辽宁省义县幅水系沉积物地球化学异常筛选及评价
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摘要
本文运用新的方法和思路,对辽宁省义县幅1:20万区域地球化学数据进行二次开发。通过研究该区水系沉积物地球化学特征,查明不同元素的总体含量水平、空间分布情况和元素组合特点。利用分类标准化方法对原始数据进行预处理,以消除不同地质背景对异常圈定的影响。在元素综合异常、常量元素综合异常、矿物综合异常和马氏距离综合异常的研究基础上,圈定了38个地球化学综合异常区,并通过元素组合特点推测了其可能矿化类型。根据异常区面积、元素异常规模、异常元素数量、矿物异常规模、异常矿物数量、马氏距离和灰色关联度等综合指标对各个异常区进行综合评价。最后,在综合考虑异常元素组合、矿化类型和已知矿床的分布情况下,圈定了6个找矿远景区,并结合地质背景对它们进行分析评价,筛选出7个找矿优选区。
Because of China's regional geochemical scanning program, our country had accumulated a large number of regional geochemical data. However, as the limitation of data-processing conditions then, the valuable information in the data has not been fully extracted. As time goes by, many new methods, new understanding and new theories of geochemical data processing are emerging. For example, the development and popularization of computer technology and emergence of a variety of professional software have improved data processing efficiency and quality; the application of new mathematical method in earth science have also provided a new way of thinking and methods for the extraction of prospecting information. Based on this reality, this study, through the use of new ideas and methods, re-processed the 1:200000 geochemical data of Yixian sheet in Liaoning Province, determined the prospecting area and provided reference for a new round of geological prospecting work.
Regional geochemical characteristics embodied in the change and distribution of single element as well as the distribution of elements. Statistics of change coefficient and enrichment factor of different elements revealed that, in the study area, enriched elements (enrichment factor greater than 1.18) of stream sediment are Sr, Na2O, Ba and Zr and depleted elements (enrichment factor less than 0.44) are W, As, Au, Cd, Bi and Sb.
Unevenly distributed elements are Au (1.40), Hg (1.31), Bi (0.73) and As (0.63) and, among evenly distributed elements, relatively large elements of changes coefficients (greater than 0.35) are Ni, Cd, Sb, P, MgO, Cr and Mo. According to known mineralization type of study area, we selected 23 elements data of all samples to do R-factor analysis and extracted seven factors (variance contribution more than one), namely F1(Ni-Cr-Fe2O3-Co-Ti-V-Cu-Zn-Mn), F2(Sb-As-Bi-Hg), F3 (Y-Th-La-Nb), F4(Zr), F5(SiO2), F6(Au-Ag-Mo) and F7(Pb). The seven factors represent the main types of elements of study area.
Through research of regional geochemical characteristics of the study area, we find that average level of some elements in different samples classification has big differences, such as Cr, the maximum value in some classification is highly to 98.09 and the minimum value is only 52.43. In order to eliminate the effect of litho logy background to anomaly delineation, respectively standardize the samples data of each category and re-combined it to form new standardized data.
From two angles, mineralization anomaly and wall rock alteration, integrated anomaly were delineated of and finally inspected and corrected by mahalanobis distance integrated anomaly.
This study, using standardized data of 30 elements other than constant element of all samples in study area, statistics the number of constant elements (E value),whose standardized value is greater than 2 ,of each sample ,and delineates comprehensive zoning anomaly of elements, reflecting the mineralization anomaly information.
During the research, stream sediment constant element method and stream sediment mineral composition method are used to extract wall-rock alteration information. Stream sediment Constant element method directly uses the oxide standardized data, chooses 1.5 as threshold, and delineate each constant element anomaly. Through the combination of these anomalies, constant element integrated anomaly is delineated. Stream sediment mineral composition method is based on optimization algorithms in linear programming method, through the use of all oxide raw data, calculate the content of clay minerals of each sample. According to the classification of samples, the mineral data are standardized. Using these standardized data, the study statistics the number of clay mineral (E value), whose standardized value is greater than 1.5, of each sample, and delineates comprehensive zoning anomaly of mineral.
The comparison between mineral comprehensive zoning anomaly and constant elements integrated anomaly shows their high consistency, just the scope of the latter is a bit larger. The scatter of measured values and calculated values shows that, for quartz and feldspar, two kinds of values have clear linear positive correlation, but for clay minerals, they shows a certain degree of negative correlation. Explore the reasons for the existence of this phenomenon can be found that it is caused by X-ray diffraction method of the mineral, which is used to test mineral content in laboratory. This method has a high degree of requirements for the crystallization of minerals and it can only detect the high crystallized portion of mineral content. As Quartz and feldspar are detrital minerals, which have a high degree of crystallization, so the tested value and calculated value have a better consistency. For clay mineral, the crystallization of which in soil is a bit lower, but the total content of which is high, so the measured values and calculated values will appear contrast and the negative correlation between them is not surprising.
