Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines

Weights of evidence (WofE) modeling usually is applied to map mineral potential in areas with large number of deposits/prospects. In this paper, WofE modeling is applied to a case study area measuring about 920 km² with 12 known porphyry copper prospects. A pixel size of 100 m × 100 m was used in the spatial data analyses to represent in a raster-based GIS lateral extents of prospects and of geological features considered as spatial evidence. Predictor maps were created based on (a) estimates of studentized values of positive spatial association between prospects and spatial evidence; (b) proportion of number of prospects in zones where spatial evidence is present; and (c) geological interpretations of positive spatial association between prospects and spatial evidence. Uncertainty because of missing geochemical evidence is shown to have an influence on tests of assumption of conditional independence (CI) among predictor maps with respect to prospects. For the final predictive model, assumption of CI is rejected based on omnibus test but is accepted based on a new omnibus test. The final predictive model, which delineates 30% of study area as zones with potential for porphyry copper, has 83% success rate and 73% prediction rate. The results demonstrate plausibility of WofE modeling of mineral potential in large areas with small number of mineral prospects.     

应用空间统计学理论解译遥感影像信息“缺失”区

借助图像处理软件ERDAS、地理信息系统软件ArcInfo以及空间统计分析软件ILWIS ,在对TM遥感影像进行分类的基础上 ,运用空间统计学理论以及Kriging插值技术 ,内插了影像真实信息“缺失”斑块的信息 ,插值结果通过了精度检验。为解译影像信息“缺失”区 ,提供了一种手段和方法。     

GNSS 监测数据预处理在变形预测中的应用

GNSS监测技术被广泛应用于变形监测工作中,但GNSS监测数据中会有缺失值、噪声等误差的存在,对预测结果造成影响。引入改进的小波神经网络模型进行变形预测,并考虑该模型的自适应性和容错性,分别采用三次样条插值法、小波滤波法和拉依达准则对原始监测数据进行缺失值填补、去噪和粗差剔除等预处理。并利用实际监测数据进行短期预报分析,对比原始监测数据和预处理后的监测数据的预测结果,结果表明预处理后的监测数据的预测效果更好。     

变形监测缺失数据序列灰色建模方法探讨

在测量工作中,由于气候环境、观测方法、观测仪器以及观测人员自身因素等多方面的原因,可能造成观测数据的丢失或者不完全。文中针对这类数据的处理,采用加权平均法和三次样条插值法对缺失数据进行修复,建立GM （1,1）模型,并与非等间隔预测模型进行对比。通过两组仿真数据和两组实测数据验证发现：对于呈指数增长的序列和高增长序列修复之后建模预测精度更高;三次样条插值法数据修复后GM （1,1）建模预测精度较加权平均法预测精度更高;对于低增长序列,直接采用非等间隔建模预测精度更高。     

Incremental Growth of Plains-Type (Compactional) Folds in a Cratonic Environment as Revealed by the Sedimentary Sequence

The enigma of the origin and development of plains-type folds, as they were christened in the early 20th Century, essentially has been solved. The folds, a considerable distance from the tectonic disturbance, were formed by draping of sediments over differentially displaced Precambrian basement fault blocks. These Precambrian basement fault blocks controlled the location, size, and shape of the folds. Forces were transmitted through the rigid basement causing readjustment along the indigenous fracture/fault pattern formed much earlier. In the U.S. Midcontinent, the crystalline basement is overlain by a thin veneer of sediments, and once the structures were formed, they continued to develop as evidenced by features in the overlying sediments. As the stress was transmitted through the basement and then relaxed, the fault blocks moved differentially in concert to these outside forces. Sediment compaction and nondeposition over structural topographic highs reacted accordingly to form the features as seen today. To determine the structural history, structural closure on different horizons on the anticline is plotted in their appropriate stratigraphic position at depth. This gives a compaction line for each tectonically coherent segment. Similar segments show a relatively straightline with offsets at major unconformities indicating breaks in the continuum. It is at these breaks that the section can be stretched until the compaction line matches as a continuum with the resulting gap giving the approximate amount of missing section for that part of the rock column. Conversely, the amount of closure on a structure at depth for each line segment can be estimated by extrapolating downward in that segment. This technique to determine depth of burial and thus the amount of missing stratigraphic section from well data at numerous locations has been compared with estimates made by other methods and the results are similar. Where no other data are available or for quick estimates, then, it is proposed that this approach will give reasonable results and that the values can be used as a constraint in basin modeling.     

