Review on the Progress of Genetic Research Methods of Fluorite Deposits
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摘要:
萤石是重要的战略性非金属矿产,深化其成因理论的研究至关重要。笔者对萤石矿床成因研究方法的进展进行综述,以期促进国内萤石矿床成因的深入研究,助力新一轮找矿突破战略行动。在对全球和中国的萤石矿床分布特征和成因类型进行归纳总结的基础上,重点从流体包裹体、成矿流体和物质来源、成矿年代学等方面综述了目前的主要研究现状和进展。总结了萤石的流体包裹体组合和单个流体包裹体原位成分分析技术,探讨了H-O-Sr-Ca-Nd同位素示踪物源,讨论了原位微量稀土元素对成矿过程的精细刻画等。笔者认为应该重点使用原位分析技术对流体包裹体和萤石成分进行测试,以便更精细的刻画成矿流体组分的演化过程。萤石Lu-Hf、U-Pb、Sm-Nd、(U-Th)/He和裂变径迹年代学不仅对精确获得含萤石的矿床成矿年龄至关重要,而且在矿产勘查中对矿床抬升剥蚀的正确认识也十分有必要。
Abstract:Fluorite is a strategically important nonmetallic mineral, the research on its genesis is of significant importance. This paper reviews the progress of genetic research methods in order to promote the in-depth study of the genesis of domestic fluorite deposits and make a contribution to a new round of prospecting breakthrough strategy. The distribution characteristics and genetic types of fluorite deposits worldwide and in China are summarized. Furthermore, the current status and progress of the main research methods in fluid inclusions, ore-forming fluids and material sources, and ore-forming geochronology are reviewed. The fluid inclusion assemblage and in-situ composition techniques of single fluid inclusion of fluorite are summarized, and the source of H-O-Sr-Ca-Nd isotope tracer and the fine reflection of in-situ trace rare earth elements on the mineralization process are discussed. The author proposes that the in-situ analysis technique should be employed to test the fluid inclusions and fluorite components, thereby enabling a more accurate description of the evolution process of ore-forming fluid components. The application of Lu-Hf, U-Pb, Sm-Nd, (U-Th)/He and fission track geochronology of fluorite is not only important for the accurate determination of the ore-forming age of fluorite-bearing deposits, but also necessary for the correct interpretation of deposit uplift and denudation in ore-forming exploration.
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Keywords:
- fluorite /
- ore-forming fluid /
- metallogenic geochronology /
- LA-ICP-MS /
- Development trend
