Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization

Increasing the prediction rate in the identification of mineralization zones using the stream sediment geochemical data is an essential issue in the regional exploration stage. The various univariate (such as fractal and probability plot (PP) methods) and multivariate methods (such as principal component analysis (PCA)) have been performed for interpreting the geochemical data and detecting the mineralization areas. In this study, a new geochemical criterion named geochemical anomaly intensity index (GAII) was proposed for geochemical anomaly mapping. This approach was developed based on the PCA method and the catchment basin coefficient (CBC). The GAII as a weighted geochemical index is calculated using the mineralization principal component (MPC) scores and CBC. GAII can be mapped and utilized for geochemical anomaly mapping and detecting the mineralization areas. Besides, GAII can identify paragenesis elements better than the current methods. In this research, GAII was successfully used to generate geochemical anomaly maps on shear zone gold mineralization in the southwest of Saqqez, NW Iran. The geochemical data have been divided into three groups based on catchment basins and the host rock type. Then the MPCs and paragenesis elements of Au mineralization have been obtained individually using PCA. Three mineralization paragenesis groups consisting of (Au, Sn), (Au, W), and (Au, As, Sb and Ba) have been recognized for different catchment basins of the southwest of Saqqez district using PCA. GAII was calculated and mapped based on the CBC_{(Au, Sn)}, CBC_{(Au, W)}, CBC_{(Au, As, Sb, Ba)}, and their MPC scores. GAII accurately detected the Au mineralization zones and improved the geochemical anomaly map in this area compared to the PP method, concentration-area fractal model, and U-spatial statistics method. The results demonstrated that GAII was successfully used for (a) identifying the mineralization paragenesis elements, (b) intensifying the geochemical anomaly, and (c) increasing the prediction rate of mineralization zones. The shear zone gold mineralization areas in the southwest of Saqqez district were effectively detected using this new data analysis approach. GAII has provided better results than the current PP method, concentration-area fractal model, and U-spatial statistics method.     

Fractal-Based Wavelet Filter for Separating Geophysical or Geochemical Anomalies from Background

The widely used wavelet filtering technique holds potential to approach anomaly–background separation in geophysical and geochemical data processing. Wavelet statistics provide crucial information on such filtering methods. In general, conventional (Gaussian-type) statistical modeling is insufficient to adequately describe the heavily tailed and sharply peaked (at zero) distribution of the wavelet coefficients of irregular geo-anomaly patterns. This paper demonstrates that the cumulative (frequency) number of the wavelet coefficient yields a power-law scaling relationship with the coefficient based on wavelet transform of a fractal/singular measure. This wavelet coefficient–cumulative number power-law model is proven to be more flexible and appropriate than the Gaussian model for characterizing the scaling nature of the coefficient distribution. Accordingly, a fractal-based filtering technique is developed based on the wavelet statistical model to decompose mixed patterns into components based on the distinct self-similarities identified in the wavelet domain. The decomposition scheme of the fractal-based wavelet filtering method considers not only the coefficient frequency distribution but also the fractal spectrum of singularities and the self-similarity of real-world features. Finally, a synthetic data test and real applications from two metallogenic provinces of China are used to validate the proposed fractal filtering method for anomaly–background separation and identification of geophysical or geochemical anomalies related to mineralization and other geological features.     

Fast wavelet-based stochastic simulation using training images

Spatial uncertainty modelling is a complex and challenging job for orebody modelling in mining, reservoir characterization in petroleum, and contamination modelling in air and water. Stochastic simulation algorithms are popular methods for such modelling. In this paper, discrete wavelet transformation (DWT)-based multiple point simulation algorithm for continuous variable is proposed that handles multi-scale spatial characteristics in datasets and training images. The DWT of a training image provides multi-scale high-frequency wavelet images and one low-frequency scaling image at the coarsest scale. The simulation of the proposed approach is performed on the frequency (wavelet) domain where the scaling image and wavelet images across the scale are simulated jointly. The inverse DWT reconstructs simulated realizations of an attribute of interest in the space domain. An automatic scale-selection algorithm using dominant mode difference is applied for the selection of the optimal scale of wavelet decomposition. The proposed algorithm reduces the computational time required for simulating large domain as compared to spatial domain multi-point simulation algorithm. The algorithm is tested with an exhaustive dataset using conditional and unconditional simulation in two- and three-dimensional fluvial reservoir and mining blasted rock data. The realizations generated by the proposed algorithm perform well and reproduce the statistics of the training image. The study conducted comparing the spatial domain filtersim multiple-point simulation algorithm suggests that the proposed algorithm generates equally good realizations at lower computational cost.     

