深基坑施工时地表沉降预测的时序-投影寻踪回归模型

为了保证施工的正常进行、实现信息化施工,必须对基坑实际监测数据进行分析与预测。现有的趋势时间序列分析方法很难满足实际施工中高度非线性问题的拟合,预测误差较大。基于这点考虑,以天津某工程基坑施工地表沉降观测序列为例,在对原始数据进行分析的基础上,提出既可以考虑趋势时间序列,又具有高度非线性拟合性能的时序-投影寻踪回归模型。首先,通过比较分析几种时间序列方法的逼近误差和预测误差,寻求出一种逼近较好的时间序列预测方法。然后,将预测得到的时间序列和观测数据相结合,采用投影寻踪回归方法拟合。应用结果表明,该模型逼近性能良好,预测误差小,可为深基坑位移沉降的动态预测提供一条较好的途径,对基坑动态设计与信息化施工等方面具有重要的参考价值。     

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

Various methods have been used to model the time-varying curves within the global positioning system (GPS) position time series. However, very few consider the level of noise a priori before the seasonal curves are estimated. This study is the first to consider the Wiener filter (WF), already used in geodesy to denoise gravity records, to model the seasonal signals in the GPS position time series. To model the time-varying part of the signal, a first-order autoregressive process is employed. The WF is then adapted to the noise level of the data to model only those time variabilities which are significant. Synthetic and real GPS data is used to demonstrate that this variation of the WF leaves the underlying noise properties intact and provides optimal modeling of seasonal signals. This methodology is referred to as the adaptive WF (AWF) and is both easy to implement and fast, due to the use of the fast Fourier transform method.

     

黄河上游龙羊峡至积石峡段巨型滑坡OSL测年

通过对青藏高原东部黄河上游龙羊峡—积石峡段10个特大型古滑坡的地质结构、地层岩性的分析,结合各滑坡特征,在特定部位进行采样,归纳整理出滑坡光释光（OSL）测年样品的5种采样方法。选取11个样品应用OSL测年方法,得到各滑坡的年龄数据,分析各样品的可靠性,并探讨其构造时间、气候变化与滑坡群发性之间的关系。研究表明,由于样品来源广泛,实验方法高效准确,OSL方法可成功地应用于滑坡年龄测定,为滑坡研究提供可靠的地质依据。研究区特大型群发性滑坡和初步划分为4期分别为130~110ka、50~30ka、12~9ka和5~3ka,并解释了各期滑坡群发的主要触发因素。     

江陵凹陷北部高速玄武岩下二维广角地震初探

江汉油田江陵凹陷北部高速玄武岩发育,利用常规地震资料很难得到高速玄武岩下的地震反射。分析研究表明,广角地震有利于玄武岩下的地震成像。通过综合正演和实际地震数据完成了广角地震资料的处理,获得了江陵凹陷北部高速玄武岩下的反射地震信息。在开展广角地震波场的识别和利用研究过程中,从正演模型出发,探讨了在不同地质条件下高速层对地震波场的影响,并针对靶区资料讨论了如何识别广角地震信息。在上述基础上,通过对实际地震资料做精细的分析和处理,最终完成了高速玄武岩下地震资料的成像。     

Optimized Sample Schemes for Geostatistical Surveys

Sample schemes used in geostatistical surveys must be suitable for both variogram estimation and kriging. Previously schemes have been optimized for one of these steps in isolation. Ordinary kriging generally requires the sampling locations to be evenly dispersed over the region. Variogram estimation requires a more irregular pattern of sampling locations since comparisons must be made between measurements separated by all lags up to and beyond the range of spatial correlation. Previous studies have not considered how to combine these optimized schemes into a single survey and how to decide what proportion of sampling effort should be devoted to variogram estimation and what proportion devoted to kriging An expression for the total error in a geostatistical survey accounting for uncertainty due to both ordinary kriging and variogram uncertainty is derived. In the same manner as the kriging variance, this expression is a function of the variogram but not of the sampled response data. If a particular variogram is assumed the total error in a geostatistical survey may be estimated prior to sampling. We can therefore design an optimal sample scheme for the combined processes of variogram estimation and ordinary kriging by minimizing this expression. The minimization is achieved by spatial simulated annealing. The resulting sample schemes ensure that the region is fairly evenly covered but include some close pairs to analyse the spatial correlation over short distances. The form of these optimal sample schemes is sensitive to the assumed variogram. Therefore a Bayesian approach is adopted where, rather than assuming a single variogram, we minimize the expected total error over a distribution of plausible variograms. This is computationally expensive so a strategy is suggested to reduce the number of computations required     

Missing data estimation in tyrannosaurid dinosaurs: Can diameter take the place of circumference?