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滑坡是中国分布最多、危害最大的地质灾害。目前对其分布特征、破坏方式、运动机理等方面已有大量的研究(谢婉丽等,2017,2018a,2018b;刘颖莹等,2018;郭倩怡等,2021;Liu et al.,2022)。滑坡往往受多种地质环境作用影响,难以准确预测,使人类生命财产和生存环境受到重大威胁,因而很有必要开展区域性滑坡易发性评价,选择合适预防措施,以减轻地质灾害造成的影响。
目前,滑坡易发性评价主要包括启发式推断法、数理统计分析法和机器学习法等(王高峰等,2021)。启发式推断法如层次分析、专家打分(冯卫等,2021)等主要凭借主观经验确定因子权重;数理统计分析法如逻辑回归法(屠水云等,2022)、证据权法(杨华阳等,2020)、模糊综合评判法(谢婉丽等,2018b)、信息量法(阮沈勇等,2001)等主要依靠对调查数据进行数理统计分析建立易发性评价模型,可以显著降低评价过程中的主观性,但是其评价结果依赖于大量精确数据支持,对于高维空间的复杂问题,数理统计分析法不可避免的存在欠拟合、预测准确度不高的缺点。
目前学术界对滑坡易发性评价更偏重于机器学习方法的研究。机器学习可以深挖数据本质,获取最精准的滑坡易发性评价结果,具有自学习、自适应、非线性映射能力强等优点,能充分考虑事件发生概率及其影响因素之间非线性关系(王高峰等,2021),可以很好解决传统概率统计模型的缺陷(Dickson et al., 2016)。常用的评价方法有人工神经网络(冯杭建等,2016;田乃满,2020;李泽群,2022)、随机森林法(林荣福等,2020;刘睿等,2020;吴润泽等,2021)、支持向量机(王念秦等,2019;王倩等,2021;赵铮等,2022)和决策树(杨永刚等,2019)等。但是截至目前,学术界对于最佳的滑坡易发性评价模型仍未有定论。
为了探究滑坡易发性的最佳模型,笔者将MaxEnt模型引入滑坡易发性评价。
$ {\text{MaxEnt}} $ 模型作为一种机器学习方法,它以最大熵思想为核心,具有精确、高效、样本量要求低、可避免模型过度拟合等优点,常被应用于生态学领域的物种潜在分布范围预测(唐兴港等,2021),其思想与滑坡易发性区划极为类似,都是基于当前已有数据分析研究对象未来的发展变化趋势。近年来,不少国外学者已采用该方法进行滑坡易发性研究(Suchita et al.,2016;Maryam et al.,2019;Kornejady et al.,2017)。例如,Felicísimo Ángel M等(2013)在西班牙德巴河谷滑坡预测研究中将$ {\text{MaxEnt}} $ 模型和其他3种机器学习方法进行比较,结果显示$ {\text{MaxEnt}} $ 模型精度明显高于其他模型。然而,国内基于$ {\text{MaxEnt}} $ 模型在灾害易发评价方面的研究相对较少(赵冬梅等,2020;麦鉴锋等,2021;屈新星等,2021)。王益区和印台区位于铜川市中部,是铜川市政治、经济、文化和商贸中心,区内沟壑纵横,地下水资源匮乏,地质环境条件脆弱,滑坡分布较多,对区内展开滑坡易发性工作尤为重要。因此,笔者基于铜川市中部地区滑坡分布现状,采用
$ {\text{MaxEnt}} $ 模型,结合$ {\text{ArcGIS}} $ 空间分析模块,选取高程、坡度、坡向、曲率、距道路的距离、距水系的距离、地形地貌和岩土体类型8个环境因子对滑坡易发性进行研究,为铜川市中部地区防灾减灾与国土空间规划提供科学参考。1. 研究区概况
研究区位于铜川市中部,包括王益区、印台区,北接宜君县,南连富平县,东同白水县及蒲城县接壤,西与耀州区毗邻。经纬度涵盖范围为E 108°51′~109°26′,N 34°59′~35°12',总面积为791.74 km2。研究区属半干旱大陆性季风气候,历年年降水量达334.6~879.3 mm,平均为584.5 mm,且多集中在夏季,以暴雨、连续降雨的形式出现。
研究区位于汾渭地堑与黄土高原过渡区域,丘陵起伏,沟壑密布,属于山、塬、川并存的地貌类型。区内地貌可分为土石山地区、黄土残塬区和河谷阶地区。区内构造简单,未见大型褶皱与断裂。复杂的地貌、脆弱的地质环境,使区域内滑坡灾害极其发育,对研究区社会发展和居民生命与财产安全造成了很大威胁。滑坡主要分布在研究区中部及东部人类工程活动频繁的人口密集区(图1)。
2. 研究方法
2.1 数据来源
本文所涉及数据包括:
$ {\text{DEM}} $ (数字高程模型)(下载自地理空间数据云)、岩土体类型、地貌类型(下载自91卫图)、河流、道路信息(下载自全国地理信息资源目录系统)和野外调查收集的滑坡点数(44个)。结合野外实际调查数据,研究区共确定滑坡44处,其中约70%以上的滑坡属于浅层牵引式黄土滑坡,65%以上的滑坡属于小型滑坡。研究区滑坡主要分布在道路、水系两侧,且多发生于人类工程活动剧烈的区域。
2.2 评价因子选取