Through the above-mentioned double authentication, it shows that comprehensive zoning anomaly of mineral can reflect the wall-rock alteration information, so treat it as the final wall-rock alteration zones.
In order to delineate mahalanobis distance integrated anomaly, using all standardized data of all elements, the study calculate the mahalanobis distance of each sample and delineate mahalanobis distance comprehensive anomaly, statistic the elements characteristics of each sample.
Through comprehensive analysis of mineralization anomaly, wall rock alteration and mahalanobis distance integrated anomaly, the final integrated anomaly zones are delineated and numbered.
In order to find out the of each comprehensive anomaly zone, the study statistic the elements characteristics and alternation minerals of each anomaly zone, referencing the elements characteristics of each mahalanobis distance anomaly sample and the distribution of known mineral bed, finally fix on the potential mineralization type, namely Cu-Mo, Au-Ag, Cu-Pb-Zn, Fe multi-metal, barite and rare metal.
At the base of comprehensive consideration of a variety of factors, such as anomaly area, anomaly scale, the number of anomaly elements, anomaly elements characteristics, alternation mineral scale, the number of alternation mineral, using sorting method, the study evaluate the 38 anomaly zones. Further more, through the research of Paishanlou gold mine in study area, selecting 13 elements (Ag, Au, As, B, Cu, Hg, Pb, Sb, Sn, Sr, V, W and Z), the potential gold mineralization zones are secondly evaluated. The comparison of two evaluation let us know that some anomaly zones are highly resemble with Paishanlou gold mineral.
Based on the evaluation of all anomaly zones, the study carve up 6 perspective area and analysis them with geological information, finally fixed up 7 preferred prospecting areas.
Because of China's regional geochemical scanning program, our country had accumulated a large number of regional geochemical data. However, as the limitation of data-processing conditions then, the valuable information in the data has not been fully extracted. As time goes by, many new methods, new understanding and new theories of geochemical data processing are emerging. For example, the development and popularization of computer technology and emergence of a variety of professional software have improved data processing efficiency and quality; the application of new mathematical method in earth science have also provided a new way of thinking and methods for the extraction of prospecting information. Based on this reality, this study, through the use of new ideas and methods, re-processed the 1:200000 geochemical data of Yixian sheet in Liaoning Province, determined the prospecting area and provided reference for a new round of geological prospecting work.
Regional geochemical characteristics embodied in the change and distribution of single element as well as the distribution of elements. Statistics of change coefficient and enrichment factor of different elements revealed that, in the study area, enriched elements (enrichment factor greater than 1.18) of stream sediment are Sr, Na2O, Ba and Zr and depleted elements (enrichment factor less than 0.44) are W, As, Au, Cd, Bi and Sb.
Unevenly distributed elements are Au (1.40), Hg (1.31), Bi (0.73) and As (0.63) and, among evenly distributed elements, relatively large elements of changes coefficients (greater than 0.35) are Ni, Cd, Sb, P, MgO, Cr and Mo. According to known mineralization type of study area, we selected 23 elements data of all samples to do R-factor analysis and extracted seven factors (variance contribution more than one), namely F1(Ni-Cr-Fe2O3-Co-Ti-V-Cu-Zn-Mn), F2(Sb-As-Bi-Hg), F3 (Y-Th-La-Nb), F4(Zr), F5(SiO2), F6(Au-Ag-Mo) and F7(Pb). The seven factors represent the main types of elements of study area.
Through research of regional geochemical characteristics of the study area, we find that average level of some elements in different samples classification has big differences, such as Cr, the maximum value in some classification is highly to 98.09 and the minimum value is only 52.43. In order to eliminate the effect of litho logy background to anomaly delineation, respectively standardize the samples data of each category and re-combined it to form new standardized data.
From two angles, mineralization anomaly and wall rock alteration, integrated anomaly were delineated of and finally inspected and corrected by mahalanobis distance integrated anomaly.
This study, using standardized data of 30 elements other than constant element of all samples in study area, statistics the number of constant elements (E value),whose standardized value is greater than 2 ,of each sample ,and delineates comprehensive zoning anomaly of elements, reflecting the mineralization anomaly information.
During the research, stream sediment constant element method and stream sediment mineral composition method are used to extract wall-rock alteration information. Stream sediment Constant element method directly uses the oxide standardized data, chooses 1.5 as threshold, and delineate each constant element anomaly. Through the combination of these anomalies, constant element integrated anomaly is delineated. Stream sediment mineral composition method is based on optimization algorithms in linear programming method, through the use of all oxide raw data, calculate the content of clay minerals of each sample. According to the classification of samples, the mineral data are standardized. Using these standardized data, the study statistics the number of clay mineral (E value), whose standardized value is greater than 1.5, of each sample, and delineates comprehensive zoning anomaly of mineral.
