Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.     

基于BP神经网络的底水油藏控水压裂选段新方法

随着水平井压裂技术的普遍应用,压裂段的选择成为影响增产效果的主要因素之一,但针对底水油气藏,在压裂时同时考虑控水同样重要。影响底水油气藏压裂效果的因素很多,因素之间具有高度不确定的非线性关系,给控水压裂选段分析带来了诸多不便。利用传统的单因素回归等方法,显然不能满足底水油藏多种影响因素下水平压裂段的选择。针对此问题,通过油藏工程理论和现场经验,确定了避水高度、地层各向异性、天然裂缝发育程度、地应力差值、水平井曲直偏差、储层与水层之间隔层发育程度六个参数作为影响控水压裂段选择的主要因素,建立定性分析方法。通过底水脊进模型确定了参数界限,结合BP神经网络系统的原理,建立定量分析。利用计算机语言编制底水油气藏水平井压裂段控水选择软件,通过实际应用,表明其对现场施工具有一定的指导作用。     

面向户外增强现实的地理实体目标检测

本文研究了多种计算机视觉领域中用于图像分类和目标检测的卷积神经网络模型,在一种目标检测模型的基础上进行了修改,并结合了残差神经网络结构,旨在解决户外增强现实中地理实体目标检测的问题。我们根据这一模型开发了一款户外增强现实原型系统,通过将训练好的模型作为目标检测引擎,达到了较好的近实时性和准确性。     

基于神经网络的时间域直升机电磁数据电导率深度成像

采用G-S变换以及高斯数值积分法,形成了时间域直升机的航空电磁响应正演样本集,分析了飞机测量过程中吊舱高度变化对电磁响应的影响,并将吊舱高度的变化等效成电导率为零的假层厚度的变化,以去除高度计等的影响.以假层半空间模型为基础,研究了基于人工神经网络的电导率深度成像算法,通过分析两个三层模型的电导率深度成像结果得出,神经网络方法计算时间域航空电磁探测的视电导率精度较高,特别是对高阻层的视电导率计算.     

基于位移强度概念分析大坝变形规律

为改变以单个监测点为研究对象分析大坝变形规律的方法,提出位移强度概念,并以此作为分类标准,借鉴决策树算法,根据大坝所有监测点的坐标建立模型,计算并分析大坝在某一方向上的整体变形规律。     

基于广义回归神经网络的GPS高程转换

为提高GPS高程转换的精度,采用广义回归神经网络(GRNN)进行拟合。将控制点的X、Y坐标作为网络输入,高程异常作为网络输出,采用实验数据训练网络,训练完成的网络作为模型进行高程异常预测。结果表明,GRNN方法具有较高的GPS转换精度。     

利用神经网络建立GPS网络RTK的双差对流层误差模型

为减小对流层误差改正数中系统偏差的影响以提高对流层改正精度,提出了基于神经网络的顾及空间的对流层误差建模模型,该模型的对流层延迟误差改正在网内外精度均达5 cm。     

ORPOM model for optimum distribution of tree ring sampling based on the climate observation network

Tree ring dating plays an important role in obtaining past climate information.The fundamental study of obtaining tree ring samples in typical climate regions is particularly essential.The optimum distribution of tree ring sampling sites based on climate information from the Climate Observation Network(ORPOM model) is presented in this article.In this setup,the tree rings in a typical region are used for surface representation,by applying excellent correlation with the climate information as the main principle.Taking the Horqin Sandy Land in the cold and arid region of China as an example,the optimum distribution range of the tree ring sampling sites was obtained through the application of the ORPOM model,which is considered a reasonably practical scheme.     

白鹤滩水电站初始地应力场研究分析

金沙江下游拟建的白鹤滩水电站上接乌东德梯级,下邻溪洛渡梯级,是继三峡和溪洛渡之后又一座千万千瓦级的水电站。白鹤滩水电站采用地下式厂房,其厂房的高度和跨度分别为78.5 m和32 m。为了保证地下洞室群围岩的稳定性,需要准确掌握初始地应力场的方向和大小。依据地质构造作用和现场实测方法分析发现,白鹤滩水电站左、右岸的初始地应力场,由于受到河流侵蚀和岸坡卸荷等因素影响,两岸厂区的地应力场方向在局部产生一定的偏转,从该地区的NWW方向变为NE方向,通过数值反演方法对计算区域的边界条件进行回归,与实测值比较确定回归边界条件的合理性和准确性。