研究论文 正式出版 版本 3 Vol 10 (4) : 335-346 2019
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他念他翁山中段仁措湖地区花岗岩风化晕生长侵蚀模型研究及原地生成宇宙成因核素测年思考
Study on growth and erosion model of granite weathering-rinds (WRs) in Rencuo Lake area in the middle of Taniantaweng Mountains and terrestrial in situ cosmogenic nuclide (TCN) measurement
: 2018 - 09 - 11
: 2018 - 12 - 12
: 2018 - 12 - 21
380 1 0
摘要&关键词
摘要:通过建立他念他翁山流域仁措湖地区的花岗岩风化晕生长模型与侵蚀模型并利用此模型定量了仁措湖地区花岗岩暴露年代。研究结果显示:该地区花岗岩风化晕平均生长速率为4.88 mm/10 ka;平均侵蚀速率为2.15 mm/ka。通过此模型对仁措湖地区的年代进行计算,得出该地区花岗岩风化时间约为(236±88)—(19834±1560) a。结合原地生成宇宙成因核素(terrestrial in situ cosmogenic nuclide,TCN)测年原理,推算出青藏高原花岗岩冰川沉积物至少侵蚀速率分别为:青藏高原东北部为(2.61±0.05) mm/ka、青藏高原东南部为(3.43±0.70) mm/ka、青藏高原中部为(3.42±0.34) mm/ka、喜马拉雅中部为(3.71±0.72) mm/ka、高原西部为(3.14±0.52) mm/ka、喜马拉雅山脉西部为(3.36±0.67) mm/ka、帕米尔高原东部为(3.45±0.59) mm/ka、帕米尔高原西部为(3.11±0.41) mm/ka、天山为(3.63±0.53) mm/ka,恢复后整个青藏高原的TCN测年精度提高了10%左右。
关键词:花岗岩风化晕;生长曲线模型;侵蚀模型;TCN测年;仁措湖
Abstract & Keywords
Abstract: Background, aim, and scope With the advent of terrestrial in situ cosmogenic nuclide (TCN) as a more attractive and complex calibration age tool, weathering-rinds (WRs) provide only a rough age distribution compared to their precise radiation age. However, WRs should not be abandoned as a timing tool, and it can be used as a cosmogenic nuclides chronological correction indicator and sampling indicator to obtain the most accurate cosmological age. We intend to establish a model of granite WRs growth and erosion in the Rencuo Lake area of the Taniantaweng Mountains, and use this model to quantify the granite exposure age in the Rencuo Lake area. The granite WRs growth and erosion model combined with the TCN dating principle is used to quantify the granite glacial sediments at least eroded the rate and estimated the glacial age under the erosion, in order to provide a reference for the accurate dating of glacial deposits in the Taniantaweng Mountains. Materials and methods In order to obtain the most representative WRs thickness of each measuring point, the data of the measuring point are grouped into weighted groups, and the weighted average value is calculated as the WRs thickness of the measuring point. In order to verify the influence of topographic shielding and slope on the thickness of WRs, the shielding coefficient of sampling points and the correlation between slope and thickness of WRs were calculated. Therefore, the growth and erosion model of granite WRs in Rencuo Lake area is established, and the glacial age and erosion rate are measured by TCN dating method. Results Using the growth and erosion model of granite WRs, the growth rate of granite WRs is about 4.88 mm/10 ka and the average erosion rate is 2.15 mm/ka. Discussion The results of 10Be dating in the Qinghai-Tibet Plateau and its surrounding mountainous areas were restored by combining the WRs growth erosion model with the TCN dating theoretical model. At least the average erosion rates are (2.61±0.05) mm/ka, (3.43±0.70) mm/ka, (3.42±0.34) mm/ka, (3.71±0.72) mm/ka, (3.14±0.52) mm/ka, (3.36±0.67) mm/ka, (3.36±0.67) mm/ka, (3.45±0.59) mm/ka, (3.11±0.41) mm/ka, (3.63±0.53) mm/ka, which reduces the uncertainty of the theoretical model assuming that the erosion rate is zero, and provides an external rationalization factor for improving the dating accuracy. Conclusions In this model, calculated by Rencuo Lake area, the area of granite weathering time is about (236±88)—(19834±1560) a BP. The results show that the glacial age of the whole Qinghai-Tibet Plateau has been increased by about 10% on average, and the glacial sediments of the Qinghai-Tibet Plateau have been restored at least according to the results of the previous years. Recommendations and perspectives It is only a preliminary attempt to determine the age and erosion rate of glaciers by combining WRs growth and erosion model with TCN chronology. Further research is needed to solve the problem of TCN dating error and improve its accuracy.