根据研究区滑坡分布规律以及诱发因素,结合实际野外调查,经过筛选对比,最终确定高程、坡度、坡向、曲率、距道路的距离、距水系的距离、地形地貌和岩土体类型8个环境因子作为评价指标。
(1)高程:高程对滑坡易发性影响主要体现在不同高程区域气候、降水、植被类型、人类工程活动均有不同,因此对滑坡产生的影响有所不同。
(2)坡度:坡度代表了边坡陡倾程度,同时影响了斜坡应力大小与方向。坡度数据在ArcGIS中通过DEM数据提取。
(3)坡向:坡向对滑坡易发性的影响体现在朝向不同,受到的日照时长与太阳辐射强度不同,阴坡与阳坡的温差较大,同时对降水与植被类型也有一定的影响;坡向数据在ArcGIS中通过DEM数据提取。
(4)曲率:曲率代表了边坡的凹凸程度,曲率值大于0为凸形坡,等于0为直线形坡,小于0为凹形坡。曲率数据在ArcGIS中通过DEM数据提取。
(5)距道路的距离:修建道路不可避免会对沿途边坡坡脚进行开挖,破坏边坡的结构,诱发了滑坡的形成。研究区道路密集,通过ArcGIS欧氏距离分析模块获取距道路的距离栅格数据。
(6)距水系的距离:河水往往对河岸两侧边坡产生冲蚀作用,导致斜坡稳定性降低,往往距离河流越近,滑坡发生的频率越高。通过ArcGIS欧氏距离分析模块获取距水系的距离栅格数据。
(7)地形地貌:地形地貌是滑坡发生的重要因素,根据收集到的研究区地貌资料,研究区分为黄土残塬、土石山地和河谷阶地3种地貌。
(8)岩土体类型:研究区岩土体类型共分为坚硬、半坚硬层状碎屑岩,碳酸盐岩,黄土,砂砾石土共5类。研究区整体上呈现为上部为黄土,下部为基岩。研究区由北到南呈现出基岩切割深度逐渐减小的趋势。
在易发性评价开始前在ArcGIS中预先将所有数据坐标系与栅格像元大小(30 m × 30 m)进行统一,将结果数据以ASC文件导出。本次易发性评价所用的各环境变量图层见图2。
2.3 评价方法
2.3.1 MaxEnt原理
最大熵(
$ {\text{MaxEnt}} $ )原理是在满足全部已知约束的基础上开展概率事件的合理预测,但是这一过程中对于未知的部分不做任何假设。只有这样,预测结果中的概率分布最为均匀,误判概率最低,因而最终得出的概率分布结果熵值也就最高。换句话说,最大熵理论代表着在满足既定条件的情况下,熵值越大,越接近它的自然现实状态(范亦嵩,2020)。$ {\text{MaxEnt}} $ 模型运行同时需要滑坡地理分布与环境影响因子数据。在本文中,将研究区划分有限像元集,称为X。每个像元x∈X被分配一个非负概率值p(x),用来表示发生滑坡的概率分布,像元概率总和为1。这样就构建了研究区滑坡的概率分布,称为$ \pi $ 。$ \pi $ 是未知的,最大熵原理认为熵最大时,概率分布p最接近真实状态。为了求解p,我们需要先构建p分布的约束条件,关于未知的分布$ \pi $ ,我们已知的信息就是历史滑坡的发生信息和历史滑坡点的环境因子信息,特征函数具有多种表达形式,本次选用MaxEnt模型自带的表达形式,即式(1):$$ {f_x}(...) = {\lambda _1}{f_1} + {\lambda _2}{f_2} + {\lambda _3}{f_3} + {\lambda _4}{f_4} + ...... + {\lambda _i}{f_i} $$ (1) 式中:
$ \lambda $ 是一组参数,$ {f_i} $ 为像素x位置的第i个环境因子值。已知信息可以表达为所有已发生滑坡的特征函数的平均值,即式(2):$$ e = \frac{1}{m}\sum\limits_1^m {{f_{x{\rm{i}}}}(...)} $$ (2) 式中:m是历史滑坡发生数目,
$ {f_{x{\rm{i}}}}(...) $ 是第i个滑坡所在像元的特征函数。概率分布p的环境因子期望值可按公式(3)计算:$$ E={\displaystyle {\sum }_{x\in X}p(\text{x}){f}_{x}}(\mathrm{...}) $$ (3) p的环境因子期望值需要无限接近于e,所以分布p的约束条件为式(4):
$$ |e-E|<\beta $$ (4) 式中:
$ \beta $ 为任意小的正常数。最大熵分布通常具有式(5)形式:$$ p(x)=\frac{e{f}_{x}(\mathrm{...})}{Z} $$ (5) 式中:
$ p(x) $ 是第$ x $ 个像元概率值;$ Z $ 是一个确保所有像元概率值之和为1的常数。通过正则化变换后,约束条件可以表达为使式(6)的值最小:$$ \mathrm{ln}{Z}_{\lambda }-\frac{1}{m}{\displaystyle \sum _{{\rm{i}}=1}^{m}{f}_{x{\rm{i}}}(\mathrm{...})}+{\displaystyle \sum _{{\rm{j}}}{\beta }_{\text{j}}\lambda } $$ (6) 式中:m是历史滑坡数目,
$ \lambda $ 是特征函数的一组参数,$ {\beta _{\text{j}}} $ 是任意小的常数,只要确定$ \lambda $ ,使得上式值最小,就可以确定最大熵的概率分布。为计算