Assessment of groundwater water quality in central and southern Gulf Coast aquifer,TX using principal component analysis

Principal component analysis has been applied for source identification and to assess factors affecting concentration variations. In particular, this study utilizes principal component analysis (PCA) to understand groundwater geochemical characteristics in the central and southern portions of the Gulf Coast aquifer in Texas. PCA, along with exploratory data analysis and correlation analysis is applied to a spatially extensive multivariate dataset in an exploratory mode to conceptualize the geochemical evolution of groundwater. A general trend was observed in all formations of the target aquifers with over 75 % of the observed variance explained by the first four factors identified by the PCA. The first factor consisted of older water subjected to weathering reactions and was named the ionic strength index. The second factor, named the alkalinity index explained greater variance in the younger formations rather than in the older formations. The third group represented younger waters entering the aquifers from the land surface and was labeled the recharge index. The fourth group which varied between aquifers was either the hardness index or the acidity index depending on whether it represented the influences of carbonate minerals or parameters affecting the dissolution of fluoride minerals, respectively. The PCA approach was also extended to the well scale to determine and identify the geographic influences on geochemical evolution. It was found that wells located in outcrop areas and near rivers and streams had a larger influence on the factors suggesting the importance of surface water–groundwater interactions.     

宽频去噪技术在地震资料处理中的应用

提高地震资料的信噪比是地震资料处理的关键,叠前去噪是进行噪声压制的主要处理技术。小波变换方法由于具有同时在时间域与频率域分析的特点,在信号的分析处理方面得到广泛的应用;笔者采用小波变换把叠前地震数据分为不同的频段,并对包含干扰波的频段采用中值滤波消除干扰,再运用小波反变换来重构去噪后的记录;该技术不仅实现了噪声压制,还达到了保持宽频带的目的,应用于实际资料处理中,取得了很好的去噪效果。     

钦-杭结合带南段庞西垌地区Ag-Au致矿地球化学异常信息识别与提取

基于分形与多重分形理论的非线性化探数据处理方法及以空间加权主成分分析模型为代表的地学多源信息融合技术,为致矿地球化学异常信息的识别和提取提供了有力的工具。本文以钦-杭结合带南段庞西垌地区1∶5万水系沉积物地球化学数据为例,研究如何综合运用多重分形局部奇异性与空间加权主成分分析这两种地学信息处理方法来识别和提取致矿地球化学异常信息。首先,采用滑动窗口的方法绘制了研究区与银金矿化关系密切的五种地球化学元素Au、Ag、Cu、Pb和Zn的局部奇异性指数图以增强局部弱缓异常信息。然后,在控矿条件分析的基础上,运用北东向断裂构造这一重要控矿要素对Ag-Au成矿作用的影响范围,即距离北东向断裂的距离,作为应变量来构建用于空间加权主成分分析的空间权重系数的计算模型,以此来突显化探样品在控矿地质条件约束下的空间相关性。进而,采用空间加权主成分分析方法来得到Au、Ag、Cu、Pb和Zn多元素奇异性指数值的组合异常(第一主成分因子得分)。结果表明:综合运用多重分形局部奇异性与空间加权主成分分析方法可以有效的识别和提取Ag-Au致矿地球化学异常信息,圈定具有示矿意义的多元素组合异常区。     