Keywords:  granite WRs; growth curve model; erosion model; TCN dating; Rencuo Lake
风化晕(weathering-rinds,WRs)是岩石和矿物侵变最明显的风化特征之一,以岩石外部显著的化学侵变环和截然不同的视觉差异(Oquchi,2010),使其一直是地貌学基础研究的一部分(Krumbein et al,1981)。二十世纪中叶,美国地质调查局通过野外观测、早期的地理和地质学调查(Blake,1855;Jutson,1914)、以及对岩石周围的生物地球化学变化研究(Hunt,1961;Pretti,2002)发现了风化晕随时间推移,在沉积物表层(Chinn,1981)和次表层(Colman et al,1986)的环境中逐渐变厚的特征,并以此来推断地貌发育速率。近年来的基础研究主要包括:发展通用模型来解释风化晕形成(Oguchi,2010),分析影响风化晕生长速率的因素(Oguchi et al,2000;Sak et al,2000),观察风化晕形成过程与形态(Turkington,1998;Campbell,1999;Matsukura et al,2010;McBride et al,2015;Whalley et al,2001)以及详细分析地球生物的化学变化过程等(Gislason et al,1996;Austin and Vitousek,1998;Etienne,2001)。Černohouz and Šolc(1966)首次提出:气候变化引起的风化晕厚度和形成时间之间的关系是由一个对数函数来表达的。如果风化沉积物使用其他方法进行测年,比如电子自旋共振(electron spin resonance,ESR)、14C等,则沉积物绝对年龄函数可以确定。通过此函数将测量的风化晕的厚度代入来计算地形的年龄。这种方法经常被应用于高山上的冰川沉积物(Birkeland,1973;Porter,1975;Burke and Birkeland,1979;Chinn,1981;Colman,1981,1982a,1982b;Colman et al,1981;Whitehouse et al,1986;Knuepfer,1988;Shiraiwa and Watanabe,1991;Koizumi et al,1992;Koizumi et al,1993;Aoki,1994),这些研究不仅检验了厚度,还研究了风化晕的其他特性。Colman(1981a,1982a)分析了在冰川沉积物中形成的风化晕的化学和矿物学性质。Kuchitsu(1991)确定了在石器上形成的风化晕的矿物成分。松村等人(1994a,1994b)和Oguchi等人(1995)研究了矿物学、化学和机械性能,以及火山气体所改变的风化晕的颜色。在风化晕研究的各种应用中,用作时间指示器占据了主导(Oguchi and Matsukura,2000)。Černohouz and Šolc(1966)研究指出,对数函数最能够描述随着时间的推移风化晕的厚度变化,而表面厚度则与辐射年龄以及校准年龄存在相关性。在多种情况下都可以使用风化晕厚度测年,包括:古代海岸线的定年(Keating et al,2002);将流域阶地与冰河相关联(Pinter et al,1994)和海平面变化(Pazzaglia et al,2001)等地貌类型的研究。基于Etiennes(2002)风化晕的形成过程研究,本文拟建立他念他翁山流域仁措湖地区的花岗岩风化晕生长与侵蚀模型,并利用此模型定量仁措湖地区花岗岩暴露年代,通过花岗岩风化晕生长与侵蚀模型结合原地生成宇宙成因核素(terrestrial in situ cosmogenic nuclide,TCN)测年原理,定量花岗岩冰川沉积物至少侵蚀速率并推算侵蚀作用下的冰川年代,以期为他念他翁山流域冰川沉积物精确测年提供参考。
1   研究区及采样方案
1.1   研究区概况
