覆盖区矿产综合预测思路与方法

围绕覆盖区矿产综合预测的基本思路和关键方法问题,讨论了非线性矿产预测理论和方法如何在覆盖区矿产综合预测中发挥作用,重点介绍了成矿奇异性理论与非线性矿产预测方法在覆盖区矿产预测“弱信息”提取、“复合叠加信息”分解、“缺失和不完整信息”融合等关键问题中的应用.结合正在实施的中国地质调查局“覆盖区矿产综合预测”计划项目,介绍了三方面的研究进展:(1)覆盖层中地球化学元素迁移机理与覆盖层对地表地球化学异常的屏蔽和衰减作用;(2)如何识别由于覆盖层影响而造成的“弱”且“复杂”的地球化学异常;(3)如何综合具有缺失或不完整的多元勘查信息以达到提高覆盖区矿产综合预测精度、降低预测不确定性的目的.以东天山戈壁沙漠覆盖区海相火山岩型铁矿、大兴安岭南部草原覆盖区铁多金属矿、武夷山层控矽卡岩型铁矿等矿床预测为例,介绍了综合预测方法的应用过程和应用效果.研究结果表明,奇异性理论和分析方法可以有效地用于提取水系沉积物地球化学和地球物理(重、磁)弱异常,合理分解复合叠加异常,在此基础上,采用地球化学和地球物理异常的数据融合技术,分别建立了推断铁镁质火山岩、中酸性侵入岩、矽卡岩和热液蚀变等成矿或控矿地质要素的综合信息模型,以及基于综合预测要素建立的覆盖区矿产预测后验概率证据权模型和模糊逻辑模型.应用结果显示,介绍的预测方法不仅可以在出露区圈定成矿远景区,而且在戈壁沙漠覆盖区及第三、第四系松散沉积物覆盖区等均可圈出具有重要资源潜力的远景区,这些远景区往往会被传统的预测方法所遗漏.      

黄土覆盖区地球化学数据采集及采样单元之间误差校正方法研究——以内蒙古敖汉旗为例

敖汉旗幅为赤峰地区的黄土覆盖区,在该区进行1：5万区域地球化学调查工作以水系采样为主,土壤为辅.为便于研究,水系粒级采用-10~+60目,土壤样品采用-10目粒级.因采样介质不同,所代表的化学意义不同,测试中产生系统误差.对不同采样介质的测试数据以Au元素为例分别进行统计,确定不同采样地区的背景值,并利用不同的系统误差校正方法对土壤采样区与水系采样区进行系统误差校正,以求在满足异常分析的同时更低程度遗漏厚黄土覆盖区低缓弱异常.3种校正方法在保证水系沉积物异常条件下,对黄土区异常都有一定程度弱化.标准化方法弱化的程度相对小,对刻划黄土区地球化学信息最接近,衬度变换与中位数变换校正后对黄土区地球化学信息反映弱.     

A catchment basin approach to the analysis of reconnaissance geochemical-geological data from Albay Province,Philippines