Missing data, due to taphonomic deformation, inaccessibility of specimens, or human error in collecting, cataloguing and measuring features, is a formidable problem in current palaeontological studies. Missing values within a data set can undermine confidence in analyses, skew results in promoting analyses of small portions of a population, not necessarily representative of the entire data set, and drastically decrease sample sizes. Missing data estimation methods, however, may reduce the effects of these missing values and potentially boost sample sizes for palaeontological studies. Here, six missing data estimation models for the prediction of femoral circumferences in tyrannosaurids were statistically tested and their predictive success measured against true circumferences, and other models. The statistical analyses suggest that estimation models based on anteroposterior diameter values of tyrannosaurid femora were poor predictors of circumference, whereas those based on mediolateral diameters were much more successful. Three out of the six models, were presented as viable alternatives to missing measured circumferences and may be used to boost tyrannosaurid samples with significant levels of missing data.     

Estimation of uranium from multichannel spectra in airborne gamma ray spectrometry using 1.12 MeV and 1.76 MeV photopeaks of 214Bi — An application to Cuddapah Basin

Present study examines the utility of gamma rays of energies 1.12 MeV and 1.76 MeV of uranium in estimation of uranium in airborne gamma ray spectrometric survey (AGRS) in comparison to the conventional 1.76 MeV alone used world wide. As a case study, the AGRS data of northern part of Cuddapah Basin is considered. Multi-channel processing is applied on this dataset to reduce the spectral noise and use of combined 1.12 MeV and 1.76 MeV has reduced the Poisson’s related statistical error. Uranium is estimated using above two energies individually and in combination, using multi-channel processing followed by standard corrections procedure. Result indicates that a combination of both energies has given best estimation due to a reduction in overall noise which helped in demarcating geological entities and litho-contacts. This indicates the efficacy of this technique which improved the radio-geochemistry of study area in understanding the radio-elemental variation.     

国外大地电磁响应函数估计方法

从本质上来说,能否得到大地电磁响应函数的无偏估计,取决于实测电磁场时间序列中所含噪声的类型和强度。当磁场时间序列不含噪声且电场仅含不相关噪声时,单站ＲｏｂｕｓｔＬＳ方法是有效的;当磁场和电场时间序列同时含有不相关噪声时,Ｒｏｂｕｓｔ远参考方法也是有效的;但当磁场和电场时间序列含较强的相关噪声时,已有的方法已无能为力。文中在简要回顾大地电磁法的发展历史之后,对国外估计大地电磁响应函数的方法作了较全面的总结和评述,并着重介绍了几种值得关注的新方法。应该指出,受欢迎的方法必须兼顾估计的精度、稳定性和野外工作效率。     

BP神经网络在自喷井井底流压预测中的应用

利用神经网络具有的高度非线性映射能力,将其应用于井底流压的预测,以解决现有各种方法不能完全满足需要的矛盾。从网络的结构和算法、训练样本的选择和处理、网络的学习精度和泛化能力等三个方面对BP神经网络进行研究和改进,建立了井底流压的预测模型,并对某油田的实测井底流压数据进行了精度检验和精度分析。预测结果表明,相对误差最大为2.0821%,平均相对误差为1.5108%,绝对误差一般在0.5~1 MPa,高于其他各种计算方法的精度。     

An application of the deterministic variogram to design-based variance estimation

When estimating the mean value of a variable, or the total amount of a resource, within a specified region it is desirable to report an estimated standard error for the resulting estimate. If the sample sites are selected according to a probability sampling design, it usually is possible to construct an appropriate design-based standard error estimate. One exception is systematic sampling for which no such standard error estimator exists. However, a slight modification of systematic sampling, termed 2-step tessellation stratified (2TS) sampling, does permit the estimation of design-based standard errors. This paper develops a design-based standard error estimator for 2TS sampling. It is shown that the Taylor series approximation to the variance of the sample mean under 2TS sampling may be expressed in terms of either a deterministic variogram or a deterministic covariance function. Variance estimation then can be approached through the estimation of a variogram or a covariance function. The resulting standard error estimators are compared to some more traditional variance estimators through a simulation study. The simulation results show that estimators based on the new approach may perform better than traditional variance estimators.     