研究区主要位于他念他翁山流域中段西北侧,距G214-S303道路约8公里的仁措湖地区(96°41′E,30°41′N)(图1)。Chinn(1981)指出影响风化晕厚度的因素有很多,主要包括:气候、岩性、地质条件、地形遮蔽坡度等。Colman(1981)和Chinn(1981)认为,通过采样程序的设计,把非时间变量限制在一个最小的范围内是可能的。为控制变量,把以下因素选取在同一范围:(1)气候区域:风化晕样品均采自昌都地区仁措湖区域,此地气候以寒冷为特点,常年平均气温7—8℃,日照时间长,太阳辐射强,昼夜温差大,年温差小。(2)岩石岩性:前人研究认为,颗粒较粗的火山岩和沉积岩以及浅度的变质岩易于发育和保存风化晕。迄今为止,较多地用于风化晕测定的岩石有砂岩、安山岩和玄武岩。本文采用的均是仁措湖地区普遍出露的二长花岗岩测定风化晕。(3)地质条件:采样地点的选择要考虑到该地貌部位有过长期的稳定历史。本文所有采样地点均位于他念他翁山流域中部,地质条件相对稳定区域。为避免周围山地冰川的影响,采样点选择在远离冰川前进方向的前端外侧位置。


图1   仁措湖地质条件及采样点位置
Figure 1. Geological conditions and sampling points of Rencuo Lake
1.2   采样设计
在采样地点选取直径大于5 cm的花岗岩砾石(图2)。将每块砾石用地质锤击开,利用精度达0.1 mm带有刻度的放大镜测定风化晕厚度。测定风化晕主要参考程绍平和陈国光(1991)的研究方法,通过清晰可见的颜色变化,对每块样品砾石在不同的方向上的风化晕厚度值进行测定,求其平均值代表该砾石的风化晕厚度。测定厚度时要避开:(1)砾石所击开的面并不是大致垂直于砾石外部表面的部位;(2)在砾石被击开过程中使部分风化晕破碎剥落的部位;(3)砾石的外部表面向外凸出的部位。此外,向砾石内部扩散、内部界线模糊不清的风化晕,以及砾石外围厚度不对称的继承性风化晕都不予测定。


图2   花岗岩风化晕研究样品
Figure 2. Studied granite sample with WRs
1.3   采样结果
在仁措湖周边4个区域共采集26个样品(表1),其中采样点RCH-01—RCH-09位于仁措湖东北侧花岗岩基岩山地;RCH-10—RCH-22位于尼它村西侧花岗岩基岩山地,RCH-23采自仁措湖岸边风化壳顶部花岗岩砾石,RCH-24—RCH-26采自尼觉村东北侧道路旁花岗岩风化沉积物。为了减少周围河流和冰川作用对测量结果的影响,采样点选取主要位于仁措湖周围花岗岩基岩山地内侧。为了获得每一个测点最有代表性的风化晕厚度,将该测点的数据分组加权,求其加权平均值作为该测点的风化晕厚。
表1   采样点基本信息及风化晕平均厚度
采样编号Sample number经度Longitude /E纬度Latitude /N海拔高度Altitude /m采样区域
Sampling point
屏蔽系数Shielding factor坡度
Slope/°
平均风化晕厚度WR average thickness /mm
RCH-0196°42′13″30°43′27″4749仁措湖东北侧花岗岩基岩山地
Granite bedrock mountain in the northeast side of Rencuo Lake
0.99923.000.8
RCH-0296°42′15″30°43′14″47120.99920.2012.8
RCH-0396°42′14″30°43′31″47470.99925.7010.5
RCH-0496°42′20″30°43′19″47380.97711.209.8
RCH-0596°42′20″30°43′44″47210.9789.202.6
RCH-0696°42′14″30°43′14″47220.97613.303.3
RCH-0796°42′19″30°43′47″47450.97611.803.2
RCH-0896°42′12″30°43′13″47340.99118.100.8
RCH-0996°42′12″30°43′12″45790.9924.500.3
RCH-1096°41′12″30°41′12″4465尼它村西侧
花岗岩基岩
山地
Granite bedrock mountain on the west side of Nietan Village
0.99290.6
RCH-1196°41′09″30°41′10″44630.9948.001.1
RCH-1296°41′09″30°41′10″44620.99512.001.3
