共查询到19条相似文献，搜索用时 125 毫秒
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基于奇异值分解法的含量-面积法对化探异常的确定 总被引：1，自引：0，他引：1
地球化学场的数据通常具有分形的特征。应用奇异值分解方法对元素含量数据矩阵进行分解,对奇异值进行分形统计,得到了分形模型。重建元素含量的数据矩阵。然后利用含量-面积的分形方法对重建后的数据矩阵进行分形处理。使用MAPGIS软件进行统计分析,得出研究地区元素的异常下限,做出元素异常分布图,并与该地区已知矿点的分布图进行叠加分析,预测潜在的成矿区。 相似文献
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能谱密度-面积多重分形模型能够有效分解复杂地球化学异常模式,处理地球化学数据中的复杂非线性关系.这里采用能谱密度-面积多重分形模型,开展胶西北金矿集区地球化学异常识别和资源潜力评价研究.在对原始地球化学数据进行对数比变换的基础上,通过因子分析提取反应成矿过程的主要元素组合,将得到的元素组合图,采用能谱密度-面积多重分形... 相似文献
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以黄岗—甘珠尔庙成矿带南部1∶20万化探扫面数据为基础,借助奇异值分解技术及地球化学块体方法,提取了该区地球化学综合异常及评价该区锡、锌矿的成矿潜力。通过研究得出如下结论:(1)不同方向构造的交汇部位、构造截切地球化学异常的部位或不同方向异常的叠加部位,是寻找钼、锡、钨、铜、铅、锌等矿床的有利地段;(2)区内多数矿床的赋存状态与二叠纪地层有密切的空间关系,特别是二叠纪地层与岩体的接触带或二叠纪地层与其他时代地层的接触带并发育断裂构造的部位;(3)研究区内共圈定了Sn地球化学块体1个,区域异常1个,局部异常8个;Zn地球化学亚块体1个,区域异常1个,局部异常7个。并以1 000 m作为块体厚度,计算了该区潜在锡资源量为211.92万t,潜在锌资源量为146.42万t;(4)通过对该区地球化学块体及异常结构的分析,并根据该区锡、锌矿的成矿条件来确定远景区的等级,本文共圈定了Sn成矿Ⅰ类远景区3个,Ⅱ类远景区4个,Ⅲ类远景区2个;Zn成矿Ⅰ类远景区3个,Ⅱ类远景区2个,Ⅲ类远景区2个。 相似文献
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多标度分形与地球化学场分解 总被引：3，自引：0，他引：3
在地球化学叠加场内，不同时期、不同规模的地质作用形成的地球化学景观具有不同的分形标度区间和分维。利用多标度分形方法把黑龙江省团结沟金矿外围的叠加地球化学场分解成了分别与北东向和北面向构造相对应的地球化学正常场和异常场，并在此基础上，讨论了地球化学场的演化。 相似文献
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多标度多形与地球化学场分解 总被引：3，自引：0，他引：3
在地球化学叠加场内，不同时期、不同规模的地质作用形成的地球化学景观具有不同的分形标度区间和分维。利用多标度方法把黑龙江省团结沟金矿外围的叠加地球化学场分解成了分别与北东向和北西构造相对应的地球化学正常场和异常场，并在此基础上讨论了地球化学场的演化。 相似文献
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由于受到覆盖区矿体埋藏深度以及表生地球化学作用的影响,隐伏矿致异常以及弱缓化探异常信息的识别一直是地球化学勘查的难点。张八岭-管店地区位于安徽东部张八岭构造带北部,地表多被第四系覆盖,已有研究显示该区具有较好的Au成矿潜力。本文以Au矿床作为找矿目标,开展了面积性深层土壤取样,并利用多重分形理论的局部奇异性指数分析方法对深层土壤Au元素地球化学异常信息进行提取。结果表明,深层土壤Au元素的地球化学异常能够有效识别已知的金多金属矿(化)点,同时较好的反映出深部隐伏断裂构造与已知矿(化)点的空间关系;较之传统方法,基于非线性理论的奇异性指数方法能够有效降低地球化学背景场的影响,具有更好的异常识别效果,可应用于隐伏或亚出露环境下的地球化学异常识别研究。 相似文献
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张八岭—管店地区位于皖东张八岭构造带北部,区内已发现多处金多金属矿点,但尚未取得显著突破。近年来,多维分形理论与地球化学勘查相结合的方法已被广泛应用于Au、Cu矿床的找矿勘查工作,其中S-A(能谱密度-累计面积)方法是一种建立在能谱空间上的分形滤波技术,能够利用傅里叶变换对物化探数据进行异常和背景分解。以张八岭—管店地区深部土壤Au元素为例,利用S-A方法对研究区深部土壤中Au元素地球化学场进行异常识别。结果显示,该方法能够有效分离地球化学背景,识别已知金铜矿点的矿致异常信息,在区内已知矿点以外识别出多个Au元素异常区域。研究成果可为进一步找矿勘探提供新的目标和方向。 相似文献
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利用GIS与多重分形奇异值理论，探讨了一种用地球化学数据提取断裂构造信息的方法。基于滇东南地区1∶20万水系沉积物地球化学数据，依据构造地球化学原理选取Au、As、Sb、Hg、W、Sn、Bi共7种元素进行构造信息提取。经因子分析及因子旋转，发现主因子得分的空间分布与已知岩浆岩及断裂的空间展布行迹密切相关。通过计算断裂构造因子的奇异性指数进行弱信息增强，凸显出断裂构造的弱异常。将奇异性指数作为证据层，点状要素化的断裂构造数据作为训练点，进行模糊证据权(FWofE)计算，并对计算结果利用Student′s t值确定异常下限，圈定断裂构造异常范围。地球化学数据具有深部指示作用，这种断裂构造信息提取方法为深部找矿的断裂构造信息提取提供了新思路。 相似文献
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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. 相似文献
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利用小波分析提取深层次物化探异常信息 总被引：21，自引：1，他引：20
介绍了小波分析的多分辨理论与Ｍａｌｌａｔ快速算法,同时利用小波对实际物化探异常进行了不同尺度分解,为物化探异常的区域背影及局部异常的提取提供了 的途径,探讨了小波系数图所揭示的物化探异常的分形特征。 相似文献
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多重地球化学背景下地球化学弱异常增强识别与信息提取 总被引：1，自引：0，他引：1
为对钦州湾-杭州湾成矿带(南段)庞西垌地区地球化学数据进行异常识别研究与信息提取,利用含量-面积法(C-A)得出庞西垌地区成矿主元素的异常下限,得到各元素异常分布图,并与已知矿(床)点进行叠加分析,发现已知矿(床)点与C-A法分析得到的异常区基本吻合,可根据该异常区预测未知矿床,从而为该研究区矿产资源潜力评价提供依据。为进一步从研究区复杂的地球化学背景中分离出与成矿有关的地球化学异常,采用分形滤波技术(S-A)提取致矿异常。研究表明,S-A法可在C-A法揭示的区域异常的基础上更深层次地提取出与矿化有关的局部异常用以反映研究区的多重地球化学背景,S-A法可有效地使弱异常增强进而提取出致矿异常,为庞西垌地区探寻隐伏矿体提供依据。 相似文献
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《Chemie der Erde / Geochemistry》2016,76(4):491-499
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. 相似文献
