数值模拟在沂水铁矿区地下水环境影响评价中的应用

地下水数值模拟在矿区地下水环境评价中应用较少,通过对山东沂水铁矿矿区进行水文地质详细勘察,建立了尾矿库及矿区地下水水流及水质数学模型,预测和评价了该矿建设实施过程中对地下水环境可能造成的直接影响和间接危害。结论为矿山建设阶段和运行阶段正常情况下,尾矿区水位水质变化对地下水环境没有明显的影响,非正常情况或者事故状态下,预测污染因子在泄漏点附近一定范围出现超标现象;采矿区矿井排水对当地浅层地下水位影响较大。针对这种影响和危害提出防治措施,为沂水铁矿建设项目选址决策、工程设计和环境管理提供科学依据。     

不同种类纳米零价铁的毒性比较研究

纳米零价铁是一种高效的环境修复材料,可以处理多种污染物;然而,纳米粒子的尺寸效应可能导致其在自然界中存在潜在毒性风险。选择几种常用包覆型、负载型和裸露的纳米铁,通过大肠杆菌的耐受性实验,比较3种纳米铁的毒性。研究表明,负载型纳米铁的分散性最好,而裸露纳米铁最差。3种纳米铁虽然对大肠杆菌都表现出毒性,但是负载型纳米铁的毒性最小。通过毒性减缓的机理分析,说明纳米铁改性后阻止了纳米颗粒与细菌的直接接触,这是空间位阻效应的作用。研究结果进一步证实了在使用纳米材料前应充分评估潜在毒性和环境效应的重要性。     

辽宁本溪南芬铁矿地质特征

南芬铁矿床成因类型为太古宙鞍山式火山沉积变质铁矿床,矿床规模为超大型,矿体形态和组构较简单,矿石有用组分分布均匀.矿体由分布于太古宇鞍山群茨沟岩组中的3个铁矿层组成,控矿构造为单斜构造,矿体的规模、产状、矿物组成、品位（TFe）稳定.     

辽宁鞍本地区铁矿床地质特征及找矿标志分析

在区域成矿地质背景分析的基础上,对辽宁鞍山、本溪地区典型条带状铁矿床的矿床地质特征和找矿标志进行了分析总结,研究表明铁矿产于太古宙晚期花岗岩-绿岩带内,主要为条带状含铁建造型铁矿床,鞍山群茨沟岩组、樱桃园岩组是找矿主要层位.矿石具有条带状、块状构造,半自形等粒粒状变晶结构、残留结构等.含铁建造的演化趋势与火山作用密切相关.鞍本凹陷区以及高大磁异常、复杂磁异常、低缓磁异常、深大磁异常和剩余磁异常区是寻找大型、超大型铁矿的最有利部位.     

南坑河污染初步调查及河岸植物的富集能力

南坑河是新丰江的一条支流,直接汇入广东省最大的饮用水源地新丰江水库。野外调查中发现私自采矿和在河流沿岸洗矿等行为不断发生,大量洗矿废水未经处理直接注入南坑河,可能会造成污染。因此,采样初步分析了南坑河河水、河流底泥沉积物及附近优势植物的9种重金属元素含量。结果发现：与地表水II类标准相比,南坑河枯水期水体污染严重,其中连续2 a的铅、砷和汞超标,超标倍数分别为1.7~1.8、1.3~1.9和1.6~2.9倍,而底泥中铜、镉、铅、锌和砷均超标,超标倍数分别为4.8、9.0、3.9、4.3和10.9倍,镉和砷超标较为严重。另外,收集并鉴定了河岸或河漫滩中天然生长的15种植物,发现以蕨类植物和多年生禾草为主要类型。通过分析植物体内9种重金属元素含量,发现对砷吸附能力最强的当属蜈蚣草,其叶片中砷的含量（609.20 mg/kg）和茎干中砷的含量（814.77 mg/kg）均大于根部的砷含量（577.30 mg/kg）;蜈蚣草对铁、锰、铜和铅的吸附能力也远远大于其他河岸植物,可以作为良好地修复以砷为主的复合重金属污染土壤的富集植物。     

湖南道县铁锰矿区岩溶发育影响因素分析

湖南道县铁锰矿区岩溶发育主要受地层岩性、地形地貌、断裂构造等因素的控制,岩溶形态主要有溶洞、暗河、溶沟、溶槽、漏斗、落水洞和溶蚀洼地等,岩溶非均一性特征明显。在地层岩性上,以矿区CaO含量最高的上泥盆统佘田桥组和锡矿山组下段二至四层白云质灰岩、白云岩与锡矿山组下段第一层泥灰岩、泥质灰岩隔水层交界部位岩溶最为发育,佘田桥组见溶洞260个,占总溶洞数的96.7%。在平面上,岩溶以中部及北部地势平坦的覆盖岩溶区最为发育,见洞率54.8%,其次是岩石裸露的东部及南部低山丘陵区,矿区南部见洞率40.4%。在垂向上,由浅及深岩溶发育由强至弱,标高0~220m钻孔揭露溶洞237个,占溶洞总数的86.5%,-150~0m揭露溶洞28个,占溶洞总数的10.2%,-400m以下无溶洞及溶蚀现象。在地质构造上,断裂及其影响带岩溶较发育,断裂带见溶洞209个,占总见洞数的76.3%,且溶洞充填率较高。      

