内蒙古达里诺尔火山群火山地貌特征及火山岩岩石学特点

在内蒙古自治区锡林郭勒盟与赤峰交界处、达里湖的西北部有一群第四纪火山,数目达上百座,多数为中心式或裂隙-中心式喷发,形成相对高度在50~130m之间的火山锥,它们被称为"达里诺尔火山群",也是世界上海拔较高的火山群之一。因锥体形成后受风化、流水侵蚀等外力作用,多数火山锥已受剥蚀,但相对较新的火山还保留着完整的火山机构,包括火山渣锥、火山口、降落和溅落堆积物、塌陷坑、熔岩流、喷气锥等。达里诺尔火山锥体多由火山碎屑物组成,熔岩则流动迭加在早期形成的多级熔岩台地之上,分布总面积约3100km2,与其它火山岩共同组成了广阔的内蒙古高原。火山区整体呈四周低、中间阶梯状隆起的台地地形,按高程可分为四个阶梯,高度分别为1280m、1360m、1440m和1500m。熔岩台地的展布和火山锥的分布均呈东西走向的条带状,火山群的整体分布主要受东西向天山-阴山基底断裂和北东向大兴安岭-太行山断裂的双重影响,与大地构造关系密切。本群火山锥体虽然大小不一、形态各异,但按锥体形态可将火山锥分为截顶单锥形、复合型、盾形和残余型等四类,其剥蚀程度有明显差异,暗示了火山喷发活动时代的先后关系。锥形及复合型火山位于火山群中部,而盾形火山多沿台地边部分布,残余形火山则分布广泛,表明台地边部火山形成时间较早,晚期火山活动集中于台地中部,而更早期造台地的溢流式拉斑玄武岩喷发规模更大,形成本区基底。达里诺尔火山岩的岩性主要为碧玄岩和碱性玄武岩,少量为粗面玄武岩,虽然SiO_2变化范围较大,但全碱含量变化幅度相对较小,均属钾质系列的碱性火山岩范畴。火山岩斑晶数量少,在镜下为斑状结构,基质多为隐晶质,少量微晶质,明显不同于火山岩之下早期熔岩台地火山岩的基质显晶质结构。火山岩的微量元素呈LREE富集、HREE亏损的右倾型,显示了岩浆低度部分熔融的特征,并且本区与邻区火山岩可能有共同或相似的岩浆源。在岩性相近的基础上,结合火山锥体剥蚀和保留程度显示的火山活动相对时序,暗示本区岩浆成分从碱性玄武质和粗面玄武质逐渐演化为碧玄质,岩浆可能来自软流圈,且来源逐渐变深。     

基于四面体格网的三维地质体建模算法

构建真三维模型是地质学、三维GIS、科学可视化及工程应用领域共同关心的课题。提出了基于TEN的三维离散数据的构模,详细说明了从离散的三维初始数据点建Delaunay四面体格网的算法和步骤。算例表明,该算法能够较好地生成任意形状三维地质体。     

地下水污染监测网多目标优化设计模型及进化求解

采用模拟-优化方法建立了一个用于地下水污染监测网设计的多目标优化模型,该模型包括最小化监测费用、污染物质量评估误差、污染羽一阶矩评估误差和二阶矩评估误差等4个目标函数,以充分揭示减少地下水污染监测费用与提高污染监测精度之间的权衡关系.将改进小生境Pareto遗传算法与地下水流模拟程序和污染物运移模拟程序相耦合用于求解地下水污染监测网多目标设计模型.算例研究表明,采用进化算法求解监测网的多目标模型,能真实地反映各个目标函数间的权衡关系,并且不用考虑传统方法中惩罚因子的影响.与单目标优化模型相比,多目标优化模型可在较短的时间内得到优化问题的一系列Pareto权衡解,以利于相应条件下决策者选择最为经济有效的地下水污染监测方案.     

504铀矿床成因探讨

铀矿赋存于∈_(1q)(清虚洞组)、∈_(2(?))(石冷水组)中,与黑色蚀变关系密切。矿化受F_1,F_2断层控制。根据矿床地球化学特征和区域地层铀的平均丰度,表明504铀矿床是以∈_(1(?))(牛蹄塘组)为矿源层的中低温热卤水强烈改造层控矿床。铀矿的形成与∈_(1n)有机质热裂解向石油转化以及石油的运移、逸散全过程有关。     

Efficient seismic response data storage and transmission using ARX model‐based sensor data compression algorithm

This paper presents a linear predictor (LP)‐based lossless sensor data compression algorithm for efficient transmission, storage and retrieval of seismic data. Auto‐Regressive with eXogenous input (ARX) model is selected as the model structure of LP. Since earthquake ground motion is typically measured at the base of monitored structures, the ARX model parameters are calculated in a system identification framework using sensor network data and measured input signals. In this way, sensor data compression takes advantage of structural system information to maximize the sensor data compression performance. Numerical simulation results show that several factors including LP order, measurement noise, input and limited sensor number affect the performance of the proposed lossless sensor data compression algorithm concerned. Generally, the lossless data compression algorithm is capable of reducing the size of raw sensor data while causing no information loss in the sensor data. Copyright © 2005 John Wiley & Sons, Ltd.     