$ \lambda $ ,MaxEnt模型采用机器学习中的经典算法连续迭代算法,即初始输入$ \lambda $ =(0,0,0…0),代表每个环境因子作用相同,根据不断调整$ \lambda $ 值,使式(6)值不断变小,直到迭代次数达到用户指定次数,或不在显著变小时停止迭代。此时的$ \lambda $ 值为最终结果中的$ \lambda $ 值。2.3.2 评价模型构建与准确度验证
将滑坡点(44个)地理位置和各环境影响因素数据输入MaxEnt模型中,分别取不同比例的滑坡点数量作为训练数据用于建模,余下滑坡点作为测试数据,进行模型验证。模型结果选用Logistic形式,表示某个滑坡在整个模拟区域每个栅格上的存在概率(p)。在预试验中运行多次,使得出的AUC值相对稳定(±0.001)即可。
选用ROC–AUC值和Kappa一致性检验2种方法对模型精度进行交叉检验。首先,利用模型“Jacknife”中的ROC曲线,对模型模拟的滑坡点潜在分布点预测成效进行评价。其次,通过Kappa值验证模型整体准确度,即通过模型运行结束后的研究区滑坡易发预测图与现状滑坡分布图进行Kappa一致验证。评价指标对应准确度见表1。
表 1 AUC值与Kappa值评价标准表Table 1. Assessment standard of AUC value and kappa value精确度 极差 较差 一般 较好 优秀 AUC 0.5~0.6 0.6~0.7 0.7~0.8 0.8~0.9 0.9~1 Kappa 0~0.2 0.2~0.4 0.4~0.55 0.55~0.7 0.7~1 本次研究采用标准差验证模型模拟结果是否稳定,对不同训练比例构建模型运行多次得出的AUC值进行标准差计算,计算公式为:
$$ \begin{split} \\ {\text{SD}} = \sqrt {\dfrac{{\displaystyle\sum\limits_{i = 1}^N {{{\left( {{X_i} - \bar X} \right)}^2}} }}{{N - 1}}} \end{split}$$ (7) 式中SD为AUC的标准差,N为重复次数,
$ {X_i} $ 为第i次AUC的大小,$ \bar X $ 为N次的AUC平均值,SD值越小,模型模拟结果越稳定。3. 评价结果与分析
3.1 模型运行结果检验
以往研究中,关于模型最适宜训练比例的选取并未达成一致,多数研究者采用训练比例取值范围为70%~90%(Shrestha et al.,2019)。
为研究不同随机训练比例对MaxEnt构建模型准确度的影响,本次研究选取70%、75%、80%、85%和90%作为随机训练样本点数据比例,按照上述步骤模型运行10次,对评价结果进行标准差计算(表2)。从表中可知,滑坡样本点80%时AUC值达到最大值,但样本点为75%时该模型的标准差最小,表明研究区样本点为75%时MaxEnt模型数据最稳定。因此,笔者采用75%训练数据作为模型运行基础,通过模型10次迭代运行后获得的ROC曲线(图3)。从图中可知,训练集10次模拟得到的AUC值能达到0.905,大于0.9,评价精度优秀,证明该模型可以精确模拟灾害点分布与环境因子之间的关系
表 2 AUC均值/SD值与训练比例的关系表Table 2. Relationship between AUC mean value/SD value and training proportion训练样本比例 70% 75% 80% 85% 90% AUC平均值 0.902 0.905 0.909 0.904 0.887 标准差 0.0763 0.0661 0.0855 0.0839 0.0565 3.2 滑坡易发性区划
将模型运行结果提取至ArcGIS,根据Jenks法,将研究区滑坡易发性等级分为5级(图3):低易发(0~0.11)、较低易发(0.11~0.24)、中易发(0.24~>0.43)、较高易发(0.43~0.69)、高易发(0.69~1.00)。易发性等级由高到低,面积分布分别为34.52 km2(4.36%)、45.69 km2(5.77%)、105.16 km2(13.28%)、239.37 km2(30.23%)和367.10 km2(46.36%)。滑坡较高易发区和高易发区主要分布于研究区中部与东部,多位于水系与道路两侧,人口密集分布区域。
将滑坡现状分布与图4中得出的滑坡潜在分布进行
$ {\text{Kappa}} $ 一致性检验,结果显示$ {\text{Kappa}} $ 系数为0.76,表示评价分区与现状滑坡点分布十分符合。3.3 环境变量对地质灾害易发性贡献度分析
采用MaxEnt模型的“Jacknife”模块,可以对每个环境因子进行滑坡易发性贡献率分析(图5),图中深蓝色条块代表无其他环境影响因素干扰下其对易发性分布的贡献率,浅蓝条块即去掉该因子后,其他的所有变量的总贡献率。