结构方程模型在地球化学数据分析中的应用

为了实现通过确定地球化学组合元素来反映成矿异常, 本文在主成分分析模型的基础上, 引入了新的结构方程模型(SEM).与主成分所不同的是, 结构模型综合了经典统计方法中的因子分析和路径分析方法, 以与研究对象具有较好的拟合度为标准来确定最优解, 并通过模型最优解来确定新的成分组合, 因此结构模型所确定的成分变量不一定是具有最大变化性, 而是与研究对象最接近的因子变量, 该因子能够更好地反映研究对象.介绍了结构方程模型方法的原理, 并利用加拿大Nova Scotia省西南部湖泊沉积物地球化学数据建立了与热液型金矿有关的地球化学元素结构方程模型, 研究了结构方程模型所给出的组合变量空间分布规律以及与金矿床的关系.与主成分分析方法所给出的计算结果进行对比发现, 结构模型所计算的与金矿相关的组合变量与矿床的空间相关性较高, 并且对金矿床(矿点)也具有较好的预测性.      

岩石地球化学数据挖掘及弱异常识别--以新疆阿舍勒铜矿为例

勘查地球化学找矿工作的重点在于正确解译地球化学数据,以便从冗杂的地质信息中精准提取与成矿有关的异常信息,指导找矿研究。然而,地球化学数据属于成分数据,具有闭合效应,只有对数据进行正确的预处理才能应用多元统计分析方法,还原元素真实的空间分布。本文在阿舍勒铜锌矿区外围南侧区域共收集1009件地表原生晕样品,对样品中的13种微量元素进行测试,并对原始数据、对数及ilr变换后的数据进行EDA分析,对比数据空间分布及内部结构特征。运用(稳健)主成分分析,结合成分数据双标图及第一主成分点位图,解译三类数据指示的元素组合与成矿信息之间的关联。随后运用多重分形滤波技术,对以ilr变换为基础的稳健主成分得分数据分解元素组合异常和背景分布特征。结果表明:①经过对数及ilr变换后的数据相比原始数据空间尺度更均匀,数据近似正态分布;②三类数据双标图表明,仅ilr变换后的数据消除了“闭合效应”,且其第一主成分元素分组揭示了研究区铜矿化与铅锌多金属矿化组合;以对数变换与ilr变换为基础的第一主成分点位图表明,后者主成分得分异常能够较好指示研究区地质找矿信息;③结合研究区地质找矿信息、元素组合异常及背景空间分布特征,最终圈定3个有利找矿靶区。     

东天山中段区域化探异常评价方法研究

本文利用1/20万区域化探数据,研究了东天山中段地区针对不同类型铜矿化的元素组合异常圈定方法。选择典型（含）铜矿床及其周围地段的化探测量点为建模样本,通过因子分析,查明针对不同矿化类型的指示元素定量组合;以因子得分为综合指标,用泛克里格剩余值圈定化探异常。结果得到3个元素组合：Cu-Co-Ni-Mn-V-Fe-Cr组合...     

基于小波分析的基桩内部应力监测数据异常辨识

小波分析在时频域具有良好的局部化特征,采用小波分解方法可简单、快捷地计算出数据序列的奇异性指数,以检出数据序列中的随机突变信号。通过试验数据验证,奇异性指数对随机突变信号的检出是正确有效的。根据多传感器监测系统中突变信号的分布规律,进行异常属性自动辨识。研究说明基于小波分析的异常属性识别是一种新颖有效的方法。     

小波变换在瞬时带宽提取中的应用

采用最佳匹配地震子波的解析小波 (简称SW小波) 作为基本小波, 提出了基于小波变换计算瞬时带宽的方法 (简称小波换变方法)。通过比较小波变换方法和通常使用的基于希尔伯特变换的瞬时带宽计算方法 (简称希尔伯特变换方法), 结果表明小波变换方法在精确度和抗噪性能等方面优于希尔伯特变换方法。用小波变换方法来计算某油田地震记录的瞬时带宽图, 结果可清楚的看到古潜沟的位置和模式。      

Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt,Tibet, China

Decomposing mixed geochemical patterns is a challenge in geochemical exploration and environmental assessment. In this paper, the spectrum–area technique (S–A) is used to decompose a mixed pattern of arsenic in Gangdese belt based on stream sediment data. S–A is a multifractal model based on power–law relationships between area of the set consisting of wave numbers with spectral energy density above S[A(>S)] on the 2D frequency domain. The original spatial distribution map of arsenic obtained by inverse distance weighted (IDW) shows a mixed pattern due to superposition of different geological processes or events and is converted into the frequency domain by means of Fourier transformation. Two components, including power spectrum density and phases, are obtained. The spectrum energy density (S) and the area (A) enclosed by the above-threshold spectrum energy density is plotted on a log–log scale. Two cutoff values determined by three straight lines define three filters which decompose the original map of arsenic into background, anomalous, and high frequency (noise) components. The areas with high anomaly of arsenic mainly are located surrounding known Cu deposits, indicating that arsenic anomalies may be related to Cu mineralization.     

Two-dimensional Conditional Simulations Based on the Wavelet Decomposition of Training Images

Scale dependency is a critical topic when modeling spatial phenomena of complex geological patterns that interact at different spatial scales. A two-dimensional conditional simulation based on wavelet decomposition is proposed for simulating geological patterns at different scales. The method utilizes the wavelet transform of a training image to decompose it into wavelet coefficients at different scales, and then quantifies their spatial dependence. Joint simulation of the wavelet coefficients is used together with available hard and or soft conditioning data. The conditionally co-simulated wavelet coefficients are back-transformed generating a realization of the attribute under study. Realizations generated using the proposed method reproduce the conditioning data, the wavelet coefficients and their spatial dependence. Two examples using geological images as training images elucidate the different aspects of the method, including hard and soft conditioning, the ability to reproduce some non-linear features and scale dependencies of the training images.     

胶东山城金矿叠加分带序列分解与叠加矿化性质识别

为适应地球化学分带的复杂性,定义了叠加分带序列和矿化阶段分带序列的概念,提出了叠加分带序列分解及叠加矿化性质识别方法。在胶东山城金矿岩石地球化学勘查中该方法获得成功的应用。揭露了该矿区已知矿体的常规分带序列中隐含的两种矿化叠加性质：１号脉为无工业意义的矿化叠加;８号脉则主要为下部隐伏矿体的叠加,并已得到后期勘探验证。     

Incorporation of principal component analysis,geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district,NW Iran

Delineation of mineralization-related geochemical anomalies of stream sediment data is an essential stage in regional geochemical exploration. In this study, principal component analysis (PCA) was applied to 12 selected elements to acquire a multi-element geochemical signature associated with Cu-Au mineralization in Feizabad district, NE Iran. The spatial distribution of enhanced multi-element geochemical signature of the second component (PC2) was modeled by different geostatistical procedures including variogram calculation, ordinary kriging (OK) and inverse distance weighting (IDW) interpolation techniques. Concentration-area (C-A) fractal and U-spatial statistics models were then applied to the continuous-value interpolated models for delineation of geochemical anomalies. Quantitative comparison of results based on the known mineral occurrences in the study area was carried out using normalized density index and success-rate curves. All generated models represent a high positive relation with known Cu (±Au) deposits in the study area, although, comparison of the results revealed that the OK-based U-spatial statistics model was superior to the rest of models. Besides, the low, moderate and high-intensity anomalies are spatially associated with geological-structural features in the study area.     

空间模式的广义自相似性分析与矿产资源评价

尺度不变性(scale invariance)包括自相似性(各向同性)、自仿射性(成层结构)、广义自相似性(各向异性标度不变性),是由各种地质过程和地质事件所产生的地质特征和模式的本质属性．尺度不变性可用分形和多重分形模型来表征．这些尺度特征的定量化可为刻画地质空问模式和模式识别提供有力的工具．例如。热液矿床的群聚现象可以用局部分形特征(局部奇异性)来刻画．通过在特征空问中(如频率空问)识别空问模式的广义自相似性．可以将空间混合模式进行分解或异常的识别．介绍了几种相关的分形模型和方法。包括度量空问模式广义尺度独立性(GSI)的线性模型;基于广义尺度独立性的异常分解S—A方法;度量空问模式的局部奇异性方法;以及如何利用分形特征预测未发现矿床的2种方法．有些方法已应用于许多矿产资源评价实例中．给出了对加拿大Nova Scotia省西南部湖泊沉积物样品中的4种元素As、Pb、Zn和Cu的地球化学数据处理分析结果。证明了局部奇异性分析和S—A异常分解方法对地球化学异常的增强和分离的有效性．研究表明：由S—A方法分解的异常往往具有多重分形的特点,而且普遍具有局部奇异性．研究区内具有明显奇异性的地区(元素含量富集区)是金矿异常区域。它们与金矿成矿作用和已知矿床的赋存密切相关．     

