基于Monte Carlo模拟的三维剖面地质界线不确定性分析

三维剖面地质界线是构建三维地质结构模型的重要基础数据,其不确定性会影响三维模型的几何形态和属性分布。以单一分布为假设前提的统计学不确定性分析方法掩盖了其他概率分布特征对模型的影响。突破单一误差分布条件的假设前提,本文使用Monte Carlo方法模拟了不同概率分布情况下地质剖面数据中地质界线的抽样采集,以及地质界线空间分布的不确定性;依托地质界线空间位置与地质属性的耦合关系,提出了用地质属性概率分布实现地质界线空间不确定性的定量可视化,并结合实际地质剖面探讨了多种概率分布条件下地质界线的空间不确定性。实例研究表明,基于Monte Carlo模拟的不确定性分析方法可以突破单一误差分布假设条件,结合地质属性概率可充分揭示出建模数据的内在不确定性与模型外在要素形态之间的耦合关系。     

Dispersion attenuation structure of the crust around the Weifang-Jiashan segment of the Tanlu fault zone in North China

On the condition that the velocity ratio of compressive wave to shear wave is stable during the propagating process of seismic wave, this paper develops an estimation method for medium quality factor based on single station's travel time difference between direct S and P waves and the first period signal of direct P wave. 8 774 high SNR wave data altogether recorded by 75 stations are analyzed. The results show that: (1) under the normal regional stress field, the quality factors Q_mSP in the stable tectonic area are higher than that in the active tectonic area around the Weifang-Jiashan section and its surrounding area in the Tanlu fault zone; (2) in the Juxian-Tancheng section the seismic wave attenuation is relatively quick, and the media is relatively broken, suggesting no tectonic stress accumulation; (3) the Xinyi-Sihong section is currently locking and in accumulating elastic strain energy stage, which has the deep environment and conditions of strong earthquake generation similar to those of the 1668 M8.5 Tancheng strong earthquake.     

精密工程测量数据处理综合系统讲座 第五讲 测量机器人测量自动化系列软件及其应用

阐述了测量机器人工程控制网网观测自动化系统Geo_Net和测量机器人变形监测自动化系统Geo_Def的主要功能、特点和设计思想,介绍了该系列软件的应用情况和前景。     

磁测资料预处理中梯度改正问题探讨

在地质矿产调查、地球物理勘探中,高精度磁测以经济、实用、快捷等特点,在寻找铁磁类矿物及构造中被广泛应用,但在磁测工作中,资料预处理正常场梯度改正比较繁琐,误差又比较大,笔者通过论述和实列对比,指出了目前磁测规范中正常场梯度改正存在的问题,并对正常场梯度改正方法进行了一定的探讨,提出了精度较高的正常场梯度改正方法,同时还提出了一种快捷的磁异常计算方法,使得磁测资料预处理更加简便、准确。     

工程区高地应力判据研究及实例分析

对原地应力状态的准确把握是地下工程稳定性评价的重要基础,而选择可行的高地应力判据是进行地应力状态评价的前提条件。在详细分析现有各种原地应力评价判据的基础上,提出可行的高地应力判据,并在判据模型中引入了国际上认可度非常高的Shoerey模型,但该模型没有考虑岩体变形模量和岩石弹性模量的区别。通过广泛研究目前国内外关于岩体强度的理论和方法,引入Hoek-Brown岩体强度估算理论,补充完善了Shoerey模型。云南禄丰某工程隧道,工程区共布置两个地应力测孔,测试结果较为离散。利用应力状态评价和数据拟合方法,推算出最大埋深部位的应力值达19.29 MPa,而岩体整体强度仅为5.243 MPa。按照强度应力比和Shoerey模型的评判标准,该隧道洞身部位的应力状态均为极高应力状态,应引起重视。对原地应力状态评价的一般方法和步骤进行了概括总结,以方便其他工程参考使用。     

土壤样品中稀土元素的高分辨电感耦合等离子质谱分析及其不确定度的评定探讨

在敞开体系中,用HF、HNO₃和HClO₄溶解电感耦合等离子体质谱法同时测定土壤样品中的15种稀土元素。在高分辨等离子体质谱仪(Element2)上建立了土壤样品中稀土元素含量的ICP-MS分析方法,经土壤国家一级标准物质分析验证,结果与标准值相符,测定的15种稀土元素的相对标准偏差均小于10%,加标回收率为96.5%~114.7%。实验表明,该方法不但操作简便快速,而且具有灵敏度高、检出限低,重现性好等优点,举例说明了测量不确定度的评定程序。     

CT长骨测量在骨折髓内钉固定术中的应用

目的:探讨CT在长骨测量中的应用价值。方法:经X线检查证实的肱骨、股骨、胫骨骨折患者600多例,其中302例临床行髓内钉固定术,并在术前进行了健侧骨的CT定位扫描,测量其两点之间骨干长度及骨髓腔最窄处的宽度。结果:依据术前CT定位测量数据而选择的髓内钉在手术中完全适用。结论:此方法操作简便测量数据准确为临床提供可靠依据。     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

云南地壳形变与丽江7.0级地震

本利用滇西、滇中地区大地垂直形变和跨断层短水准、短基线的监测资料进行综合分析论证，其结果表明：１９９６年２月滇西丽江７．０级地震前，该区应力场方向自１９９０年以来产生了由北东－北西的转变，震源区出现高达１０９ｍｍ的垂直差异运动１９９３－１９９６年有７５％的断层形变测点相继出现不同程度的中期、短期和临震异常。大地垂直形变与断层形变的共性演变特性，为强震的预测提供了一个较好的震例。     

不同前处理方法和测量时间对泥炭中无机矿物颗粒粒度的影响

用常规方法分析长江口泥炭全样粒度数据时,发现粒度频率分布曲线末端出现含量很低的一个或两个次主峰的奇怪现象.不同的前处理方法对泥炭粒度的测量结果表明:粒度数据应选取样品上机后第3 min内的测量结果;由于长江口泥炭样品中碳酸盐含量很少,粒度分析采用加H2O2加热去除有机质再加分散剂经超声波处理后的前处理方法测量即可.频率...