Geochemical characteristics and genetic type of a lithium ore (mineralized) body in the central Yunnan Province,China

Lithium ore (mineralized) bodies in the area A of central Yunnan Province belong to a sedimentary-type, which are controlled by stratum. The studied ore (mineralized) body mainly occurs in the Middle Permian Liangshan Formation. This work described the morphology, structures, main ore types and geochemical characteristics of this ore body in detail, and discussed the ore-forming material source, occurrence state of lithium and the formation mechanism of lithium ores to clarify the prospecting marks. In the further exploration, comprehensive evaluation of the lithium resources of known bauxite ore bodies in central Yunnan Province should be strengthened, and the exploration of hidden lithium ore bodies should be intensified in order to discover more large and super-large lithium orebodies, which will fill the gap of the national demand for lithium resources, and promote the national defense construction and new energy industry development.     

格里戈良分带指数法在小龙钨矿区深部地球化学找矿预测中的应用

运用格里戈良分带指数法研究确定了小龙钨矿原生是晕轴向分带序列，该分带序列准确地反了矿床元素垂向变化规律，对指导本区深部铜、银、钨地球化学找矿预测评价和坑道工程署有重要意义。     

浅谈吸附烃找油气方法中的干扰因素

吸附烃是油气化探的主要指标之一，它直接反映从油气藏运移出来的烃类气体。应用吸附烃法找地下油气藏应排除地层、地形、地貌景观的干扰异常。不同地层上方土壤中吸附烃含量变化可能与地层沉积时间、沉积环境有关；不同地形、地貌景观下土壤中吸附烃含量异则是由于地化环境（ｐＨ值等）所致。消除这些干扰因素的最简单方法是分区统计背景值，或者用不同异常下限或用衬度值圈定异常。经校正后的异常能更真实可靠地反映油气藏，已在四     

甘肃省贵金属矿和铂族元素地球化学勘查的发展及新案例

由于Ａｕ、Ｐｔ、Ｐｄ等元素分析测试技术的突破，使中国的贵金属地球化学勘查得到迅速发展。甘肃省自８０年代以来，金矿地球化学勘查取得了丰硕的成果。９０年代初，在研究改进Ｐｔ、Ｐｄ、Ａｕ化学－光谱法快速联测分析方法的基础上，在祁连山地区开展了针对金和铂族元素的区域地球化学勘查。工作结果，Ｐｔ、Ｐｄ、Ａｕ的报出率都达到了１００％，从而可靠地圈定了Ｐｔ、Ｐｄ、Ａｕ的地球化学异常。这一案例证明，地球化学勘查在寻找金和铂族元素矿产方面都是有效的     

地壳抬升对流体化学场特征的影响

地壳抬升作为一种重要的地质现象，可使地层中的流体地球化学特征发生根本性的改变，可使地表水、游离水和细菌直接作用于油气藏，使之遭到水洗、氧化和菌解破坏，因而，地层流体的相关组分会受到改造，生物标志化合物的结构也会发生改变，最后，影响流体运移的方向及通道。本文就地壳抬升对流体化学场的影响进行论述，并提出有关依据。     

浅论湖南省银异常分布特征和找矿方向

湖南省银矿床银主要来源于岩浆源,已发现的银矿床主要有三类:矽卡岩型银铅锌矿床,脉状热液型银铜铅锌矿床,斑岩型银多金属矿床。银矿的分布受大地构造的控制:属扬子准地台上扬子台褶带的湘西北区无岩浆侵入活动,无银矿点分布,也末发现银异常;属扬子准地台江南台隆的雪峰古陆过渡区,银异常密集分布,强度中等,系沉积型含银的钒磷矿分布区,尚未发现工业银矿床,属华南褶皱系赣湘桂粵褶皱带的湘中、湘南、湘东区,银异常呈点状分布,强度大,是主要岩浆源型银矿分布区,是银矿找矿的重点地区。     

岳阳市环境地球化学影响及其综合防治

1:20万蒲圻幅化探水系沉积物测量,发现岳阳市东北区的F、As、Hg、Pb、Mo、U、Cu、Be、Sb等9种元素的过高或过低含量,有碍于人体健康,容易诱发相应的地球化学疾病;该环境中的B、Zn及Mo、Cu、As等5种元素,以及Cu/Mo比值的过高或过低,均有碍农业和林业的发展,会出现某些植物病虫害。因此,本文研究了该区的地球化学特征,划分出6个有碍人体健康、5个有碍农业与林业发展的综合预测区,并提出了防治措施与建议。     

应用荧光光谱特征作受害植物判别指标的研究

本文讨论了几种植物在正常、受害及衰老状况下的荧光光谱特征及其变化。结果表明,叶绿素两个荧光峰强度的消长与植物受害程度有着内在的联系,利用这种关系可预测植物长势和进行植物生理状态的研究。     

An improved classification of mineralized zones using particle swarm optimization: A case study from Dagh-Dali ZnPb (±Au) prospect,Northwest Iran

Classification of mineralized areas into different geochemical classes in terms of prospectivity is crucial in the optimal management of exploration risk and costs. Machine learning (ML) algorithms can be served as appropriate alternatives for separating ore-related anomalies due to avoiding the assumptions of statistical distribution and compatibility with the multivariate nature of geochemical features. By hybridizing the ML with a metaheuristic algorithm called particle swarm optimization (PSO), this contribution aims to provide an innovative approach to optimize the classification of geochemical anomalies within the study area. The algorithm, PSO, is inspired by simulating the social behavior of flocks of birds in search of food. The Dagh-Dali ZnPb (±Au) mineral prospect in northwest Iran was subjected as a case study to examine the integrity of the proposed method. Mineralization-related features were extracted by applying principal component analysis (PCA) on metallogenic elements analyzed in soil samples as PC1 and PC2 with elemental assemblages of AgAuPbZn and PbZn, respectively. The silhouette index was employed to estimate the number of underlying geochemical clusters within the adopted feature space. To constitute a comparative analysis, two k-means clustering and PSO-based learning (PSO-L) algorithms were implemented to classify the gridded data of PC1 and PC2 within the study area. The results indicated that the use of PSO has improved the cost function of the clustering problem (up to 4%). Adapting the mineralization classes with the metallogenic evidence demonstrated by boreholes drilled in the study area indicated that PSO-L was superior to the traditional k-means method, improving the accurate estimation of subsurface mineralization classes by 7%. By overcoming the drawbacks of conventional methods for trapping at the local optima, PSO-based learning possesses the potential to highlight weak mineralization signals that are numerically located in boundary conditions. The results show that the proposed approach can serve as an effective medium for optimal modeling of geochemical classes and management of detailed exploration operations.