Distribution Prediction of Middle-deep Lacustrine Source Rocks in Eocene Wenchang Formation in the Kaiping Sag,Pearl Mouth Basin

Oil and gas shows are rich in drilling wells in Kaiping sag,however,large oilfield was still not found in this area.For a long time,it is thought that source rocks were developed in the middle-deep lacustrine facies in the Eocene Wenchang Formation,while there is no source rocks that in middle-deep lacustrine facies have been found in well.Thickness of Wenchang Formation is big and reservoirs with good properties could be found in this formation.Distribution and scale of source rock are significant for further direction of petroleum exploration.Distribution characterization of middle-deep lacustrine facies is the base for source rock research.Based on the sedimentary background,fault activity rate,seismic response features,and seismic attributes were analyzed.No limited classification method and multi-attributes neural network deep learning method were used for predicting of source rock distribution in Wenchang Formation.It is found that during the deposition of lower Wenchang Formation,activity rate of main fault controlling the sub sag sedimentation was bigger than 100 m/Ma,which formed development background for middle-deep lacustrine facies.Compared with the seismic response of middle-deep lacustrine source rocks developed in Zhu I depression,those in Kaiping sag are characterized in low frequency and good continuity.Through RGB frequency decomposition,areas with low frequency are main distribution parts for middle-deep lacustrine facies.Dominant frequency,instantaneous frequency,and coherency attributes of seismic could be used in no limited classification method for further identification of middle-deep lacustrine facies.Based on the limitation of geology knowledge,multi-attributes of seismic were analyzed through neural network deep learning method.Distribution of middle-deep lacustrine facies in the fourth member of Wenchang Formation is oriented from west to east and is the largest.Square of the middle-deep lacustrine facies in that member is 154 km²and the volume is 50 km³.Achievements could be bases for hydrocarbon accumulation study and for exploration target optimization in Kaiping sag.     

甘肃省地热资源赋存特征及潜力评价

甘肃省地热资源丰富,其地热资源的开发利用具有较大的潜力。本文介绍了甘肃省地热资源分布概况,结合地区构造、地热水化学特征、地热资源量及开发利用现状探讨了甘肃隆起山地对流型与沉积盆地传导型地热资源的赋存特征,并对两种类型地热资源潜力进行了分析评价。结果表明,沉积盆地地热资源分布于河西、陇西及陇东盆地等,热储类型为裂隙型和孔隙型,热储层自元古宙至新生代均有发育,岩性以砂岩为主;隆起山地型地热资源主要分布于祁连山和西秦岭造山带,热储类型为断裂破碎带,岩性以花岗岩为主;通过潜力计算得知,全省热量开采系数≥0.4的仅占29.17%,表明目前甘肃地热资源开采程度较低,开采潜力较大。     

基于小波变换与神经网络的石羊河流域夏季地温预测模型研究

地温变化在气候反馈效应中起着重要作用, 理解地温及其与影响因素之间的时空关系对预测全球温度变化至关重要。利用1998 - 2017年石羊河流域的逐日常规气象观测资料, 采用小波分析结合BP（Back Propagation）神经网络构建了石羊河流域夏季地温预报模型, 结果表明： 日平均地温预测效果在不同站点均为最佳, 其中预测值和观测值的相关系数均大于0.87, 3 ℃以内的预测概率均大于84%。其中, 民勤地区地温预测效果最好, 预测值和观测值的相关系数达到0.91, 3 ℃以内的预测概率达到86%。日最高地温的预测值与观测值的相关系数高于0.8, 但误差平方和、 标准差较大。永昌地区日最高地温的模拟效果最好, 3 ℃以内的预测概率达到83%。日最低地温的预测与观测值的平均相关系数高于0.66, 3 ℃以内的预报概率高于83%, 但预测值略低。其中, 武威地区日最低地温的预测效果最好, 预测值与观测值的相关系数为0.72, 3 ℃以内的预测概率达到94%。研究成果可为有效弥补干旱、 半干旱区地温观测资料缺失和探讨其与局地气候的关系提供一些参考。     

采用锚固和支撑与新型防护网SNS联合治理黄山风景区云汤线危岩崩塌

将传统的支撑锚固与新型SNS边坡防护技术应用于危岩的治理工程,不但解决了大块危岩治理问题,还解决了松动而无法清除危岩的崩落、掉块问题,且对自然景观破坏小,对旅游公路运营影响小,解决了场地狭窄与工程安全的矛盾,施工方便迅速,工程安全可靠。     

三维地质模型的建立——以阿尔及利亚Zarzaitine油田为例

对阿尔及利亚Zarzaitine油田储层岩芯进行观察与描述,在沉积特征综合分析的基础上,结合岩芯的分析化验资料、测井和区域地质背景资料,根据沉积微相和电相之间的对应关系研究,划分出了各小层沉积微相。从沉积微相的分布特点,分析了该区储层(F4)沉积微相的空间演化规律,进而建立了该油田储层(F4)的三维地质模型。