Earthquake location in strongly heterogeneous media

Traveltime computation methods for strongly heterogeneous 3-D media developed during recent years are well suited for earthquake location. We present here a new method based on the traveltime algorithm of Podvin-Lecomte, related to the inverse problem formulation of Tarantola & Valette. The Podvin-Lecomte method, based on the Huygens principle, is very robust and allows arbitrary surface topography and station placement even for borehole instruments. First arrival traveltimes are computed for each of the recording stations using a fine 3-D velocity mesh (up to 10⁶ cells on a workstation). The traveltime grid allows the use of the Tarantola & Valette formulation, which enables a full non-linear approach. The solution is given as a 3-D probability density function of hypocentre coordinates, which accounts for the arrival time measurements as well as a priori information for the location, the accuracy of both the arrival time readings and the computation of the theoretical traveltimes. This powerful method called 3DGRIDLOC gives the location of the induced seismicity of the gas field of Lacq (France) using 443 520 cells of a 3-D velocity mesh and the observations from nine recording stations, one of which is located at the bottom of a 3880 m deep borehole. Location of synthetic foci as well as more than 500 actual earthquakes shows the real advantages of this new method versus the classical HYPO71. A new insight into the induced seismicity is now possible: induced seismicity may occur as far away as 10 km from the gas reservoir and involve a much greater volume of rock than expected using earlier locations.     

RECENT DEVELOPMENTS IN MULTIVARIATE CALIBRATION

With the goal of understanding global chemical processes,environmental chemists have some of the mostcomplex sample analysis problems.Multivariate calibration is a tool that can be applied successfully inmany situations where traditional univariate analyses cannot.The purpose of this paper is to reviewmultivariate calibration,with an emphasis being placed on the developments in recent years.The inverseand classical models are discussed briefly,with the main emphasis on the biased calibration methods.Principal component regression(PCR)and partial least squares(PLS)are discussed,along with methodsfor quantitative and qualitative validation of the calibration models.Non-linear PCR,non-linear PLSand locally weighted regression are presented as calibration methods for non-linear data.Finally,calibration techniques using a matrix of data per sample(second-order calibration)are discussed briefly.     

北方农牧交错带草原产草量遥感监测模型

及时准确地了解草原产草量的时空配置状况,对于科学合理地利用、管理草地,保证畜牧业生产持续稳定发展、改善生态环境等具有重要的意义。本文利用2005年的MODIS数据和同期野外实测的668个样方产草量数据,分析了5种植被指数和草地生物量之间的相关关系。研究表明:(1)分区模型优于不分区模型,在分区基础上建模更能反映产草量的实际情况;(2)通过线性、非线性模型和BP神经网络模型的对比,得出BP神经网络模型在拟合精度上优于线性和非线性模型,是最适宜监测北方农牧交错带草原产草量的模型;(3)5种植被指数中,NDVI和SAVI与草地生物量之间的拟合精度最高,是研究区最适宜使用的植被指数。     

青海湖环湖地区草地植被生物量遥感监测模型

利用青海湖环湖地区2000年陆地卫星TM遥感图像数据和同期野外实测的34处样方产草量数据,分析了遥感植被指数与草地植被生物量之间的相关关系,进而分别建立了遥感植被指数与草地植被生物量的一元线性回归模型和非线性回归模型。研究表明,遥感植被指数与草地生物量之间存在较好的相关性,但不同遥感植被指数与草地植被生物量相关性程度存在一定差别。此外,所建遥感植被指数与草地植被生物量的非线性回归模型在拟合精度上优于一元线性回归模型,且由三次方程得到的非线性回归模型最适用于监测青海湖环湖地区的草地植被生物量。     

DETECTING AND ADJUSTING FOR NON-LINEARITIES IN CALIBRATION OF NEAR-INFRARED DATA USING PRINCIPAL COMPONENTS

A new regression method for non-linear near-infrared spectroscopic data is proposed.The technique isbased on a model which is linear in the principal components and simple functions(squares and products)of them.Added variable plots are used to determine which squares and products to incorporate into themodel.The regression coefficients are estimated by a Stein estimate which shrinks towards the estimatedetermined by the first several principal components and the selected non-linear terms.The technique isnot computationally intensive and is appropriate for routine predictions of chemical concentrations.Themethod is tested on three data sets and in all cases gives more accurate predictions than does linearprincipal components regression.     

