rationale, eleven geophysical parameters are being recorded in continuous mode at the Multi-Parametric Geophysical Observatory (MPGO), in Ghuttu, Garhwal Himalaya, for earthquake precursory research. The critical analysis of various geophysical time series indicates anomalous behavior at few occasions; however, the data is also influenced by many external forces. These external influences are the major deterrent for the isolation of precursory signals. The recent work is focused on the data adoptive techniques to estimate and eliminate effects of solar-terrestrial and hydrological/environmental factors for delimiting the data to identify short-term precursors. Although any significant earthquake is not reported close to the observatory, some weak precursory signals and coseismic changes have been identified in few parameters related to the occurrence of moderate and strong earthquakes.     

On the use of maximum entropy/autoregressive spectrum in harmonic analysis of time series

The maximum entropy (ME) spectrum, or its equivalent form of the autoregressive (AR) spectrum, has been used as a tool for harmonic analysis of time series in geophysics. This paper critically examines its usage in estimating the amplitude and the exponential decay rate of a harmonic function. The argument is based upon Prony's relation, which relates a complex-conjugate pair of poles for the AR model of the time series on one hand, to the complex frequency of one harmonic component in the time series on the other. It is found that: (i) the ME spectrum can be used as an estimator for the decay rate in a way similar to the Fourier spectral analysis; (ii) the ME spectrum contains no information whatsoever about the amplitude, contrary to what has been claimed and practiced in geophysical applications.     

强震地面运动的混沌特性分析

引入非线性动力学理论和混沌时间序列分析方法考察强震地面运动加速度时程的非线性特征。首先采用功率谱分析法、主成份分析法和Cao方法定性判断地震动加速度时程具有混沌特性,然后应用混沌时间序列分析方法定量计算了30条地震动加速度时程的三个非线性特征参数。计算表明,这些地震动时程的关联维数为2．0～4．0的分数维,Kolmogorov熵K2为大于零的有限正值,最大Lyapunov指数在o～i．0之间。结果说明,强震地面运动具有混沌特性,地震动的高度不规则和复杂性是地震过程强非线性的反映。     

Statistically correct determination of long-period trends in geophysical data series

Linear trend analysis of geophysical time series is considered in connection with the problem of determining long-period variations (possibly of man-made origin) in the presence of short period noise of larger amplitude. Criteria for testing hypotheses about linear trends are presented for the cases of independent observations and of the observations whose correlation function is known. The possibility of increasing the precision of the results using area-averaged values is investigated. Examples are given of the long time series analysis of air temperature, carbon dioxide and water vapor content. Time series of total atmospheric ozone content at some stations are also considered, and conditions for correct statistical analysis of such data are given.     

Visibility variations at Schiphol-Airport,Amsterdam

Summary Transmissometer records at Schiphol-Airport, Amsterdam have been analyzed to show the frequencies of visibility changes, in periods between 1 and 16 minutes, on occasions that the visibility is below 1000 metres for at least 30 minutes. Tables are presented for the frequencies of visibility changes in shallow and in deep fog, both for increasing and decreasing visibilities, with an initial visibility in the range of 60–1000 metres (the lower limit of this range is connected with the length of the transmissometer-baseline in use).In deep fog the probability of a visibility change surpassing 25 percent is about 1 percent after 1 minute and 20 percent after 16 minutes if the initial visibility is between 100 and 200 metres. The corresponding figures in shallow fog are 7 and 55 percent. If the initial visibility is between 200 and 400 metres, the corresponding figures in deep fog are 5 and 45 percent and in shallow fog 26 and 84 percent.     

Hydrological trend analysis with innovative and over-whitening procedures

Different statistical methodologies can be employed to identify possible trend components in any hydro-meteorological time series. A pre-whitening (P-W) procedure has been suggested to reduce the serial correlation effect on Mann-Kendall (M-K) trend analysis. In this paper, instead of P-W, an over-whitening (O-W) procedure is suggested, which generates serially independent series with the same trend slope value. Analytically necessary formulations for O-W are presented with a non-parametric but simple innovative trend assessment procedure, which are supported by extensive simulation studies. The applications of the methodology are presented for eight factual time series records from tropical, temperate and arid regions including temperature, rainfall, streamflow, relative humidity and CO₂ concentrations for different short and long durations. Relative humidity and CO₂ records are monthly time series and, hence, there are trend and periodicity components. It is noticed in all cases that the natural trends remain as they were after the O-W procedure, thus providing an opportunity to determine reliably the trends embedded even in the serially dependent series. The O-W procedure is applicable even in the cases of periodicity in the original records.     

Determination of the phase projection of a dynamic system from strain field observations

The paper analyzes the possibility of extracting a geophysical field component from a time series considered as a phase projection of a dynamic system. Based on model calculations, the method of averaging of phase trajectories is shown to be applicable for the recovery of time variations of a dynamic system and more reliable determination of its quantitative characteristics if the variations are superimposed by a high-dimension process (a purely random process) and a low-dimension process (linear interpolation). The analysis of the extraction of dynamic system variations from strain records obtained at various points of seismically active regions of China provided the following estimates of dynamic parameters: the phase space dimension is m = 5–7, and the correlation dimension varies, depending on the observation point, within the range d ₂ = 2.6–3.5.     

