初至层析静校正在复杂山地三维地震勘探中的应用

随着地形高差增大、地貌单元多变、近地表模型复杂,目前被广泛应用的初至折射静校正的精度已无法满足精细勘探的要求。初至层析静校正技术,由给定的初始模型进行正演,用射线追踪方法得到初始模型的初至波,利用该初至波和实际拾取的初至波进行比较,计算地表模型的修正量,反复迭代求得准确的地表模型。山西省国阳新能股份有限公司二矿390水平九采扩区地表标高940～1100m,地貌单元复杂,在对该区资料处理时,利用初至层析折射静校正,经9次迭代计算后,真实地刻划出近地表模型。在二种静校正技术对比中,初至折射静校正不但近地表模型精度低于层析折射静校正,而且其叠加剖面精细程度也远低于层析静校正,如在初至折射静校正叠加剖面同相轴上呈现的凹凸形态,在层析静校正叠加剖面并无显示,且后者剖面的信噪比也比前者明显提高。     

基于本体的地理空间信息集成框架与实现

论述了地理本体的基本概念、结构,介绍了地理本体的建模方法,提出了基于地理本体的空间数据集成框架,包括构建地理本体、数据预处理、概念映射、数据映射和数据抽取与转换5个步骤;设计了基于本体的空间数据集成方案,以数字地形图数据、海图数据向标准本体的集成为例对方案进行验证试验,并取得了初步成果。     

基于空间方向相似性的面状矢量空间数据匹配算法

数据一致性匹配处理技术是空间数据融合中的关键技术.采用方向关系矩阵模型进行空间方向关系描述,讲座了一般情况下的这间方向相似性计算,提出了基于空间方向相似性的矢量数据匹配算法流程.最后,以面状目标为研究对象,以某地局部地区居民地数据为例,对基于空间方向相似性的矢量数据匹配算法进行了试验与分析.     

城市三维可视化快速建模与浏览系统的研究

随着信息时代的到来,虚拟现实做为信息技术发展重要驱动力越来越多地应用到城市建设领域中来。本文分析了虚拟现实技术如何在城市尺度上进行快速建模和浏览,并通过具体案例对该技术进行了详细的阐述。     

上海市长江口及邻近海域地质调查现状及展望

海洋区域地质调查近年来逐渐得到重视,海洋地质调查技术的发展也为开展海洋区域地质调查奠定了基础。上海地区社会经济发展对地质工作的需求,为摸清上海市长江口及邻近海域的地质现状提出了必然要求。本文在系统整理长江口及邻近海域地质资料的基础上,重点分析了区内存在的重点地质问题和研究存在的问题,根据海洋地质调查的技术发展,结合国内相关地区的调查经验,对长江口及邻近海域内区域地质调查进行了展望,提出了采用综合物探技术、地质取样和测试分析进行综合地质环境调查的建议,并对具体采用的调查技术和调查内容进行了论述。     

地震勘探技术在深部找煤中的应用

成矿理论与勘探实践证明,东部一些老矿由于勘探深度、范围的限制以及认识上的局限,在矿区深部、周边仍有一些矿未能发现和查明,其中相当一部分仍具有找矿潜力;而寻找西部新的煤炭资源是国家“稳定东部发展西部”战略需要。针对中国东西部不同的地质条件和勘探目标,其地震勘探野外施工方法及资料处理原则有所差异;而资料解释在东部以查明地层赋存形态、煤层赋存范围、构造发育特征为主,西部则以寻找煤系地层波阻抗差异明显、连续性好的反射波组为解释重点。地震勘探技术在中国东西部的二个典型地震时间剖面佐证了其在深部找矿的应用效果。     