RCH-1396°41′09″30°41′10″44620.9949.000.7
RCH-1496°41′08″30°41′09″44620.9954.001.1
RCH-1596°41′09″30°41′18″44610.99713.000.3
RCH-1696°41′10″30°41′10″44620.97720.000.2
RCH-1796°41′09″30°41′09″44600.98521.901.0
RCH-1896°41′09″30°41′09″44590.98316.205.7
RCH-1996°41′09″30°41′09″44590.98411.601.3
RCH-2096°41′10″30°41′09″44580.98724.801.3
RCH-2196°41′10″30°41′09″44590.9873.000.3
RCH-2296°41′10″30°41′09″44590.9863.000.2
RCH-2396°41′15″30°41′46″4456尼觉村东南侧
风化壳顶部
花岗岩砾石Granite gravel at the top of the weathering crust on the southeast side of Nijue Village
0.9882.701.7
RCH-2496°41′15″30°41′46″4456559乡道旁花岗岩风化沉积物Granite weathering sediments along the 559 township road0.9922.700.2
RCH-2596°41′33″30°42′49″44770.9993.001.7
RCH-2696°41′33″30°42′50″44750.9933.001.4
为验证地形屏蔽和坡度对风化晕厚度的影响,分别对采样点的屏蔽系数进行计算(Li,2013,2018)(表1)以及坡度与风化晕厚度相关性进行分析(图3)。研究结果表明:采样点的地形屏蔽系数基本都在0.9—1,显示了该地区受地形屏蔽影响较小。通过对坡度与风化晕厚度的相关性进行分析,结果显示:风化晕厚度随坡度的增大而增大(图3)。并且95%的个体样品数据满足置信区间要求;风化晕厚度与坡度的相关性较强,相关系数R2=0.801达到了显著水平。


图3   风化晕平均厚度与坡度相关度分析
Fig 3 Analysis of the correlation between the average thickness of WRs and the slope
2   风化晕厚度生长与侵蚀模型
为建立花岗岩风化晕厚度生长曲线模型,对4个采样区域的26个样品点的风化晕厚度数据依据采样区域进行重分类,并基于前人研究经验(Birkeland,1973;Burke and Birkeland,1979;Shiraiwa and Watanabe,1991),对重分类的4个区域的厚度数据进行模拟年代计算(表2)。
表2   花岗岩风化晕生长曲线的年代模拟数据
编号
Numbering
样品类型
Sample type
模拟年代
Correction age /a
风化晕平均厚度
WR average thickness /mm
1花岗岩基岩
Granite bedrock
19834±156011.3
2花岗岩基岩
Granite bedrock
1671±1001..2
3花岗岩砾石
Granite gravel
2850±2561.8
4花岗岩沉积物
Granite sediments
178±540.2


图4   仁措湖地区花岗岩风化晕厚度生长曲线
Fig.4 Granite WR thickness growth curve of Rencuo Lake
Fig.4 Granite WR thickness growth curve of Rencuo Lake
对表2的资料以及表1样品数据通过对数转换和最小二乘法进行回归方程演算,获得仁措湖地区花岗岩风化晕厚度生长曲线(图4)和相应的风化晕厚度与时间关系的公式:
\(\frac{\text{dt}}{\text{dx}}\text{=}\text{3837e}\text{0.266x}\) (1)