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex dataset in (a), and finally, (c) the dominant independent complex modes are extracted and used to represent the dominant space and time amplitudes and associated phase propagation patterns. The performance of CICA is examined by analyzing synthetic data constructed from multiple physically meaningful modes in a simulation framework, with known truth. Next, global terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) gravimetry mission (2003–2016), and satellite radiometric sea surface temperature (SST) data (1982–2016) over the Atlantic and Pacific Oceans are used with the aim of demonstrating signal separations of the North Atlantic Oscillation (NAO) from the Atlantic Multi-decadal Oscillation (AMO), and the El Niño Southern Oscillation (ENSO) from the Pacific Decadal Oscillation (PDO). CICA results indicate that ENSO-related patterns can be extracted from the Gravity Recovery And Climate Experiment Terrestrial Water Storage (GRACE TWS) with an accuracy of 0.5–1 cm in terms of equivalent water height (EWH). The magnitude of errors in extracting NAO or AMO from SST data using the complex EOF (CEOF) approach reaches up to ~50% of the signal itself, while it is reduced to ~16% when applying CICA. Larger errors with magnitudes of ~100% and ~30% of the signal itself are found while separating ENSO from PDO using CEOF and CICA, respectively. We thus conclude that the CICA is more effective than CEOF in separating non-stationary patterns.     

基于MapSIS的地震地球物理台网事件可视化平台建设

地球物理台网观测数据是反映前兆台网数据变化信息的重要部分,可为监测数据质量评估、地震分析预报会商、地震科学研究等工作提供科学依据。为充分利用这些保存在数据库中的事件数据记录,研制了基于MapSIS的地球物理台网事件可视化平台。该平台实现了事件记录的人工交互分析、自动统计分析,基于震情自动触发的专题报告自动生成等功能,具有功能全面、运行稳定的优点。该平台现已在全国地震前兆台网、地震分析预报会商中部署使用,可为地震分析预报提供科学可靠的信息服务。     

Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey)

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.     

Segmentation algorithm for long time series analysis

Time series analysis is an important issue in the earth science-related engineering applications such as hydrology, meteorology and environmetrics. Inconsistency and nonhomogeneity that might arise in a time series yield segments with different statistical characteristics. In this study, an algorithm based on the first order statistical moment (average) of a time series is developed and applied on five time series with length ranging from 84 items to nearly 1,300. Comparison to the existing segmentation algorithms proves the applicability and usefulness of the proposed algorithm in long hydrometeorological and geophysical time series analysis.     

我国金属与非金属矿物探的回顾与展望

概要回顾了我国金属与非金属矿物探的历史及47年来所经历的四个发展阶段．物探方法由4种增加至40余种，一些已达国际先进水平．47年来，航空物探和区域重力调查基本覆盖了我国陆地面积，提出一大批基础地质和深部地质问题的新认识．物探发现了数以千计的金属与非金属矿的矿床．在技术方面，建立起了第二代航空物探系列、地面高精度重力和磁勘查系列、综合电法系列、综合核物探系列以及地下物探和测井系列．针对固体矿产的勘查任务，提出了物探的发展方向和一些建议．     

An improved time series model for monthly stream flows

This study aims to develop an improved time series model to overcome difficulties in modeling monthly short term stream flows. The periodic, serial dependent and independent components of the classical time series models are improved separately by information transfer from a surrounding long term gauging station to the considered flow section having short term records. Eventually, an improved model preserving the mathematical model structure of the classical time series model, while improving general and monthly statistics of the monthly stream flows, is derived by using the improved components instead of the short term model components in the time series modeling. The correlative relationships between the current short term and surrounding long term stations are used to improve periodic and serial dependent behaviors of monthly flows. Independent components (residuals) are improved via the parameters defining their theoretical probability distribution. The improved model approach is tested by using 50 year records of Göksu-Himmetli (1801) and Göksu-Gökdere (1805) flow monitoring stations located on the Ceyhan river basin, in south of Turkey. After 50 year records of the station 1801 are separated into five 10 year sub series, their improved and classical time series models are computed and compared with the real long-term (50 year) time series model of this station to reveal efficiencies of the improved models for each subseries (sub terms with 10 year observation). The comparisons are realized based on the model components, model estimates and general/monthly statistics of model estimates. Finally, some evaluations are made on the results compared to the regression method classically applied in the literature.     

Discreteness of spatial-temporal manifestations of solar activity and solar-terrestrial phenomena

The discreteness of the time series of the solar-related geophysical phenomena is justified. The balance of the formation and decomposition (neglecting diffusion) of fractal elements during the evolution of the equilibrium systems as applied to the heliophysical and geomagnetic phenomena is the original position in this case. The discreteness properties of the spatial-temporal characteristics of solar activity are simply justified based on a fractal analysis. The performed calculations in the solar wind medium in the near-Earth space make it possible to classify the fluxes depending on their fractal dimension. The possible mechanism by which high-speed solar wind streams are generated is also briefly discussed in the scope of the fractal paradigm.     

On reconstruction of the Ito-like equation from persistent time series

The Langevin equation with finite-range persistence was introduced as a macroscopic model of various geophysical phenomena. The modified histogram procedure (MHP) of reconstruction of the equation from time series was proposed. An efficiency of MHP was tested on artificial persistent time series (with short and long-tail distributions) generated by different Ito-like equations. For an exemplary geophysical time series, the appropriate Ito-like equation was reconstructed.