原生晕-因子分析地球化学异常识别——以内蒙古查干花地区1:5万水系沉积物Mo异常提取为例

Identification of geochemical anomalies using primary halo-factor analysis: Taking the Mo anomaly extraction of 1:50000 stream sediments in the Chaganhua area of Inner Mongolia as an example

摘要:

近些年来我国的化探扫面工作积累了丰富的化探数据资料，如何有效利用这些化探数据，并结合已知的矿床（点）类型来针对性获得更加综合的找矿化探信息，是目前找矿过程中值得研究的课题。本文提出了原生晕-因子分析（PH-FA:primary halo-factor analysis）方法，通过利用原生晕的成矿元素的关联关系来刻画次生晕中的关联属性，使次生晕中成矿元素关联属性更接近成矿事件的元素数据结构。以内蒙古查干花地区为例，从原生晕矿石地球化学数据中分析得出Bi为Mo最相关的元素，通过对比背景的富集程度进行权重组合计算获得Mo、Bi的PH-FA权重系数分别为0.717607和0.282393，将Bi与Mo进行权重组合计算PH-FA得分，并提取化探异常。结果表明，该方法与传统的因子分析相比，所圈定的异常与控矿断层相交的重合率高13.05%，且构造线、岩体等控矿因素同样具有更强的空间分布相关性，表明该方法在识别控矿因素以及矿区外围找矿中有较高的可行性。

Abstract:

In recent years, China has accumulated a wealth of geochemical data in its geochemical exploration and scanning work. How to effectively utilize these geochemical data and combine them with known ore deposit (point) types to obtain more comprehensive mineralization exploration information is a topic worth studying in the current mineral exploration process. This article proposed the primary halo-factor analysis (PH-FA: primary halo-factor analysis) method, which used the correlation relationships of the ore-forming elements in the primary halo to characterize the correlation attributes in the secondary halo, making the correlation attributes of the ore-forming elements in the secondary halo closer to the element data structure of the ore-forming event. Taken the Chaganhua area in Inner Mongolia as an example, the ore geochemical data of primary halo was analyzed to determine that Bi was the most relevant element to Mo. By comparing the enrichment degree of the background and calculating the weight combination, the PH-FA weight coefficients of Mo and Bi were 0.717607 and 0.282393, respectively. Bi and Mo were combined and the PH-FA score was calculated to extract geochemical anomalies. The results showed that compared with traditional factor analysis, this method had a 13.05% higher overlap rate between the delineated anomalies and the ore-controlling faults. The ore-controlling factors such as structural lines and rocks also had stronger spatial distribution correlation. This indicates that this method has high feasibility in identifying ore-controlling factors and exploring the surrounding areas of the mining region.