A systematic approach for identifying mineral exploration target areas from reconnaissance stream sediment data without sufficient a-priori control information has been demonstrated in a portion of western Albay Province in the southern Bicol Peninsula of the Philippines. The approach involved devising a rapid method of catchment basin mapping using a geographic information system (GIS) so that the areal influence of the catchment basins may be incorporated in the geochemical data analysis. Areal proportions of mapped rock units occurring in the sample catchment basins and observed Mn and Fe contents in stream sediments are used as independent variables in multiple regression analysis to predict element contents in stream sediments related to lithologic and chemical controls. The predicted element contents are filtered-out from the original data to leave residuals in which the effects of other factors (e.g., mineralization) may be seen. A simple correction for the effects of downstream dilution is applied; this allows for the different sizes of the sample catchment basins so that positive geochemical residuals are enhanced. The inter-relationship of the different positive residuals in ‘highly enriched’ samples are investigated through principal components analysis to determine and quantify an ‘anomalous geochemical signature’. Lastly, the ‘anomalous geochemical signature’ is integrated with ‘proximity’ to faults/fractures to determine favourable target areas. For the test region, the lithologic controls explain between 80% and 100% of the variability in most of the elements studied. Chemical controls account for generally less than 5% of the variability in the data. Most of the dilution-corrected residuals reveal high relative enrichment in certain areas underlain by andesite and/or diorite. An anomalous Cu-Mg-Fe-Zn geochemical signature is disclosed by the principal components analysis of the dilution-corrected residuals in ‘highly enriched’ samples. Most sample catchment basins defined by this ‘anomalous geochemical signature’ pertain to areas underlain by andesitic rocks. Integration of the ‘anomalous geochemical signature’ and ‘proximity’ to faults/fractures reveals that some of these anomalous sample catchment basins are favourable target areas. These areas are interpreted to contain andesite-hosted stockwork or stringer zones that once formed part of a complete stratigraphic sequence of a volcanogenic massive sulphide occurrence. The results demonstrate the usefulness and ability of the procedures followed to extract significant anomalies from the reconnaissance geochemical data without the benefit of sufficient a-priori control data to aid in anomaly recognition. Similar procedures could also be applied elsewhere.     

地质异常的奇异性度量与隐伏源致矿异常识别

以个旧锡多金属矿床为例,研究了致矿地质异常的非线性特征.从异常地质事件和成矿作用的奇异性出发,定量分析了地质异常的奇异性、不连续性、非平稳性、混沌性、自相似性、临界性等非线性特征.在此基础上,详细介绍了局部奇异性分析原理和方法,论述了奇异性指数对隐伏源异常的识别能力.结果表明,奇异性分析方法在个旧地区水系沉积物地球化学数据处理和隐伏源地球化学异常识别和圈定应用中是有效的.分析结果一定程度上消除了隐伏源深度的影响,所圈定的局部地球化学异常不仅在个旧东区较好地对应了已发现的大型锡矿床的分布,而且在其他低缓地球化学异常区也圈定了多处局部异常,为进一步开展锡矿勘查提供了重要靶区.奇异性理论和方法有望为深部矿产预测、隐伏矿预测、覆盖区矿产预测等特殊环境开展矿产预测提供了新的实用性理论和方法技术.      

地球化学异常识别的两种机器学习算法之比较

在Sklearn的Python语言代码基础上,开发了基于孤独森林和一类支持向量机的多元地球化学异常识别方法程序。选择吉林省和龙地区为实验区,从1∶5万水系沉积物资料中提取地球化学异常。把实验区已知矿点的空间分布位置作为"地真"数据,绘制两种机器学习算法的ROC曲线并计算AUC值,用来对比两种方法的多元地球化学异常识别效果。研究结果表明:两种机器学习算法都能够有效识别多元地球化学异常,所提取的多元地球化学异常与已知矿点具有显著的空间关联性;孤独森林算法在数据处理耗时和多元地球化学异常识别效果方面略优于一类支持向量机。     

Comparison of U-spatial statistics and C–A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district,NW Iran

The delineation of populations of stream sediment geochemical data is a crucial task in regional exploration surveys. In this contribution, uni-element stream sediment geochemical data of Cu, Au, Mo, and Bi have been subjected to two reliable anomaly-background separation methods, namely, the concentration-area (C–A) fractal and the U-spatial statistics methods to separate geochemical anomalies related to porphyry-type Cu mineralization in northwest Iran. The quantitative comparison of the delineated geochemical populations using the modified success-rate curves revealed the superiority of the U-spatial statistics method over the fractal model. Moreover, geochemical maps of investigated elements revealed strongly positive correlations between strong anomalies and Oligocene–Miocene intrusions in the study area. Therefore, follow-up exploration programs should focus on these areas.     