Importance of hydrogeological conditions during formation of the karstic bauxite deposits,Central Guizhou Province,Southwest China: A case study at Lindai deposit

Karstic bauxite deposits are widespread in Central Guizhou Province, SW China, and high-grade ores are frequently sandwiched with overlying coal and underlying iron-rich layers and form a special “coal–bauxite–iron” structure. The Lindai deposit, which is one of the most representative karstic bauxite deposits in Central Guizhou Province, was selected as a case study. Based on textural features and iron abundances, bauxite ores in the Lindai deposit are divided into three types of ores, i.e., clastic, compact, and high-iron. The bauxite ores primarily comprise diaspore, boehmite, kaolinite, illite, and hematite with minor quartz, smectite, pyrite, zircon, rutile, anatase, and feldspar. The Al₂O₃ (53–76.8 wt.%) is the main chemical contents of the bauxite ore samples in the Lindai district, followed by SiO₂, Fe₂O₃, TiO₂, CaO, MgO, S, and P etc. Our geological data on the Lindai deposit indicated that the ore-bearing rock series and its underlying stratum have similar rare earth elements distribution pattern and similar Y/Ho, Zr/Hf, and Eu/Eu¹ values; additionally, all ore-bearing rock samples are rich in MgO (range from 0.16 wt.% to 0.68 wt.%), and the plots of the dolomites and laterites lie almost on or close to the weathering line fit by the Al-bearing rocks in Zr vs. Hf and Nb vs. Ta diagrams; suggesting that the underlying Middle Cambrian Shilengshui Formation dolomite is the parent rock of bauxite resources in the Lindai district.Simulated weathering experiments on the modern laterite from the Shilengshui Formation dolomite in the Lindai bauxite deposit show that hydrogeological conditions are important for karstic bauxite formation: Si is most likely to migrate, its migration rate is several magnitudes higher than those of Al and Fe under natural conditions; the reducing inorganic acid condition is the most conducive to Al enrichment and Si removal; Fe does not migrate easily in groundwater, Al enrichment and Fe removal can occur only in acidic and reducing conditions with the presence of organic matter.The geological and experimental studies show that “coal–bauxite–iron” structure in Lindai deposit is formed under certain hydrogeological conditions, i.e., since lateritic bauxite or Al-rich laterite deposited upon the semi-closed karst depressions, Si can be continuously removed out under neutral/acidic groundwater conditions; the coal/carbonaceous rock overlying the bauxitic materials were easily oxidized to produce acidic (H₂S, H₂SO₄, etc.) and reductant groundwater with organic materials that percolated downward, resulting in enrichment of Al in underlying bauxite; it also reduced Fe³⁺ to its easily migrating form Fe²⁺, moving downward to near the basal carbonate culminated in precipitating of ferruginous (FeS₂, FeCO₃, etc.) strata of the “coal–bauxite–iron” structure. Thus, the bauxitic materials experienced Al enrichment and Si and Fe removal under above certain hydrogeological conditions forming the high-quality bauxite.     

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.     

甘肃省肃南县白尖铁矿地质特征及找矿方向

白尖铁矿位于镜铁山铁矿床外围,是北祁连西段铁多金属成矿带内重要的沉积变质型铁矿床之一。白尖铁矿矿体赋存于长城系桦树沟组中,共圈定铁矿体8条,矿体多呈层状产出,分布受控于含矿层位。矿体长度在86~424 m之间,平均厚度在2.24~26.21 m之间,TFe平均品位为22.55%~34.80%,矿石自然类型以菱铁矿矿石为主;矿石具细粒结构,条带状构造发育,品位低、变化大,具沉积变质型铁矿的特点。通过对白尖铁矿地层层序、成矿地质背景和矿床地质特征综合分析研究,初步提出了今后勘查中找矿方向以赋矿层位为主,这对镜铁山铁矿床外围此类铁矿床的勘查具有一定的参考意义。     

山东枣庄杨家堡子铁矿床地质特征及成因探讨

杨家堡子铁矿位于山东省枣庄市峄城区峨山镇,隶属苍峄铁成矿带,矿体赋存于新太古代泰山岩群山草峪组上部的变质岩中。该矿床内圈定磁铁矿体11个,求得铁矿石(333)资源量3 378.2万t,矿床平均品位TFe 33.30%,mFe 22.64%。主矿体为Ⅲ-6矿体,占矿床总资源量的83.69%,其长度1 137 m,最大斜深1 065 m,埋深707~1 369m,形态复杂程度中等,品位变化属均匀类型。含矿岩石为磁铁石英角闪岩。矿石中的主要有用组分为Fe,伴生元素含量较低,均无综合回收利用价值,有害组分S、P的含量均较低,经选矿后可达到炼钢及炼铁要求。矿石结构主要有粒状变晶结构和柱状变晶结构;矿石的构造主要为条带状构造,块状构造的矿石较少,但品位相对较高。矿石自然类型为原生磁铁矿石,工业类型属需选磁性铁矿石。该矿床成因与苍峄铁矿一脉相承,为典型的BIF型铁矿。根据区内以及邻区的见矿情况结合磁异常特征分析,杨家堡子矿区有良好的找矿前景。