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101.
幸福河是中国新时代江河治理的新目标,对保障河流健康及经济社会可持续发展意义重大。为定量评价幸福河状况,本文通过对幸福河概念内涵的进一步梳理,提出幸福河评价体系;以安全运行、持续供给、生态健康、和谐发展"四大判断准则"为框架,按"目标-准则-指标"三层级,构建幸福河评价指标体系,包括基本指标16个、备选指标34个;参考相关规范、标准文件和研究成果,将幸福河评价指标划分为5个等级,分别给出各指标5个等级的分级标准值;引入"幸福河指数"来定量评价河流幸福河状态,采用"单指标量化-多指标综合-多准则集成"方法,定量计算幸福河指数。最后,以黄河为例,分别对2017年黄河上中下游分段、支流渭河以及流经的9个省区开展幸福河评价的实例应用。经验证,所提出的幸福河评价体系能较好地反映黄河客观实际,具有较强的可靠性和适用性。  相似文献   
102.
基于野外地质露头观察、岩心描述、薄片鉴定和地球化学等分析测试资料的综合分析,对鄂尔多斯盆地及周缘地区上二叠统石千峰组沉积岩石类型、沉积构造、沉积相类型及沉积体系空间展布等方面开展研究。结果表明,石千峰组主要发育冲积平原、三角洲平原、三角洲前缘、浅湖和南部海陆过渡的潟湖、沙坝等沉积环境,石千峰期,古气候干燥炎热,沉积古地形相对平缓,物源供给充足,形成了一套紫红色、棕红色泥岩和浅灰色中粗粒长石岩屑砂岩、岩屑长石砂岩、长石砂岩为主的地层。盆地北部三角洲规模大,以辫状河道发育沉积特征为主,砂体厚度大、横向分布稳定,而盆地南部三角洲规模相对较小。石千峰期,海水可能多次入侵盆地南部麟游-韩城-乡宁一带,发育海相夹层和沙坝沉积,一定程度上影响了南部的沉积环境和砂体展布。  相似文献   
103.
104.
为了明确辽河西部凹陷曙北地区沙河街组四段薄砂层成因类型及分布规律,从岩心、测井响应和地震反射特征等方面总结了各类成因薄砂层的识别标志,并预测了各砂层组的砂体展布,探讨了砂体发育的控制因素,总结了沉积模式.研究结果表明,曙北地区沙四段为扇三角洲-湖泊的沉积体系,薄砂层发育水下分流河道、河口坝、低隆滩坝、沿岸浅滩4种成因类型,其中以水下分流河道和低隆滩坝为主.古地貌、古物源和古水深控制了各类砂体的展布.以曙光和兴隆台古潜山一线为界,南部为扇三角洲体系沉积区,主要发育前缘水下分流河道和河口坝;北部(包括低隆区)为无明显水流注入的半封闭湖湾区,主要发育低隆滩坝砂体和灰/云坪,在西侧沿岸局部地区还发育沿岸浅滩.储量区外大面积发育的低隆滩坝砂体,是下一步碎屑岩增储上产的潜在接替区域.  相似文献   
105.
赵一  李衍青  李军  刘鹏  蓝芙宁 《地球学报》2021,42(3):324-332
本文对滇东断陷盆地南洞岩溶地下水系统各地下河的水文动态特征进行了分析,推断了南洞岩溶地下水系统的结构特征.根据岩性构造、地下河发育以及补径排关系,将其划分为四个子系统.分别采用降雨入渗系数法和径流模数法对南洞岩溶地下水系统的天然资源量进行计算,计算结果分别为35610.7万m3/a和33460.2万m3/a.用枯季径流模数法对南洞岩溶地下水系统的可采资源量进行了计算,计算结果为23407.3万m3/a,其可开采资源量巨大.南洞地下河在没有天然补给量的情况下,120天消耗的调蓄量为4503.3万m3,南洞地下河日允许开采资源量为49.4万m3/d.二号暗河建库蓄水条件下库区上游的日允许开采量为75.9万m3/d,蓄水库容来源于工程设计,资源保证程度高.本次研究可为南洞岩溶地下水资源的开发利用和调配提供科学依据.  相似文献   
106.
