射阳闸闸下感潮河段电厂码头和取水工程淤积研究

以射阳港电厂三期扩建码头和取水口工程为例,建立射阳闸至河口段物理模型进行水流泥沙试验。试验研究表明,丁坝群并辅以开挖槽方案实施后,电厂河段减淤效果较好,三期取水口前沿河床平均高程从-4.41 m降低到-4.76 m,煤码头前沿港池的平均高程从-3.77 m降低到-4.49 m左右,能满足设计部门的要求。     

俯冲带流体——来自（超）高压变质岩石的证据

俯冲带是地球上岩浆活动、高压—超高压变质作用、中深源地震、壳幔物质交换、元素循环和铜金大规模成矿的集中发生场所。富水流体不仅调控了这些地质作用,而且也深刻影响了全球C、S等挥发分的循环。当蚀变大洋岩石圈及上覆沉积物进入俯冲带中深部（15~300 km）,伴随由葡萄石- 绿纤石相至超高压榴辉岩相的递进变质作用,含水矿物在不同深度的分解造成流体释放为一连续过程。除极端高地温梯度环境之外,大多数俯冲带洋壳释放的流体为富水流体。但,俯冲带不同深度所产生的流体特征有明显差异。出露于全球造山带的高压—超高压变质地体保存了分凝体、脉体、水压致裂角砾岩等流体作用的有力证据,绿辉石、石榴子石、绿帘石等矿物中原生流体包裹体为流体的直接记录。在俯冲带中等深度（＜65 km）,流体是溶质含量很低的含卤化物水溶液,可含CO2-3、SO2-4、HS-等组分,其所含Si、Al、Ca、Mg、Fe、Na主量元素溶质相当于海水中固化物量的2~3倍,并具大离子亲石元素（LILE）、轻元素（B、Li）富集和高场强元素（HFSE）亏损的特点。当深度≥65 km时,流体转化为类似于超临界性质的溶液,含CH4、C2H6、H2S等挥发分,其所含主量元素溶质显著增加,微量元素除LILE等外,还负载相当量的HFSE和过渡族成矿元素。俯冲带65~100 km深度,为富水流体向超临界流体转变的区间。这种流体具有“亚超临界”性质,发生了氧化还原性质和元素溶解能力的渐变,并伴随硬柱石和角闪石的最终耗尽。高压变质岩石及相关脉体的O、Sr、Nd和金属同位素示踪研究表明俯冲带流体的源区多样,有蚀变基性洋壳、地幔橄榄岩和沉积物,并保存了海底热液蚀变作用的印迹。流体以脉冲方式沿网络状裂隙呈隧道式运移和传输,规模可达千米级,时间尺度在数月至数百年。目前通过高压—超高压变质岩的研究对俯冲带流体已经有了深入的了解,但展望未来,该领域依然有诸多争议和科学问题值得探索。     

从沉积速率和沉积物粒度看冰后期海侵以来珠江三角洲西江大鳌沙的形成

大鳌沙处于珠江三角洲西江河口的近口段,洪季西江河口的潮区界西线在其顶端附近。冰后期海侵以来,大鳌沙顶端的PRD05和中部的PRD04两个钻孔的沉积速率和沉积物粒度有着极大的差别。7 630 a BP以前,PRD04孔的沉积速率小于PRD05孔的沉积速率;但7 630 a BP以来,PRD04孔的沉积速率大于PRD05孔的沉积速率。从粒度分析看,埋深12.28 m以下,PRD04孔沉积物比PRD05孔的粗,分选性比PRD05孔的差;埋深12.28 m以上,PRD04孔的沉积物比PRD05孔的细,分选性相对比PRD05孔的好。分析表明,9 000～4 200a BP, 沉积物由南（中部）向北（顶部）输运,涨潮流冲蚀老沉积物,在喷出磨刀门通道后,流速迅速降低,粗颗粒泥沙先沉积,而细颗粒泥沙被涨潮流搬运至更北的区域沉积。近3500年以来,河流动力占优势,沉积物由北（顶部）向南（中部）输运。大鳌沙的形成与涨潮射流密切相关。涨潮射流口的位置在蛇地山和右岸纵向山地之间,宽约2200 m。     

机器学习方法在预测泉水潜在出露位置中的应用

泉水出露受到多种因素影响,在传统地质勘查手段之外,各种模型方法及影响因子预测手段,也被越来越多地应用于泉水的研究中.本文尝试利用机器学习的方法进行泉水出露位置的预测研究.根据北京市野外调查,确定了1378个测试样本点,选取了高程、坡度、坡向、地形湿度指数、径流强度指数、距河流距离、距断裂距离、岩性、归一化植被指数及土地...     

The magnetic susceptibility measurements of turbidity current sediments from Fuxian Lake of Yunnan Province and their correlations with earthquakes

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前陆盆地研究进展综述

简要评述了经典的前陆盆地以及前陆盆地系统和复合式前陆盆地概念的定义和基本特征。介绍了有关前陆盆地的沉积作用、动力学机制以及造山带的构造作用与盆地的沉积响应等方面研究中所取得的最新进展。最后指出了在对前陆盆地的研究中应注意的有关问题。     

中国雄黄雌黄矿床类型、成矿机制与找矿方向

中国雄黄雌黄矿床,根据成矿作用、地质产状、成矿地质构造环境和地球化学特点分为三大类七种类型。即层控型（下关式单一雄黄矿床,水落式多金属雄黄矿床）、热水沉积型（界牌峪式雄黄雌黄矿床,松潘式金砷矿床）、热液型（石峡式辰砂、雌黄矿床,王庄式辉锑矿雄黄矿床和宁陕式金、钨、铊、雄黄矿床）。三大类矿床成矿机制各异。应在适宜的地层、大地构造区域,根据有利的矿物、地球化学等信息寻找该类矿床。     

湘西南金矿床成矿物质来源的地球化学证据

雪峰古陆是华南重要的金成矿区带之,该区金矿的成矿物质来源一直是一个颇有争议的问题。在野外系统调研的基础上,笔者从稀土元素,微量元素以及同位素（Ｐｂ,Ｓ,Ｓｒ）地球化学角度,探讨了湘西南金矿床的成矿物质来源。研究表明,该区金矿的成矿物质主要来自矿围岩而非中基性脉岩,即赋矿地层为金的矿源层。     

Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping

This paper proposes a new approach of weights of evidence method based on fuzzy sets and fuzzy probabilities for mineral potential mapping. It can be considered as a generalization of the ordinary weights of evidence method, which is based on binary or ternary patterns of evidence and has been used in conjunction with geographic information systems for mineral potential mapping during the past few years. In the newly proposed method, instead of separating evidence into binary or ternary form, fuzzy sets containing more subjective genetic elements are created; fuzzy probabilities are defined to construct a model for calculating the posterior probability of a unit area containing mineral deposits on the basis of the fuzzy evidence for the unit area. The method can be treated as a hybrid method, which allows objective or subjective definition of a fuzzy membership function of evidence augmented by objective definition of fuzzy or conditional probabilities. Posterior probabilities calculated by this method would depend on existing data in a totally data-driven approach method, but depend partly on expert's knowledge when the hybrid method is used. A case study for demonstration purposes consists of application of the method to gold deposits in Meguma Terrane, Nova Scotia, Canada.     

A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of –1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.