REVIEW ARTICLE MULTIVARIATE EXPLORATORY DATA ANALYSIS AND GRAPHICS:A TUTORIAL

Exploratory data analysis(EDA)is a toolbox of data manipulation methods for looking at data to seewhat they seem to say,i.e.one tries to let the data speak for themselves.In this way there is hope thatthe data will lead to indications about'models'of relationships not expected a priori.In this respect EDAis a pre-step to confirmatory data analysis which delivers measures of how adequate a model is.In thistutorial the focus is on multivariate exploratory data analysis for quantitative data using linear methodsfor dimension reduction and prediction.Purely graphical multivariate tools such as 3D rotation andscatterplot matrices are discussed after having introduced the univariate and bivariate tools on which theyare based.The main tasks of multivariate exploratory data analysis are identified as'search for structure'by dimension reduction and'model selection'by comparing predictive power.Resampling is used tosupport validity,and variables selection to improve interpretability.     

Three-dimensional inversion without blocks

Summary. We propose a method for solving non-linear inverse problems in the case where the unknown is a function of the spatial coordinates and the data set is discrete and finite. The method is based on a generalized leastsquares criterion, it is defined directly for non-linear problems (without previous linearization of the forward problem), and in the particular linear case it gives the same results (although slightly more general) than the Backus & Gilbert approach. As an example, we apply the method to the three-dimensional seismic velocity inverse problem, using as data the arrival times of seismic waves. The following paper (Nercessian et al .) shows some esults obtained using the present method.     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

Cluster analysis of seismic moment tensor orientations

This paper demonstrates that well-known methods of cluster analysis and multivariate data analysis are useful for geodynamic interpretation of seismic moment tensors. To use these methods, moment tensors are expressed as vectors in a 6-D space. These are vectors in a rigorous sense, rather than an arbitrary set of ordered numbers, because a dot product can be defined that is independent of the coordinate system. In this vector space, non-isotropic moment tensors are a 5-D linear subspace and normalized moment tensors are unit vectors, or points on a unit sphere. Distance along a great circle of the unit sphere satisfies reasonable requirements for any measure of the difference between normalized moment tensors. In regions with a few isolated sets of orientations, cluster analysis based on the great circle distance identifies the same groups of earthquakes that a seismologist would. Figures based on principal component analysis and discriminant analysis illustrate orientation clustering better than equal area projections of moment tensor principal axes. In one case where clusters have been claimed to exist, orientations appear to be continuously distributed and no evidence is found for separate populations of moment tensors.     

Resolution analysis for discrete systems

summary . Treatments of geophysical inverse problems have tended to polarize into approaches intended to generate models either described by piecewise continuous functions or with some prior discretization. The two approaches are here developed in parallel, and the ideas of a trade-off between the anticipated error and the attainable level of detail in the model estimate are extended to the discrete case, either with even or uneven discretization.
An alternative approach to specifying the potential resolution of a model is to establish upper and lower bounds on parameter values. Linear programming methods are extended to determine bounds which allow for subjective limits on parameter values.
For a non-linear system the possible resolution may be investigated by estimation procedures based on the full set of successful solutions obtained by Monte-Carlo inversion.     

标度律视角的城市效能测度及中国城市多维要素效能分析

“城市标度律”（urban scaling laws）揭示了城市系统中城市人口和城市要素间普遍存在的非线性规模缩放关系,但目前大多数城市评价忽视了这种关系,常使用人均指标比较不同规模的城市,这会导致评价结果偏差。为此,考虑城市人口和城市要素间的非线性规模缩放关系构建了要素效能指数,用以修正城市规模的影响,度量城市要素实际发挥出预期能力的程度。以城市发展不同维度上的9个城市要素为例,分别测度了2017年中国292个地级以上城市各要素的效能,识别了各要素效能的空间差异和集聚特征,分析了要素效能指数的优势并讨论了结果的政策启示。结果表明：① 9个城市要素都符合“城市标度律”,其中8个要素与城市人口呈非线性规模缩放关系;从标度律的视角测度城市效能是必要而且可行的。② 城市综合经济、商业消费和公共财政效能普遍以东部沿海地区较高;东北地区的基础教育和中西部地区的公共交通效能较低;土地和水资源禀赋较高的地区对应的效能值并不高;大部分城市的生态绿地效能较低。600万人以上的城市中,杭州、深圳、天津的各维度效能表现较均衡。③ 与人均指标评价结果的对比证明要素效能指数能够修正人口规模对城市评价的影响,对常用的总量和人均指标是有益的补充。标度律视角下的城市效能测度能够给城市人口管理和城市要素发展的决策提供明确的指导方向,同时为不同规模大小的城市提供更客观的比较标准,对国家城镇体系规划和落实中国新型城市化战略具有积极意义。     

Stable Iterative Methods for the Inversion of Geophysical Data

Summary. Interpretation of earth electrical measurements can often be assisted by inversion, which is a non-linear model-fitting problem in these cases. Iterative methods are normally used, and the solution is defined by' best fit'in the sense of generalized least-squares.
The inverse problems we describe are ill-posed. That is, small changes in the data can lead to large changes in both the solution and in the iterative process that finds the solution. Through an analysis of the problem, based on local linearization, we define a class of methods that stabilize the iteration, and provide a robust solution. These methods are seen as generalizations of the well-known Singular Value Truncation and Marquardt Methods of iterative inversion.
Here, and in a companion paper, we give examples illustrating the successful application of the method to ill-posed problems relating to the resistivity of the Earth.     