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A spatial analysis method for geochemical anomaly separation 总被引：2，自引：0，他引：2
Qiuming Cheng F.P. Agterberg G.F. Bonham-Carter 《Journal of Geochemical Exploration》1996,56(3):183-195
One purpose of using statistical methods in exploration geochemistry is to assist exploration geologists in separating anomalies from background. This always involves two types of negatively associated errors of misclassification: type I errors occur when samples with background levels are rejected as background; and type II errors occur when samples with anomalous values are accepted as background. A new spatial statistical approach is proposed to minimize errors of total misclassification using a moving average technique with variable window radius. This method has been applied for geochemical anomaly enhancement and recognition as demonstrated by a case study of Au and Au-associated data for 698 stream sediment samples in the Iskut River area, northwestern British Columbia. Similar results were obtained using the fractal concentration-area method on the same data. By employing spatial information in the analysis, the process of selecting anomalies becomes less subjective than in more traditional approaches. 相似文献
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Geochemical data are typical compositional data which should be opened prior to univariate and multivariate data analysis. In this study, a frequency-based method (robust principal component analysis, RPCA) and a frequency-space-based method (spectrum–area fractal model, S–A) are applied to explore the effects of the data closure problem and to study the integrated geochemical anomalies associated with polymetallic Cu mineralization using a stream sediment geochemical dataset collected from the Zhongteng district, Fujian Province (China). The results show that: (1) geochemical data should be opened prior to RPCA to avoid spurious correlation between variables; (2) geochemical pattern is a superimposition of multi-processes and should be decomposed; and (3) the S–A fractal model is a powerful tool for decomposing the mixed geochemical pattern. 相似文献
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Identifying geochemical anomalies from background is a fundamental task in exploration geochemistry. The Gangdese mineral district in western China has complex geochemical surface expression due to complex geological background and 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. The results illustrate that weak anomalies are hidden within the strong variance of background and are not well identified by means of inverse distance weighted; neither are they clearly identified by the C–A method if this method is applied to the whole study area. On the other hand, singularity values provide new information that complements use of original concentration values and can quantify the properties of enrichment and depletion caused by mineralization. In general, producing maps of singularities can help to identify relatively weak metal concentration anomalies in complex geological regions. Application of singularity mapping technique in Gangdese district shows local anomalies of Cu are not only directly associated with known deposits in the central part of the study area, but also with E–W and N–E oriented faults in the north of the study area. Both types of anomalies should be further investigated for undiscovered Cu mineral deposits. 相似文献