趋势面方法圈定龙关地区化探异常及应用效果评价

以河北省张家口龙关地区1∶20万水系沉积物数据为例,采用趋势面分析方法,用不同次数的多项式拟合地球化学元素的整体分布趋势,识别出局部异常,圈定元素异常分布图。在与传统平均方法对比研究后,界定Pb、Zn、Ag、Au、Cu的趋势面法应用效果,为相似区域提供参考。     

云南某地1:5万区调各种化探方法找矿经济效果对比

本文通过对云南西南部两个1:5万图幅地球化学水系沉积物测量、土壤测量和岩石测量三种区域化探方法的试验,从找矿、经济两个方面进行对比,得出了三种化探方法找矿效果相近的结论,但水系沉积物测量以其异常衬度高、野外采样工作量小、样品分析费用少而明显优于土壤测量。在试验中得到的另一发现是低密度(每平方公里约2个样)的岩石测量在发现异常信息和了解微量元素分布区域性变化规律方面有好的效果。岩石测量作为一种有效的区域化探方法值得重视和进一步研究。     

基于元素含量和空间变异系数的化探异常信息识别

化探异常信息识别是化探数据分析最重要的任务之一, 也是化探数据在资源勘查领域受到广泛关注的最重要原因, 前人对化探异常信息识别做过大量研究, 这些研究中的大多数主要关注化探示踪元素的含量, 近而根据含量指标计算异常阈值, 而对示踪元素在空间中的分布特征关注较少。本文选择 1: 20万比例尺的克拉玛依幅为研究区, 根据区内金矿的矿床地球化学特征选择Ag、As、Au和Sb等4种元素为本区内金矿的示踪元素, 以地球化学元素分散晕形成理论为依据, 使用GIS技术和Matlab软件绘制研究区内4种金矿示踪元素的综合地球化学异常图。结果表明, 与传统阈值方法得到的化探异常图相比, 本文得到的化探异常图能够更好地指示研究区内已知金矿。     

青海祁漫塔格地区1∶5万水系沉积物测量方法技术及应用成果

青海省祁漫塔格地区位于柴达木盆地西南缘,是青海东昆仑成矿带西部的重要组成部分。近10年来祁漫塔格地区完成20385km2的1∶5万水系沉积物测量工作,实现了祁漫塔格地区1∶5万水系沉积物测量工作的全面覆盖,提供了大批可供进一步开展工作的化探综合异常和找矿靶区,为祁漫塔格地区矿产资源评价工作提供了翔实的地球化学资料。经异常查证,区内已发现了夏日哈木、铜金山、拉陵高里河沟脑等多金属矿床及一批找矿信息,已证实具有小型-超大型规模,使祁漫塔格地区成为重要的矿产勘查地区。近年来祁漫塔格地区1∶5万水系沉积物测量工作进展及找矿成果表明,1∶5万水系沉积物测量工作对于重要成矿带上矿床的发现具有重要作用,有力推动了祁漫塔格地区矿产资源调查评价工作的开展。     

Towards a quantitative model of downstream dilution of point source geochemical anomalies

The quantitative relation between point source soil geochemical anomalies and their response in stream sediments is linked through the concept of productivity (area × concentration). The equation proposed by Hawkes in 1976 is shown to be a subset of a wider range of equations proposed by Russian workers. These workers introduced two coefficients to account for the contrasting behaviour of different elements and the position of the soil anomaly within the drainage catchment. The calculation of these coefficients requires a series of samples downstream from the ending of anomalous soil input. The concept of productivity has particular application where multiple samples and anomalies are present in catchments. Plotting of productivity allows the discrimination of anomalous areas not detectable from raw element concentrations. The usefulness of these techniques is demonstrated from a small detailed stream sediment survey in NW Scotland. Dispersion from a gossan shows deviation from the Hawkes equation with the result that detailed sampling is required to detect soil anomalies. Plotting productivity allows the detection at the base of major slopes of anomalies that are diluted by upslope sediment.     