Spectral Analysis based on Wavelet Transform Maxima: Identification of Multi-order Stratigraphic Boundaries and Cycles

In sedimentology, stratigraphic sequences and cycles are ordered by time spans and physical scales, such as thickness, and bounded by discontinuities, including unconformities or flooding surfaces. Spectral analysis based on wavelet transform (WT) maxima is proposed and used as a quantitative tool to identify multi-order stratigraphic boundaries and cycles in well log data. The proposed spectral analysis is based on quantitative analysis on the center frequencies and resolutions of Gaussian wavelets in time and frequency, and uses a combination of the WT maxima based on both the first order Gaussian wavelet having a high time resolution and the seventh order Gaussian wavelet having a high frequency resolution. WT maxima spectra, which can characterize the evolution of WT maxima across scales and periods along WT maxima lines concerned with sequence boundaries, are used to detect dominant spectral peaks corresponding to the time-period domain WT maxima and to determine WT maxima spectral slopes. The WT maxima spectral slopes are helpful for discriminating sequence boundaries from intrasequence cyclic variations in well log data, and the time-period domain WT maxima are used to relate the detected boundaries to relevant cycles. The interval WT maxima spectra and the stationary index, related to the WT maxima spectra, are introduced as indicators that can be used for the hierarchical ordering of the boundaries and cycles. Application of the proposed method to well log data shows that the suggested method is efficient in identifying multi-order sequences that relate well to the Milankovitch cycles.

     

Spatial filtering of exploration geochemical data using EDA and robust statistics

The spatial filtering techniques that are used for the analysis and interpretation of exploration geochemical data to define regional distribution patterns or to outline anomalous areas are, in most cases, based on non-robust statistical methods. The performance of these techniques is heavily influenced by the presence of outliers that commonly exist in the data. This study describes a number of filtering techniques motivated by the development of exploratory data analysis (EDA) and robust statistical procedures. These are the median filter (MF) and the adaptive trimmed mean filter (ATM) for the smoothing of regional geochemical data to reduce spurious variations; two new filters, the fence filter (FF) and the notch filter (NF), have been developed to define geochemical anomalies.The application of the spatial filtering techniques is illustrated by Zn data from approximately 3100 stream sediment samples taken in a regional geochemical survey over 25,000 km² of the western margin of the São Francisco Basin, Brazil. Regional distribution patterns for Zn obtained by the MF and ATM filters are clearly related to known stratigraphic units. Anomaly filtering using the FF and NF has delineated most known base metal and gold occurrences, as well as a number of anomalies located in geologically favourable environments but unrelated to any known mineralization. The two anomaly filters have, for the most part, defined the same anomalies in the study area but only the NF highlights the anomaly associated with the important Morro Agudo Pb-Zn deposit, which is too subtle to be immediately apparent in the unprocessed data.     

基于邻域约束聚类的地球化学异常提取

通过分析地球化学数据的元素值属性和空间位置,提出一种基于邻域约束聚类的方法,使用该方法对地球化学元素聚类后,能提取矩形、环状、半环状等特殊形状,进而提取地球化学异常。选取河南崤山地区2个实验区的地球化学数据进行实验,实验一的结果表明,出现矩形的位置与已知钨矿矿点位置一致;实验二的结果表明,出现环形的位置与已知铜矿矿点位置一致。实验证明了基于邻域约束聚类的方法在提取地球化学异常方面的有效性。