基于神经网络的单元自动机CA及真实和优化的城市模拟

提出了一种基于神经网络的单元自动机（CA）。CA已被越来越多地应用在城市及其它地理现象的模拟中。CA模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。     

“一带一路”沿线汽车市场时空变化特征及影响因素研究:基于2005-2017年面板数据

“一带一路”沿线是中国品牌汽车主要出口市场,研究“一带一路”沿线区域汽车市场变化对中国品牌汽车深入实施国际化战略有重要意义。该文采用空间自相关、新兴时空热点分析、面板数据模型等方法分析2005—2017年“一带一路”沿线59个国家汽车市场时空变化特征及影响因素,研究发现：①“一带一路”沿线国家汽车消费市场整体增长较为缓慢,销量占全球汽车销量比重呈现下降趋势;②不同区域的汽车市场存在较大差异,南亚地区汽车产销规模大而且增长较快;③大部分国家汽车市场主要依赖进口,南亚及东南亚地区汽车市场潜力较大;④新兴时空热点分析方法识别出印度、俄罗斯、伊朗及印度尼西亚为连续热点、加强的热点、持续的热点等三种时空变化模式;⑤ 面板数据动态差分GMM模型表明“一带一路”沿线滞后一期汽车销量、人均GDP、服务业增加值、汽车产量、城镇人口等因素对汽车销量有促进作用。研究认为在中美经贸关系频繁波动背景下,中国汽车企业应该高度重视“一带一路”汽车市场的重要性,在不同区域因地制宜地采取差异化国际化策略;根据“一带一路”汽车市场具有惯性效应这一特点,中国汽车企业可侧重印度、俄罗斯、伊朗、印度尼西亚等已有汽车市场规模较大的国家深入推进国际化战略。     

基于核主成分元胞模型的城市演化重建与预测

通过元胞自动机(CA)模拟和重建城市演化的复杂非线性过程,对于城市土地利用规划和决策具有指导意义。利用传统线性方法获取的地理CA转换规则,较难刻画城市演化的时空动力学过程。基于核主成分分析方法(KPCA),通过核函数映射,在高维特征空间下不仅能够对多重共线的空间变量进行非线性降维,且由此建立的地理元胞模型KPCA-CA参数物理意义明确,能够较好地体现城市化过程的非线性本质。基于GIS环境下自主研发的地理模拟框架SimUrban,利用该KPCA-CA模型模拟和重建了快速城市化区域上海市嘉定区1989-2006年城市演化过程,并预测了研究区2010年的城市空间格局。模拟结果显示,嘉定区城市主要沿中心区域及主干道路而扩展,体现了KPCA方法提取的前两个主成分的作用,与城市实际发展情况相符。利用混淆矩阵和面积控制精度等指标,对模拟结果进行了评价,得到总体精度为80.67%、Kappa系数为61.02%,表明模拟结果与遥感分类结果及统计结果符合程度较好;与传统基于线性方法的地理CA模型比较,KPCA-CA模型模拟结果精度更高。     

基于均一化资料的西安极端气温变化特征研究

基于1960~2015年西安气象站点逐日最高温、最低温数据,采用RHtest软件对非均一化气温序列进行订正,进而选取16项极端气温指数,对西安极端气温变化特征进行分析。结果表明：① 由于气象站点迁移,西安气温资料存在非均一性,导致极端气温变化趋势被低估;② 全球变暖背景下,西安极端气温变化表现出：“快速增温与平稳波动并存,冷暖变化趋势相反,夜晚增暖趋势比白天明显,白天波动变化明显于夜晚,持续性高温事件变化不大,持续性低温事件大幅下降”的变化特征;③ 通过不同区域趋势变化对比、冷暖、昼夜变化关系对比发现,受城市热岛影响,西安极端低温事件减少更为突出,远高于中国其他对比区域（秦岭南北、黄土高原、东北地区等）;④ 在昼夜变化上,西安极端气温变化与中国、全球变化具有一致性,但是通过冷暖指标对比发现,西安极端气温变化具有区域性,表现为冷昼日数下降高于暖昼日数上升,冷夜日数下降高于暖夜日数上升,冷持续日数和暖持续日数共同表现为下降趋势。     