图5显示,环境变量贡献大小顺序为:距水系的距离>地貌类型>距道路的距离>高程>岩土体类型>坡向>曲率>坡度。可以看出距水系的距离、地貌类型是贡献度排名前二的因子,距道路的距离、高程和岩土体类型也是影响滑坡易发性的重要因子,而坡度、坡向和曲率对滑坡易发性分布的影响较小。
3.4 环境变量对滑坡易发性指数影响分析
通过
$ {\text{MaxEnt}} $ 模型所生成的环境因子响应曲线(图6),不仅能反映每个环境影响因素与滑坡易发性之间的关联,也可通过每个环境因子变化时对应的易发性分布概率来体现每个环境因子对最终的易发性评价影响。其中,x轴是环境变量的取值范围,y轴是模型的易发性指数。由于无法用曲线来表示2个分类型变量对易发性评价结果的影响规律,因此用矩形面积来表示岩土体类型、地貌类型对易发性结果的影响,面积越大,对易发性评价影响的能力就越高。由高程的响应曲线可以得出(图6a),滑坡易发性于高程850 m左右达到峰值,随后便随高程增加而降低,这是由于研究区居民多居住于海拔较低的河谷平原区,伴随着频繁发生的工程活动;通过坡度的响应曲线来看(图6b),坡度在不断增长时,滑坡易发性随之持续增长;坡向的响应曲线表现为随着坡向取值增大,易发指数呈先增大后减小的趋势(图6c),可能原因为阳坡滑坡发生概率大于阴坡,因为阳坡接受到的阳光和降水更为充沛,岩土体更易风化;从曲率的响应曲线来看(图6d),随着曲率值增大,滑坡易发性随之增大,总体上表现为凸形坡对滑坡易发性的影响大于凹形坡;从距道路的距离的响应曲线来看(图6e),滑坡易发性在距道路800 m范围内时较大,随距道路的距离增加滑坡易发性随之减小,原因就是道路分布的地带由于各种工程活动的破坏,导致环境条件变得恶劣,边坡失稳,灾害频发;距水系的距离曲线与距道路的距离曲线类似,距离水系越近,易发指数越高(图6f);从地貌类型来看(图6g),河谷阶地区对滑坡易发性的影响最大,黄土残塬区次之,土石山地区最小,证明河谷阶地区是最适宜滑坡发育的地貌;从岩土体类型来看(图6h),坚硬层状碎屑岩对易发性影响最低,而砂砾石土对易发性影响最高,这与其易风化、透水性强和工程性质差的性质有关。
4. 结论
(1)基于地质环境条件选取了高程、坡度、坡向、曲率、距道路的距离、距水系的距离、地形地貌、岩土体类型等8个环境影响因子,建立了以铜川市中部王益区、印台区为研究区的评价指标体系。
(2)运用
$ {\text{ArcGIS}} $ 与$ {\text{MaxEnt}} $ 模型对研究区滑坡易发性进行了评价。结果表明,高和较高易发区分别占研究区总面积的4.36%和5.77%。滑坡较高易发区和高易发区主要分布于中部和东部道路、水系两侧,是人口集中分布区域。得到的评价结果与实际滑坡分布情况相符,$ {\text{AUC}} $ 值达到了0.905,表明该模型的评价精度十分优秀,可以用于研究区滑坡易发性评价。(3)使用“Jackknife”模块分析环境影响因素对模型结果影响程度,结果显示距水系的距离和地貌类型是贡献度最大的环境因子;坡向、坡度和曲率贡献度最低,表明研究区滑坡对其变化并不敏感。环境影响因素的响应曲线也揭示了易发性随各个环境影响因素的分布规律,表明该模型对分类环境变量和连续环境变量均有较好的适用性。
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图 1 全球主要萤石矿床分布图(Hayes et al., 2017)
Figure 1. Distribution map of major fluorite deposits around the world
图 2 中国主要萤石矿床分布图(王吉平等, 2014)
Figure 2. Distribution map of major fluorite deposits in China
图 3 萤石矿床成因类型划分(Hayes et al., 2017)
a. 含氟或可能含氟的8种矿物或矿物群;b. 根据构造和岩浆组合对热液萤石矿床进行的简化分类
Figure 3. Genetic classification of fluorite deposit
图 4 中国典型萤石矿床成因模式
a. 内蒙古赤峰地区与花岗岩岩浆热有关的萤石矿床构造背景(Pei et al., 2017);b. 内蒙古赤峰地区与花岗岩岩浆热有关的萤石矿床成因模式图(Pei et al., 2017);c、d. 浙江骨洞坑与次火山岩热液有关的断裂控矿的萤石矿床成因模式图(Fang et al., 2020);e. 黔东北双河与热卤水热液有关的重晶石-萤石矿床成因模式图(李敏等,2021);f. 扬子板块西缘碳酸盐岩地层中似层状产出的与铅锌矿床伴生的萤石矿床(Yu et al., 2022)
Figure 4. Genetic model of typical fluorite ore deposit in China
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