赣西北地区地球化学图的地质应用

本文是通过赣西北地区重新扫面的水系沉积物样品,按四平方公里一个组合样进行二十一个元素和氧化物定量分析的1:50万地球化学图,运用于地质找矿方面的部分研究成果。着重阐述了异常初步评价、成矿远景区划、钒成矿区预测和地层地化分层,揭示了区域地球化学资料所反映的多方面的地化信息,提供了较多的基础地化资料、有关的特征标志和重要的找矿线索。     

水系沉积物测量在阿巴通德拉扎卡地区找矿应用

水系沉积物测量技术在马达加斯加应用较少,笔者通过在马达加斯加阿巴通德拉扎卡地区水系沉积物测量找矿勘查,探讨该方法在此类地球化学景观地区的应用效果。应用该方法,在马达加斯加阿巴通德拉扎卡地区圈定147处综合异常,通过野外1∶1万地质填图和地表工程揭露,发现钛铁、镍、金等矿产,这说明水系沉积物测量方法适用于马达加斯加中部高原地区的地球化学景观条件,圈定的异常准确、有效,并可取得较好的找矿效果。水系沉积物测量方法可在马达加斯加的同类地区推广使用。     

湘西茶庵铺地区地球化学特征及找矿方向

在1∶5万水系沉积物测量的基础上,初步总结了湘西茶庵铺地区的地球化学特征。通过元素含量特征、变异系数等分析探讨了元素的成矿可能性,通过相关分析得出了多种元素组合,进而圈定了19处综合地球化学异常。AS10异常区经初步查证发现了3条较大规模的金多金属矿体。通过成矿地质条件和区域成矿规律分析,确定了沃溪式金多金属矿和沉积型钼钒多金属矿为主要矿产预测类型,圈定了3处找矿远景区。结果表明:水系沉积物测量方法在湘西地区有较好的金多金属矿找矿效果。     

青海省纳日宗地区水系沉积物地球化学特征及找矿远景

纳日宗地区位于南祁连岩浆弧和宗务隆山—夏河—甘加裂谷的接触部位,该区及其周边已发现金属矿床(点)10余处,具有良好的金、铅、铜矿找矿前景。以该区1∶5万水系沉积物测量的16种元素含量数据为基础,统计分析了水系沉积物地球化学参数、元素相关性、成矿有利度系数等指标,总结了元素组合、富集贫化规律及其与地质背景、地质构造与成矿作用的关系。区内主要富集元素为Sn、Pb、Bi、As、Sb,成矿有利度系数高的元素有As、Pb、W、Au、Bi、Sb。结果显示,水系沉积物元素异常沿NW—NWW向断裂构造分布,初步认为构造发育地段具有较好的Au、Pb、W找矿潜力。依据单元素异常特征和成矿地质条件,圈定综合异常74处,划分出3个找矿远景区,指出了各找矿远景区的找矿方向。     

铜陵—池州地区水系沉积物测量地球化学特征及找矿方向

在长江中下游铁、铜、金多金属成矿带1∶20万水系沉积物测量的基础上,对铜陵—池州地区的地球化学异常特征进行初步总结。通过对区内元素进行因子分析,得出多种与成矿有关的异常组合,利用成矿元素异常组合划分地球化学分区,进而圈定地球化学异常。通过对比成矿地质背景和地球化学条件,对铜陵地区典型矿床进行相似度分析,预测池州地区的找矿远景区。结果表明:综合区域上的地层、构造、岩浆岩和区域矿产等资料,对成矿元素异常组合进行地球化学分区是可行的,能有效反映致矿异常,突出矿化信息;根据样点相似度值的计算结果与典型矿床相似度图的耦合情况分析,池州市南部的梅街镇、青阳县东侧、铜山铜矿外围是区域上最为有利的铜多金属找矿远景区。     