微地震数据去噪方法综述

随着常规油气藏资源不断枯竭,非常规油气藏的勘探开发已逐渐成为一种必然趋势,从而使得微地震监测技术快速发展。微地震事件的发生持续时间短、声波频率高,使得实际采集到的微地震数据信噪比较低。本文首先简要介绍了微地震监测技术能够在非常规油气藏开发中保证高效的增产,以及微地震噪声压制在微地震监测技术数据处理流程中是关键一步,直接影响着后续微地震研究的准确性和可靠性;并对地面微地震监测数据中的噪声源进行分析,归纳了地面微地震监测中常见噪声：强脉冲干扰、工业交流电干扰、钻井干扰、声波干扰、规则干扰等,分析了其各自的基本特点。然后,概述了地面微地震数据去噪方法已取得的成果,按频率、传播方向、空间分布区域等特性进行分类,分析各种去噪方法在实际应用过程中针对的噪声类型,以及在去噪过程中对有效信号造成的影响等。最后,基于深度学习具有更强的复杂函数表征能力,分析了3种典型深度学习模型的结构及特点;结合在其他相关领域数据去噪问题的成功应用,深度学习可以解决目前微地震数据噪声压制存在的问题,可以作为微地震数据去噪的一种新方法;考虑到目前微地震数据样本数量可能影响深度学习在微地震监测中大规模的应用,本文提出用生成式对抗网络来构建微地震数据样本库以解决该问题,并将其用于后续深度学习过程中的模型训练。     

基于遗传熵谱估计的年径流周期识别

为识别年径流量序列的隐含周期,提出基于加速遗传算法的熵谱估计算法,与传统的方差谱和Burg谱相比,该方法由熵谱分析的4个等价条件构建多目标函数,并以加速遗传算法作为优化算法,谱估计结果不依赖于初始值的选取,对数据长度、信噪比和初相位有较强的适应性。在三川河流域后大成站1956-2000年径流量序列周期识别中的应用结果表明,在95%的置信检验水平下,序列中存在着12.29年和2.67年的显著隐含周期,为三川河流域年径流的变化规律和变化的阶段性研究提供了一条新的定量研究手段。     

地面核磁共振模型约束反演含水层参数

导电模型的地面核磁共振感应电动势是含水量的非线性函数.引入模型约束的迭代反演方法求解该非线性问题的反演.在反演过程中,根据理论公式计算迭代过程中灵敏度矩阵,并采用平坦模型和光滑模型两种约束.对均匀半空间、层状导电模型和实际数据进行了反演模拟,结果表明,模型约束迭代反演方法能从地面核磁共振感应电动势获得含水层较为合理的含水量及分布,且该结果可以从作为初始模型的均匀含水量分布反演得到.对无噪音数据,平坦模型和光滑模型约束对反演的含水量分布影响不大;但当数据存在一定的噪音时,平坦模型约束将比光滑模型约束获得更为精确的含水层参数.     

Fitting Data Under Omnidirectional Noise: A Probabilistic Method for Inferring Petrophysical and Hydrologic Relations

While geophysicists recognize that all measurements of media properties are subject to noise, methods for fitting petrophysical relations commonly employ regression-based formulas, which assume no error in one of the properties being related (the “independent" variable). To derive a more rigorous method for fitting such relations, we take a probabilistic viewpoint on the problem of fitting petrophysical relations to sample correlation data. Unlike prior approaches, we take into account the fact that noise is present in both measured properties. Under basic assumptions, we derive a new objective function for such problems which is proportional to the data likelihood and which can be used for both model parameter optimization and model selection. We present several numerical experiments outlining the utility of our method, and compare results of our method against results of other commonly used methods, such as kriging, regression, and total distance minimization. The results of our applications using the maximum likelihood technique appear visually accurate, and we also provide quantitative comparisons of performance that suggest the method produces more desirable results.     

Nonlinear regression for dependent variables

Nonlinear regression methods can be used to fit functions for two related variables where both variables are subject to error. A computer program for nonlinear estimation described previously has been modified to fit such functions for a given set of data. A numerical example is provided for a second-degree equation in xand y.A closer fit to an observed set of data is possible if the error structure for the variables is specified.     

光释光测年中石英样品提纯方法的改进

为了解决光释光前处理中遇到的传统方法无法获得满足纯度石英的问题,通过大量对比实验,结合玻璃工艺中石英分离研究成果,最后获得了新的石英提纯流程,与传统方法相比,新方法通过化学分离避免了密度变化对石英长石分离的影响,从而解决了斜长石和岩屑在传统的重液分离中无法剔除的问题,新方法能够获得满足纯度要求和量要求的石英,同时,新方法比传统方法实验室工作效率更高。     