甘肃大泉水——白沙岘矿区含煤地层及煤层赋存特征

大泉水-白沙岘矿区位于甘肃省景泰县境内,在大地构造位置上处于北祁连加里东褶皱带东段,区内含煤地层为下石炭统靖远组和上石炭统羊虎沟组,属海陆交互相含煤沉积,根据岩性、岩相特征可划分为两个沉积旋回。该矿区主要可采煤层煤．层组赋存于羊虎沟组,厚1．71～9．00m,沿走向由西向东厚度变小,在大泉水井田可采厚度主要分布在V线以西,其东区段只是零星分布且不可采;在白沙岘井田煤层总厚度为2．99～5．15m,厚度变化小,属稳定煤层。煤：层组赋存于靖远组,仅分布在大泉水井田,厚1．33～8．88m,沿走向由西向东呈长透镜体状,西段和中段煤层发育较好．V线以东区段煤层变薄并出现无煤区。     

基坑潜水疏干的简化计算在昊华水泥厂中的应用

以流线、流面、汇点的概念为基础,对稳定流双井干扰和直线隔水边界附近涌水量理论公式进行对比分析,提出了二个虚拟界面,其中虚拟界面Ⅰ,运用流线、流面的性质,流线方程等给出证明;虚拟界面Ⅱ则通过半无限条形降落漏斗的分析,应用元流和总流的能量方程得到流量为零,流线为零的平面。在同样条件下,条形无限涌水量是半无限潜含水层涌水量的二倍。应用总流能量方程对三种情况水头损失的分析,解释了这种关系存在的合理性,得出虚拟界面Ⅱ,并以此得出该界面内的最大残余水头计算公式。将基坑降水运用虚拟界面简化为扇形,条形半无限含水层,从而实现单井预测,该方法应用到昊华水泥厂基坑降水中,预测效果理想。     

Using Sequential Self-Calibration Method to Identify Conductivity Distribution: Conditioning on Tracer Test data

An iterative inverse method, the sequential self-calibration method, is developed for mapping spatial distribution of a hydraulic conductivity field by conditioning on nonreactive tracer breakthrough curves. A streamline-based, semi-analytical simulator is adopted to simulate solute transport in a heterogeneous aquifer. The simulation is used as the forward modeling step. In this study, the hydraulic conductivity is assumed to be a deterministic or random variable. Within the framework of the streamline-based simulator, the efficient semi-analytical method is used to calculate sensitivity coefficients of the solute concentration with respect to the hydraulic conductivity variation. The calculated sensitivities account for spatial correlations between the solute concentration and parameters. The performance of the inverse method is assessed by two synthetic tracer tests conducted in an aquifer with a distinct spatial pattern of heterogeneity. The study results indicate that the developed iterative inverse method is able to identify and reproduce the large-scale heterogeneity pattern of the aquifer given appropriate observation wells in these synthetic cases.     

The Tau Model for Data Redundancy and Information Combination in Earth Sciences: Theory and Application

Many decision-making processes in the Earth sciences require the combination of multiple data originating from diverse sources. These data are often indirect and uncertain, and their combination would call for a probabilistic approach. These data are also partially redundant with each other or with all others taken jointly. This overlap in information arises due to a variety of reasons—because the data arises from the same geology, because they originate from the same location or the same measurement device, etc. The proposed tau model combines partially redundant data, each taking the form of a prior probability for the event being assessed to occur given that single datum. The parameters of that tau model measure the additional contribution brought by any single datum over that of all previously considered data; they are data sequence-dependent and also data value-dependent. Data redundancy depends on the sequence in which the data is considered and also on the data values themselves. However, for a given sequence, averaging the tau model parameters over all possible data values leads to exact analytical expressions and corresponding approximations and inference avenues. Information on multiple-point connectivity of permeability arrives from core data, well-test data and seismic data which are defined over varying supports with complex redundancy between these information sources. In order to compute these tau weights for determining connectivity, one needs a model of data redundancy, here expressed as a vectorial training image (Ti) constructed using a prior conceptual knowledge of geology and the physics of data measurement. From such a vectorial Ti, the tau weights can be computed exactly. Neglecting data redundancy leads to an over-compounding of individual data information and the possible risk of making extreme decisions.