埕岛低凸起东部南区新生代受控于伸展与走滑作用,断裂构造复杂,传统认为中深层北东向与近东西向断层属于同期同沉积断层。针对这一观点及引起的问题,利用钻井和地震资料,运用构造地质理论解析断裂系统。研究区主要发育正断层、走滑正断层两种类型,断开层位有基底—东营组、平原组—东营组、平原组—基底三种情况,现今断裂以东营组与馆陶组之间的区域不整合面为时限划分为两期断裂系统。早期断裂主要切开基底—东下段,属于同沉积断层;晚期断裂主要切开平原组—东营组,可断达基底,其发育受早期断裂制约。北东向与近东西向断层分别属于早晚两期断裂系统,对油气分布各起关键性控制作用:先期基底升降引起的伸展作用形成北东向断层,控制洼槽地貌与深水重力流沉积环境,发育了连片的层状砂质碎屑流;后期郯庐断裂右行走滑派生了近东西向雁列断层,断层面既充当储层上倾方向的遮挡条件,又在东西向挤压时封闭性变差而变成油气垂向运移通道,断层及断层作用控制了圈闭分布与油气聚集的有序性,自东向西,圈闭及油水界面依次升高且充满度变小,呈全充满、欠充满、半充满等状态。断裂系统研究将地质体置于一定的构造应力场中,分析断层组合的空间排列和交切关系以及断层的力学机制和位移特征等,探究时空演化对油气分布的控制作用。断裂系统研究方法在构造作用叠合区具有适宜性,对该区及类似地区的勘探开发具有现实意义。  相似文献   
107.
本文采用对比分析与归纳分析法,在明确全民所有自然资源资产管理考核评价机制定位的基础上,充分借鉴和参考现有相关考核评价制度的设计思路,围绕“考核谁、谁来考、怎么考、考什么、结果怎么定、结果怎么用”等关键问题,从考核评价对象和实施主体、考核评价方式和实施、考核评价结果应用、考核评价内容和指标体系构建、考核评价结果评定方法等方面,提出构建全民所有自然资源资产管理考核评价机制和方法的思路,为推进生态文明建设和自然资源资产产权制度改革提供支撑。  相似文献   
108.
As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   
109.
The early Paleozoic tectonic evolution of the Xing'an-Mongolian Orogenic Belt is dominated by two oceanic basins on the northwestern and southeastern sides of the Xing'an Block,i.e.,the Xinlin-Xiguitu Ocean and the Nenjiang Ocean.However,the early development of the Nenjiang Ocean remains unclear.Here,we present zircon U-Pb geochronology and whole-rock elemental and Sr-Nd isotopic data on the gabbros in the Xinglong area together with andesitic tuffs and basalts in the Duobaoshan area.LA-ICP-MS zircon U-Pb dating of gabbros and andesitic tuffs yielded crystallization ages of 443-436 Ma and 452-451 Ma,respectively.The Early Silurian Xinglong gabbros show calc-alkaline and E-MORB affinities but they are enriched in LILEs,and depleted in HFSEs,with relatively low U/Th ratios of 0.18-0.36 andεNd(t)values of-1.6 to+0.5.These geochemical features suggest that the gabbros might originate from a mantle wedge modified by pelagic sediment-derived melts,consistent with a back-arc basin setting.By contrast,the andesitic tuffs are characterized by high MgO(>5 wt.%),Cr(138-200 ppm),and Ni(65-110 ppm)contents,and can be termed as high-Mg andesites.Their low Sr/Y ratios of 15.98-17.15 and U/Th values of 0.24-0.25 and moderate(La/Sm)_n values of 3.07-3.26 are similar to those from the Setouchi Volcanic Belt(SW Japan),and are thought to be derived from partial melting of subducted sediments,and subsequent melt-mantle interaction.The Duobaoshan basalts have high Nb(8.44-10.30 ppm)and TiO2 contents(1.17-1.60 wt.%),typical of Nb-enriched basalts.They are slightly younger than regional adakitic rocks and have positiveεNd(t)values of+5.2 to+5.7 and are interpreted to be generated by partial melting of a depleted mantle source metasomatized by earlier adakitic melts.Synthesized with coeval arc-related igneous rocks from the southeastern Xing'an Block,we propose that the Duobaoshan high-Mg andesitic tuffs and Nbenriched basalts are parts of the Late Ordovician and Silurian Sonid Zuoqi-Duobaoshan arc belt,and they were formed by the northwestern subduction of the Nenjiang Ocean.Such a subduction beneath the integrated Xing'an-Erguna Block also gave rise to the East Ujimqin-Xinglong igneous belt in a continental back-arc basin setting.Our new data support an early Paleozoic arc-back-arc model in the northern Great Xing'an Range.  相似文献   
110.
In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献   
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