式中:t为时间(单位:a);x为风化晕平均厚度(单位:mm)。在这个关系式中,其相关系数R2 =0.991,说明由此式所反映的风化晕厚度与时间之间的关系具有显著的(0.05水平)线性相关。
风化晕厚度取决于生长和侵蚀,基于Etiennes(2002)概念模型关于风化晕的生长和侵蚀原理,Phillips et al(1996)在加州内华达山脉冰川最大的冰碛层的一项子研究发现,对风化晕数据的常规取样程序去除了三分之二的真实厚度。此外,仅以光学颜色为基础测量的风化晕厚度低估了由孔隙度测量的风化晕厚度的1.5倍。因此,利用风化晕厚度生长曲线模型,可以计算风化晕侵蚀速率。本文结合风化晕厚度生长曲线和相应的风化晕厚度和时间关系的经验公式,得出花岗岩风化晕侵蚀速率模型:
\(\text{ε=}\frac{\text{D-x}}{\text{t}}\) (2)
由公式(1)和(2)得出仁措湖地区风化晕平均侵蚀速率模型:
\(\text{ε=340.2x*e}\text{0.266x        }\) (3)
式中:\(\text{ε}\)为风化晕侵蚀速率(单位:mm/a);D为原始风化晕平均厚度(单位:mm)。
运用花岗岩风化晕生长和侵蚀模型,Burke and Birkeland(1979)在内华达山测算的风化晕厚度生长速率为3 mm/10 ka;Shiraiwa and Watanabe(1991)在喜马拉雅山脉测得的速率为4.5 mm/(3650—3000)a;Koizumi and Seki(1992)和Aoki(1994)在日本中部和阿尔卑斯山测得的速率分别为5 mm/20 ka;7.8 mm/3500 a。本研究区花岗岩风化晕速率约为4.88 mm/10 ka,与相关学者先前发表的风化晕生长速率结果基本一致。唐领余等(2004)通过R1孔14C对仁措湖有机质湖泥进行年代测定,年代范围为(290±60)—(18250±1030) a,本研究通过风化晕生长曲线模型测得的年代范围为(236±88)—(19834±1560) a,与其测年范围基本在同一尺度,验证了本模型的有效性。因此,利用平均侵蚀速率模型计算得出仁措湖区域花岗岩风化晕平均侵蚀速率为2.15 mm/ka。
3   TCN测年思考
风化晕的生长与侵蚀并不是一个新的概念,Etiennes(2002)概念模型显示:风化晕厚度是其生长和侵蚀动态平衡作用的结果(Černohouz and Šolc,1966;Colman et al,1981)。随着宇宙成因核素测量作为一个更有吸引力的复杂的校准年龄工具的出现,与其精确的辐射年龄相比,风化晕只是提供了粗略的年龄分配。但是,风化晕作为计时工具不应该被抛弃,而且它可以作为宇宙成因核素年代校正指标和采样指示器,用来更大限度地获得最精确的宇宙核素年龄(Dorn et al,1991;Phillips et al,1998;Fabel et al,1999)
(1)作为TCN年代矫正指标:结合风化晕生长侵蚀模型和TCN测年理论(Lal,1991,Bierman,1994,Nishiizumi et al,1991),根据Lal(1991)提出的地表岩石宇宙成因核素累计量方程,通过引入风化晕侵蚀过程作为变量,对已知TCN测年年代数据样品进行风化晕侵蚀厚度恢复,并根据此厚度推演出TCN测年样品原始表面浓度。通过CRONUS-calc网络计算模型(Balco et al,2008),对恢复后的浓度进行年代重测定。参考Heyman(2014)按10Be年代数据分布位置将青藏高原进行区域划分的方法,将青藏高原及周边山地划分成9个区域,分别是:青藏高原东北部、青藏高原东南部、青藏高原中部、喜马拉雅中部、高原西部、喜马拉雅山脉西部、帕米尔高原东部、帕米尔高原西部及天山,本研究基于邢春雷(2017)收集和整理的国内外2001—2018年发表的近6000个青藏高原及周边山地10Be年代数据进行年代恢复和至少侵蚀速率计算,共计收集了44个分布区域,其中详细记录TCN暴露测年采样环境(海拔高度、经纬度、采样厚度、样品遮蔽度)及测试参数(10Be浓度、测试标准、误差)的文献共计29篇。本文使用文献中10Be年代数据461个(表3)。样品类型包括:漂砾、基岩、小砾石、羊背石及冰碛垄表面碎屑沉积物,其中以漂砾为主(计算岩石侵蚀速率所需岩石密度、平均吸收自由程、暴露年代、衰变常数等参数均来自参考文献中对应的数据)。