The comparison between mineral comprehensive zoning anomaly and constant elements integrated anomaly shows their high consistency, just the scope of the latter is a bit larger. The scatter of measured values and calculated values shows that, for quartz and feldspar, two kinds of values have clear linear positive correlation, but for clay minerals, they shows a certain degree of negative correlation. Explore the reasons for the existence of this phenomenon can be found that it is caused by X-ray diffraction method of the mineral, which is used to test mineral content in laboratory. This method has a high degree of requirements for the crystallization of minerals and it can only detect the high crystallized portion of mineral content. As Quartz and feldspar are detrital minerals, which have a high degree of crystallization, so the tested value and calculated value have a better consistency. For clay mineral, the crystallization of which in soil is a bit lower, but the total content of which is high, so the measured values and calculated values will appear contrast and the negative correlation between them is not surprising.
Through the above-mentioned double authentication, it shows that comprehensive zoning anomaly of mineral can reflect the wall-rock alteration information, so treat it as the final wall-rock alteration zones.
In order to delineate mahalanobis distance integrated anomaly, using all standardized data of all elements, the study calculate the mahalanobis distance of each sample and delineate mahalanobis distance comprehensive anomaly, statistic the elements characteristics of each sample.
Through comprehensive analysis of mineralization anomaly, wall rock alteration and mahalanobis distance integrated anomaly, the final integrated anomaly zones are delineated and numbered.
In order to find out the of each comprehensive anomaly zone, the study statistic the elements characteristics and alternation minerals of each anomaly zone, referencing the elements characteristics of each mahalanobis distance anomaly sample and the distribution of known mineral bed, finally fix on the potential mineralization type, namely Cu-Mo, Au-Ag, Cu-Pb-Zn, Fe multi-metal, barite and rare metal.
At the base of comprehensive consideration of a variety of factors, such as anomaly area, anomaly scale, the number of anomaly elements, anomaly elements characteristics, alternation mineral scale, the number of alternation mineral, using sorting method, the study evaluate the 38 anomaly zones. Further more, through the research of Paishanlou gold mine in study area, selecting 13 elements (Ag, Au, As, B, Cu, Hg, Pb, Sb, Sn, Sr, V, W and Z), the potential gold mineralization zones are secondly evaluated. The comparison of two evaluation let us know that some anomaly zones are highly resemble with Paishanlou gold mineral.
Based on the evaluation of all anomaly zones, the study carve up 6 perspective area and analysis them with geological information, finally fixed up 7 preferred prospecting areas.
引文
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[3]李宝强,孙泽坤.区域地球化学异常信息提取方法研讨[J].西北地质,2004,37(1):102-108.
[4]王宝珍.间隙统计法在识别地球化学异常下限中的应用[J].物探化探计算技术,1992,14(3):213-215.
[5]陈建国,王仁铎,陈永清.利用分形统计学提取化探数据中的隐蔽信息并圈定地球化学异常[J].地球科学—中国地质大学学报,1998,23(2):175-178.
[6]陈聆,魏友华,郭科.用含量面积法确定深切割地区地球化学异常[J]西南师范大学学报(自然科学版),2004,29(5):867-869.
[7]黄厚辉,郭科,唐菊兴.基于小波多尺度分析的异常下限确定方法[J].地质找矿论丛,2007,22(4):311-313.
[8]费光春,李佑国,温春齐,等.子区中位数衬值滤波法在川西斑岩型铜矿区地球化学异常的筛选与查证中的应用[J].物探与化探,2008,32(01):66-69.
[9]刘友梅,杨蔚华.模糊数学在地球化学研究中的应用[J].矿物岩石地球化学通报,1988,(04):229-232.
[10]王大勇,郝立波,陆继龙.人工神经网络在识别浅覆盖区地质体中的应用[J].吉林大学学报(地球科学版),2006,(S2):185-187.
[11]王学彦,赵更新,王小兵.新疆北部区域地球化学异常特征[J].新疆地质, 2001,19(03):194-199.
[12]蔡以评.区域地球化学异常的有序排列在基础地质研究中的应用探讨[J].福建地质,1991,(01):64-69.
[13]贾振邦,霍文毅,赵智杰,等.应用次生相富集系数评价柴河沉积物重金属污染[J].北京大学学报(自然科学版),2000,36(6): 808-812.
[14]任天翔,尹冰川,刘茹英,等.中国水系沉积物中39种元素背景值[C].第五届全国勘查地球化学学术讨论会论文摘要,地质出版社,1993:252-253.
[15]景宗楷.关于矿体变化系数的探讨[J].河南国土资源,1987,5(03):73-76.
[16]黄薰德,吴郁彦.地球化学找矿[M].地质出版社,1986:256-263.
[17]时艳香,纪宏金,陆继龙,等.水系沉积物地球化学分区的因子分析方法与应用[J].地质与勘探,2004,40(5):73-76.
[18]董毅.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[J].矿产与地质,2008,22(01):78-82.
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