南海珊瑚礁光学浅水海域多光谱遥感水深反演

海洋水深信息对研究珊瑚礁海域资源与环境具有重要作用。南海珊瑚礁海域测深数据受多种条件限制施测困难,在时间与空间方面数量非常有限。文章针对南海岛礁海域以I类水体为主导的海水光学特性,以南沙群岛库归沙洲海域为例,使用Sentinel-2多光谱卫星遥感影像和同期过境的MODIS卫星数据,构建底质光谱,采用半分析半经验模型计算海水表面遥感反射率与海水叶绿素浓度,通过对数比值模型进行该地区光学浅水海域遥感水深反演分析,并进一步通过多时相反演水深融合提升精度。经与多波束实测水深数据验证,研究区域反演水深总体均方根误差和平均相对误差分别为2.68 m 和9.99%。该方法通过叶绿素浓度推演部分海水光学特性,可以从多光谱卫星影像中快速获取南海岛礁光学浅水海域初步水深信息,供相关海洋领域分析与应用。     

小波包分解提取高采样率GPS地震波信号

随着高采样率GPS接收机的出现、高精度的定轨以及数据处理技术的发展, 利用GPS观测高频率、瞬态的地震波信号成为可能.但如何消除混叠在地震波信号中的多路径、随机噪声等误差, 有效地提取地震波信号, 仍然是制约高采样率GPS及其地球物理应用的重要因素.提出一种基于小波包分解的方法, 对动态位移序列中的多路径误差进行消除, 同时去除高频率随机噪声, 提取地震波信号.通过结合SCIGN的19个GPS测站的1Hz采样GPS观测数据, 对2010年墨西哥M7.2地震的地震波引起的地表动态位移进行解算, 采用小波包分解有效地提取地震波信号并对其进行谱分析.结果表明, 该方法提取的地震波信号能较好地反映出地震波的传播及其特性, 具有无需多天重复观测、处理精度高等特点.      

Format and philosophy for collecting,compiling, and reporting microprobe monazite ages

Microprobe monazite dating has been increasingly used to constrain the timing of deformation and metamorphism because of the potential to date very small monazite domains (down to 5 μm or less) in structural and petrologic context. This paper presents an analytical strategy, presentation format, and error considerations for microprobe monazite dating. The strategy involves high-resolution compositional mapping to delineate compositional domains within monazite crystals. Then for each compositional domain, a series of Th, U and Pb analyses are made, and a single date and error are calculated. The number of analyses in each domain is determined by the desired statistical precision of the date. Results from several monazite grains are typically combined and, along with textural relationships, are used to build an argument that the dates constrain the age of a deformation or metamorphic event. The total error involves three components: short-term random error (dominated by counting statistical uncertainty), short-term systematic error (uncertainty in background correction, conductive coating variation, and calibration), and long-term systematic error (uncertainty in standard composition, mass absorption factors, decay constants, etc.). In homogeneous compositional domains, short-term random errors (2σ) of less than 10 m.y. can be obtained from five to ten analyses. However, short-term systematic error, mainly background estimation uncertainty, would typically result in a doubling of the magnitude of random error. Microprobe dates are presented as a single Gaussian probability distribution for each domain, along with representative compositional maps. It is recommended that a consistency standard be analyzed during each analytical session and the results be reported along with those from the unknown. This proposed strategy and format are compatible with those of other geochronological techniques; they incorporate analytical limitations associated with trace, as opposed to major element, microprobe analysis, and will allow better comparisons to be made between labs and between different geochronological techniques.     

Revisited rainfall network design: evaluation of heuristic versus entropy theory methods

Rain gauges are installed to measure pointwise precipitation and provide a comprehensive perspective of its spatiotemporal variations. Selection of an efficient and reliable rainfall monitoring network is a key role to reduce its maintenance and handling cost. The main purpose of the current paper is to compare efficiencies of various network design methods. The applied methods are entropy theory (as probabilistic multi-criteria decision-making) and genetic algorithm (as one of the heuristic methods) with three objective functions. Also, two classical (ordinary kriging; OK) and modern (Bayesian maximum entropy; BME) spatial simulation methods were undertaken to provide a comprehensive spatial simulation of precipitation. The proposed assessment was applied on spatial mean annual precipitation variability in the Namak Lake watershed located in the central part of Iran. The final efficiency of developed network design methods is evaluated in terms of three criteria known as mass estimation error, total error, and spatial bias of estimated rainfall. Based on the results, different network distributions have been proposed by the methods. Despite the reliability of the heuristic approach in nonlinear optimization due to its mathematical principle, the results indicated that the network design based on entropy theory can be used to estimate long-term mean annual precipitation more reliably and accurately. Results of the mass estimation error have shown 78 and 83% superiority of the entropy theory approach from the worst approach obtained from the OK and BME methods, respectively.