运用风化晕生长侵蚀模型结合TCN测年相结合研究结果显示:(Ⅰ)根据已有年代结果恢复出青藏高原冰川沉积物至少侵蚀速率,青藏高原东北部平均至少侵蚀速率为(2.61±0.05) mm/ka、青藏高原东南部为(3.43±0.70) mm/ka、青藏高原中部为(3.42±0.34) mm/ka、喜马拉雅中部为(3.71±0.72) mm/ka、高原西部为(3.14±0.52) mm/ka、喜马拉雅山脉西部为(3.36±0.67) mm/ka、帕米尔高原东部为(3.45±0.59) mm/ka、帕米尔高原西部为(3.11±0.41) mm/ka及天山为(3.63±0.53) mm/ka,(Ⅱ)恢复后,整个青藏高原的冰川年代结果平均提高了10%左右。
表 3   青藏高原10Be年代与恢复后年代结果对比
研究区域
Study area
样本数量Number of samples最小暴露年代Minimum exposure age(ka)最大侵蚀速率Maximum erosion rate(mm/ka)恢复后暴露年代
Post-recovery exposure(ka)
至少侵蚀速率Minimum erosion rate(mm/ka)文献
References
达里加山
Dariga
2211.56±1.03-
52.96±4.70
11.18±0.91-
51.21±4.19
14.72±0.27-
60.38±0.14
4.89±0.79-
1.28±0.28
Jie Wang et al (2013)
巴颜喀拉山
Bayan Kala
5811.0±1.1-
128.7±11.6
4.33±0.84-
53.6±10.88
15.13±1.57-
131.25±15.36
4.80±0.81-
1.21±0.75
Heyman et al (2011)
柴达木盆地南缘
Southern margin of Qaidam Basin
230.7±4.4-
66.7±10.3
8.8±1.4-
19.4±2.8
30.80±2.36-
68.6±11.83
2.38±0.42-
1.12±0.24
Lal et al(2003)
折多山
Zedo Mountains
513.0±0.6-
15.7 ± 0.7
36.7±3.31-
44.46±4.14
15.72±1.07-
15.40±0.98
4.57±0.73-
4.38±0.46
Strasky et al(2009)
横断山东部
Hengduanshan East
3712.9±1.2-
183.6±17.0
6.43±0.6-
45.14±8.63
15.36±0.92-
188.66±12.53
4.67±0.74-
0.91±0.59
Fu et al(2013)
东昆仑山垭口地区
Eastern Kunlun Pass area
438.2±3.5-
81.7±7.4
6.98±1.6-
15.24±1.46
42.2±3.63-
88.87±6.89
2.75±0.40-
1.68±0.20
Yixin Chen(2011)
唐古拉山
Tanggula
2533.8±1.28-
215.37±2.81
2.48±0.04-
17.23±0.66
38.18±1.34-
236.38±1.78
4.57±0.39-
3.13±0.09
Owen et al(2005)
羌塘
Qiangtang
1020.2±1.0-
165.9±2.7
3.3±0.1-
29.1±1.5
22.12±2.3-
183.35±3.35
4.92±0.55-
3.20±0.12
Lal et al(2003)
念青唐古拉山东段
Nyainqêntanglha East
1517.4±1.6-
35.5±3.2
16.42±3.03-
33.78±6.32
20.02±2.3-
38.05±1.57
4.18±0.61-
2.90±0.37
Dong GuoCheng et al(2014)
The Naisa valley2211-1.93.44-70.2712.1-1.23.86-0.81Schaefer et al(2008)
朗塘
Langtang
321.23±0.44-
40.14±0.99
14.49±0.73-
27.64±1.16