A STATISTICAL APPROACH TO THE PROBLEM OF MISSING SPATIAL DATA USING A FIRST-ORDER MARKOV MODEL*

Geographical data sets sometimes contain missing observations that need to be estimated. A statistical approach to the problem is discussed for multivariate normal spatial data sets satisfying the first-order spatial Markov property with constant mean, where the information at neighboring or contiguous observed sites is used to estimate the missing values. The completed data are used to estimate the parameters of the distribution. The procedure is iterative. The approach is a special case of the Orchard and Woodbury missing information principle. The paper concludes with an illustrative empirical example using rainfall data from an area of Kansas and Nebraska. The quality of the estimates for different sites are compared.     

高轨无缝面阵扫描判断方法：栅格法和几何相交法

极轨海洋卫星的时间分辨率低,不能及时感知快速变化的海洋现象及海洋突发事件,因此,发展静止轨道海洋成像辐射计势在必行。静止轨道卫星可以长时间观测指定海域,但是视场小,需要通过二维指向镜等方式扩大卫星的观测区域。本文意在通过提出漏扫判断方法,给出合理的扫描方式,保证高轨面阵扫描无地理信息遗漏。首先,本文在分析二维指向镜成像原理的基础上,给出了指向镜子图像成像特性以及不同方向、大小的电机转角所引入的像面旋转。其次,画出了子图像的边界包络图,直观地给出了扫描角度变化与图像覆盖程度的关系。最后,提出了两种无缝拼接的判断方法:栅格法和几何相交法,栅格法能够判断出图像覆盖次数,几何法速度快、准确度高。实验结果表明应用本文的两种方法得到了不同扫描角度间隔下地球边缘有无漏扫的情况,找到基于本文光学系统、可覆盖全球且无缝拼接图像的最大俯仰、方位转角间隔,为电机转角步距的设置提供理论依据,保证不漏扫的同时提高载荷的时间分辨率。     

压缩感知技术在地震数据重建中的应用

地震资料室内处理过程要求野外采集的地震资料越多越好, 而地震数据远距离快速传输又要求野外地震数据量越少越好． 为解决这一矛盾, 将基于曲波变换与压缩感知的数据重建技术引入到地震资料处理中, 对实际的野外不完整数据进行压缩重建． 结果表明, 曲波变换相对于傅里叶变换在数据压缩采样方法中占有一定的优势． 但是, 在对实际资料进行处理时, 首先要对资料中的面波进行处理, 同时还要在一定曲波基元尺寸的情况下, 考虑缺失道数量的影响． 最终, 得到的重建数据图像纹理清晰、 连接自然, 从而验证了该方法的实用性和有效性．      

AR模型在大地电磁测深资料处理中的应用

大地电磁测深资料数据采集过程中,由于温度、湿度等对仪器的影响或GPS搜星不正常,采集到的数据有时会出现时间序列跳帧或缺失现象.针对这一问题,本文将基于无激励AR(p)模型预测数据的原理引入大地电磁测深数据处理中.根据已知序列确定AR(p)模型阶数以及模型参数,建立正确的预测模型对缺失数据进行预测,并对比经过预测后的数据与实际样本数据的频谱,表明AR(p)预测模型可以解决原始资料的不连续性问题,提高了大地电磁测深野外资料的利用率.