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首页> 外文学位 >A fractal filtering technique for geochemical and geophysical data processing in GIS environments for mineral exploration.
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A fractal filtering technique for geochemical and geophysical data processing in GIS environments for mineral exploration.

机译：分形过滤技术，用于在GIS环境中进行矿物勘探的地球化学和地球物理数据处理。

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摘要

Regional geochemical and geophysical data are two types of basic data sources for mineral exploration. These data usually contain the accumulative effects generated by multiple geological processes. Decomposing the mixed data into anomaly components caused by mineralization or mineralization-related geological activities from background components due to the normal geological processes is a fundamental task for exploration geologists and the basis for geochemical and geophysical data processing techniques.; The objective of this thesis is to investigate the proper technologies for extracting information from regional geochemical and geophysical data for the purpose of mineral prediction. Fractal filtering technology (S-A) that we recently developed on the basis of multifractal theory has been systematically investigated and demonstrated as an information extraction technology that can separate a complex geochemical or geophysical field into particular components with distinct scaling properties. The method involves multifractal filters defined on scaling properties of 2-D power spectra.; Applications of the fractal filtering method to arsenic (As) geochemical concentration values of lake sediment geochemical samples from southwestern Nova Scotia, Canada, have demonstrated that the method can effectively extract the anomaly components with anisotropy from the background with significant variation, for which most traditional statistical geochemical data processing methods and frequency-based regional geophysical data processing methods usually do not work well. The decomposed components with clear geological meanings can be used to estimate mineral resource potentials for the turbidite-hosted Au deposits in the study area.; The S-A has been successfully applied to identify specific rock units on airborne radiometric eU/K and eU/eTh images by removing the high value “noise” and low value background components. It has been confirmed by GIS spatial analyses upon geology, Sn-W-U mineralization and multi-element geochemical anomalies that the anomalies extracted from the two ratio images are capable of delineating Sn-U-W related late stage intrusions and alteration zones.; A multivariate analysis, Principal Component Analysis (PCA), was applied to the decomposed anomalous components obtained by using the S-A for Au, As, W, Sn and Sb geochemical maps, which improved the analysis result significantly in comparison with applying PCA to the bulk values.; The patterns decomposed by the S-A method with different ranges of scales and orientations are useful for identifying the hierarchical spatial relationships among mineralization-controlling factors. Integration of the three components obtained by applying the fractal filtering method to the As geochemical map provides a clear view how the multiple stage and scale mineralization processes finally led to the enrichment of precious metallic element Au and where to find the high potential districts for exploration. Integrating the anomaly components obtained from radiometric eU/K and eU/eTh images and geochemical elements F, Li, Rb, Nb and Th shows that the two types of anomalies may indicate the locations of Sn-W-U deposits.
机译：区域地球化学和地球物理数据是矿物勘探的两种基本数据源。这些数据通常包含由多个地质过程产生的累积效应。将混合数据从正常地质过程导致的矿化或与矿化有关的地质活动引起的异常成分分解为背景成分，这是勘探地质学家的基本任务，也是地球化学和地球物理数据处理技术的基础。本文的目的是研究用于矿物预测的从区域地球化学和地球物理数据中提取信息的适当技术。我们最近在多重分形理论的基础上开发的分形过滤技术（S-A）已得到系统地研究和证明，它是一种信息提取技术，可以将复杂的地球化学或地球物理场分离为具有不同缩放特性的特定组件。该方法包括在二维功率谱的缩放特性上定义的多重分形滤波器。分形滤波方法在加拿大新斯科舍省西南部湖泊沉积物地球化学样品中砷（As）地球化学浓度值中的应用表明，该方法可以有效地从背景中提取具有各向异性的异常成分，对于大多数传统方法统计地球化学数据处理方法和基于频率的区域地球物理数据处理方法通常效果不佳。具有明确地质意义的分解成分可用于估算研究区浊积岩型金矿床的矿产资源潜力。通过去除高值“噪声”和低值背景成分，S-A已成功应用于识别航空辐射度eU / K和eU / eTh图像上的特定岩石单位。 GIS通过地质，Sn-W-U矿化和多元素地球化学异常的空间分析已证实，从这两个比率图像中提取的异常能够描绘出与Sn-U-W有关的晚期侵入和蚀变带。对于使用Au，As，W，Sn和Sb地球化学图谱通过SA获得的分解异常组分，应用了多元分析，主成分分析（PCA），与将PCA应用于大块相比，分析结果得到了显着改善。价值观。通过S-A方法分解的尺度和方向范围不同的模式对于识别成矿控制因子之间的分层空间关系很有用。通过将分形过滤方法应用于As地球化学图而获得的三个成分的整合提供了清晰的视图，即多阶段和规模的成矿过程最终如何导致了贵金属元素Au的富集以及在何处寻找勘探的高潜力区。将从辐射eU / K和eU / eTh图像获得的异常分量与地球化学元素F，Li，Rb，Nb和Th进行积分显示，这两种异常可以指示Sn-W-U矿床的位置。

著录项

  • 作者

    Xu, Yaguang.;

  • 作者单位

    York University (Canada).;

  • 授予单位 York University (Canada).;
  • 学科 Engineering System Science.; Engineering Mining.; Geochemistry.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 164 p.
  • 总页数 164
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统科学;矿业工程;地质学;
  • 关键词

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