A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas

Fractal/multifractal modeling of geochemical data is an interesting topic in the field of applied geochemistry. Identification of weak anomalies for mineral exploration in covered areas is one of the most challenging tasks for utilization of geochemical data. In this study, three fractal models, consisting of the concentration–area (C–A), spectrum–area (S–A) and singularity index models were applied to identify geochemical anomalies in the covered area located in the Chaobuleng Fe polymetallic district, Inner Mongolia (China). The results show that (1) the grassland cover weakens the concentrations of geochemical elements; (2) the C–A model has a limitation to identify weak anomalies in covered areas; (3) the S–A model is a powerful tool to decompose mixed geochemical patterns into a geochemical anomaly map and a varied geochemical background map but suffers edge effects in an irregular shaped study area; and (4) the singularity index is a useful tool to identify weak geochemical anomalies.     

The true sample catchment basin approach in the analysis of stream sediment geochemical data

The Sample Catchment Basin Approach (SCBA) is one of the techniques widely employed in the analysis of stream sediment geochemical data and delineation of anomalous catchments. However, this method fails to take into account the real catchment basin boundaries of each sample by incorporating only the incremental area between two adjacent samples. In other words, the SCBA incorrectly assumes that the geochemistry of every sample catchment within a drainage is independent from upstream sample catchment(s) feeding into this drainage. The chemical composition of sediment at the basin outlet originates from the whole basin upstream and not the incremental area as postulated in the SCBA. Consequently, the calculated background values for various lithologies and the background value for the element of interest at the basin outlet might be far from reality.This study used a True Sample Catchment Basin Approach (TSCBA), that reflects the true catchment boundary of every stream sediment sample, and in which all calculations are carried out on the premise that this boundary and the true area affect the composition of each sample. The results obtained from the application of both the SCBA and TSCBA to a gold endowed study area in western Iran clearly illustrated the superiority of the TSCBA over the SCBA. In addition, this study demonstrated the advantage of using the modified dilution correction equation of Mokhtari and Garousi Nezhad (2015), as compared to the existing Hawkes’s equation commonly used for dilution correction of residual values.     

因子分析在水系沉积物测量地球化学分区中的应用探讨——以青海都兰地区为例

采用R型因子分析方法,对工作区水系沉积物测量所取得的数据进行了处理,提取具有代表性的6个因子,并制作了因子计量图,用以描述工作区中主要的元素组合类型,为地球化学综合异常类型的划分与圈定,提供了较好的依据。     

Separation of a geochemical anomaly from background by fractal and U-statistic methods,a case study: Khooni district,Central Iran

Separation of geochemical anomalies from background are one of the important steps in mineral exploration. The Khooni mineral district (Central Iran) has complex geochemical surface expression due to a complex geological background. This region was chosen as a study area for recognition of the spatial distribution of geochemical elements and separating anomalies from background using stream sediment geochemical data. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods, spatial statistical methods and fractal and multi-fractal methods. In this article, two efficient methods, i.e. U-statistics and the fractal concentration-area for separation and detection of anomalous areas of the background were used. The U spatial statistic method is a weighted mean, which considers sampling point positions and their spatial relation in the estimation of anomaly location. Also, fractal and multi-fractal models have also been applied to separate anomalies from background values. In this paper, the concentration–area model (C–A) was suggested to separate the anomaly of background. For this purpose, about 256 stream sediment samples were collected and analyzed. Then anomaly maps of elements were generated based on U spatial statistics and the C-A fractal methods for Au, As and Sb elements. According to obtained results, the U-statistics method performed better than C-A method. Because the comparisons of the known deposits and occurrences against the anomalous area created using thresholds from U-statistics and C-A method show that the spatial U-statistics method hits all of 3 known deposits and occurrences, the C-A fractal method hits 1 and fails 2. In addition, the results showed that these methods with regard to spatial distribution and variability within neighboring samples, in addition to concentration value frequency distributions and correlation coefficients, have more accurate results than the traditional approaches.