中资企业离岸研发研究进展：经济地理学的探索

基于经济地理学领域的相关文献,系统梳理并评述了中资企业离岸研发的已有研究进展。研究发现,目前国内外经济地理学在中资企业离岸研发的区位选择、空间组织、行为主体等作用机制及溢出效应等方面取得了一批研究成果：中资企业离岸研发的区位主导因素因东道国特征和企业发展阶段不同而有所侧重,离岸研发的空间组织模式及其知识流动路径因其在全球创新格局中的地位而呈现差异化特征,其中跨国人员在帮助企业及地区获取其知识溢出并实现技术追赶中发挥重要的“边界扳手”作用;但同时现有研究也存在理论针对性不强、分析手段缺乏、溢出效应重视不够等问题：建议针对中资企业离岸研发活动的特点,从关系、制度、演化的视角入手,建立新的企业离岸研发理论假说。并从创新活动“流的空间”的视角入手,揭示多尺度下中资企业离岸研发空间演化规律;从关系和演化经济地理学视角入手,厘清不同尺度及多维邻近性视角下中资企业离岸研发区位因子;从中资企业特殊商业文化入手,分析华人华侨、海外人才等行为主体在中资企业离岸研发活动形成与发展中的作用;从创新溢出效应的视角入手,区分中资企业离岸研发活动对母国和东道国发展的影响。     

Using evidence of non-linear induced polarization for detecting extended ore mineralizations

The discrimination between electrolytic and electronic conductors is highly relevant to geological modelling as it allows conclusions to be drawn about the formation and mineral composition of rocks. The induced polarization (IP) method, which compares the electric current injected into the ground with the corresponding earth potential differences can be used for this purpose.
  This paper describes a new method based on the theory that non-linear electrochemical processes on the surface of electronic conductors are responsible for non-linear IP (NLIP) phenomena. This results in multiples of the fundamental frequency being observed in the telluric voltage spectra when a monochromatic current signal is fed into the ground. The non-linearity of the current–voltage characteristic is most effectively described by a spectral method.
  A laboratory experiment was carried out, using an electrolytic trough with a small graphite cylinder serving as an electronic conductor, which clearly demonstrated the validity of the method. A field experiment was undertaken at a borehole of approximately 450  m depth, located in the transition zone of the Tepla-Barrandium and Moldanubicum in East Bavaria. A sinusoidal current was injected into the ground using a logging tool at depths varying between 150 and 450  m. The corresponding potential differences were simultaneously observed along a profile on the surface. Field and laboratory results show a striking similarity. It can be concluded that an extensive electronic conductor—probably graphite—is steeply dipping southwards meeting the borehole at approximately 310  m depth.     

Error propagation in non-linear delay-time tomography

Delay-time tomography can be either linearized or non-linear. In the case of linearized tomography, an error due to the linearization is introduced. If the tomography is performed in a non-linear fashion, the theory used is more accurate from the physical point of view, but if the data have a statistical error, a noise bias in the model is introduced due to the non-linear propagation of errors. We investigate the error propagation of a weakly non-linear delay-time tomography example using second-order perturbation theory. This enables us to compare the linearization error with the noise bias. We show explicitly that the question of whether a non-linear inversion methods leads to a better estimation of the model parameters than a linearized method is dependent on the signal-to-noisc ratio. We also show that, in cases of poor data quality, a linearized inversion method leads to a better estimation of the model parameters than a non-linear method.     

A method of obtaining a velocity-depth envelope from wide-angle seismic data

Summary. Due to the non-uniqueness of traveltime inversion of seismic data, it is more appropriate to determine a velocity-depth ( v-z ) envelope, rather than just a v-z function. Several methods of obtaining a v-z envelope by extremal inversion have been proposed, all of which invert the data primarily from either x-p , or T-p , or both domains. These extremal inversion methods may be divided into two groups: linear extremal and non-linear extremal. There is some debate whether the linearized perturbation techniques should be applied to the inherently non-linear problem of traveltime inversion. We have obtained a v-z envelope by extremal inversion in T-p with the constraint that the inversion paths also satisfy x-p observations. Thus we use data jointly in r-p and x-p , and yet avoid the linearity assumptions.
This joint, non-linear extremal inversion method has been applied to obtain a v-z envelope down to a depth of about 30 km in the Baltimore Canyon trough using x-t data from an Expanding Spread Profile acquired during the LASE project. We have found that the area enclosed by the v-z envelope is reduced by about 15 per cent using x-p control on the T-p inversion paths, compared to the inversion without x-p control.