23.55±0.15-
44.19±1.23
4.17±0.54-
4.93±0.34
Barnard et al(2006)
安纳普尔纳峰
Annapurna
612.4±2.15-
5.6±5.9
10.38±2.29-
47.52±16.67
13.52±3.46-
6.22±3.25
3.62±0.75-
1.96±1.14
Zech et al(2006)
纳木那尼峰
Namunani
3911.0±0.2-
54.5±1.3
10.64±0.26-
53.6±0.98
13.1±0.22-
60.93±3.23
4.64±0.81-
2.54±0.27
Owen et al(2010)
印度-喜马拉雅中部-加瓦尔南部
India - Central Himalaya - South of Gavar
610.96±1.03-
15.65±5.01
36.91±26.52-
52.82±10.07
12±2.14-
16.88±1.79
4.88±0.81-
2.19±0.65
Scherler et al(2010)
the Puga and Karzok valleys1312.53±1.48-
54.76±4.86
10.55±0.97-
47.02±5.67
13.76±1.88-
61.02±3.98
4.32±0.75-
2.23±0.27
Hedrick et al(2011)
拉达克北部
North Ladakh
1224.5±1.9-
48.6±4.1
11.92±2.08-
23.91±2.38
26.98±2.35-
53.47±6.25
3.86±0.49-
2.49±0.30
Dortch et al(2010)
昆仑山西部
West Kunlun Mountains
723±7-
51±4
11±1-
24±7
26.01±6-
55.89±6
1.74±0.51-
2.53±0.29
Kong et al(2007)
The Nun-Kun massif2411.1±1.0-
56.7±5.1
10.18±0.95-
53.11±4.85
13.14±2.12-
61.06±3.6
4.92±0.81-
2.16±0.27
SU YOUNG LEE et al(2014)
Lahul-Himalaya1910.5±0.3-
16.4±0.4
35.20±0.87-55.14±1.5811.25±3.6-
17.62±3.24
1.32±0.83-
3.69±0.63
Owen et al(2011)
加瓦尔
Gavar
2311.36±1.03-
20.11± 1.87
30.4±5.6-
52.54±9.5
12.12±2.28-
22.35±1.89
4.88±0.79-
3.89±0.56
Scherler et al(2010)
the upper Bhagirathi Valley211.13±0.31-
15.32±0.46
40.51±2.41-
56.93±3.11
12.41±2.39-
15.98±2.53
1.67±0.80-
4.35±0.66
Barnard et al(2006)
喀什地区-南部塔什库尔干谷
Kashgar - southern Tashkur valley
612.6±0.5-
15.0±0.4
39.23±2.1-
46.76±3.74
12.96±2.65-
16.34±1.78
4.63±0.75-
3.69±0.67
Owen et al(2012)
新疆喀什地区—库兹滚谷
Kashgar - Kutz Valley
915.3±1.5-
26.3±7.1
21.85±5.47-
37.76±14.14
16.32±3.65-
29.02±6.98
4.29±0.66-
1.73±0.46
墓士塔格山
Tomb Tagg Mountain
3110.3 ± 0.3-
53.1 ± 1.4
10.87±0.49-
60.19±2.46
11.35±2.56-
58.29±2.36
1.32±0.84-
4.41±0.28
Yeong et al(2009)
the Bogchigir Valleys1411.2±0.5 -
51.2±3.2
11.3±1.45-
52.64±2.19
12.53±4.36-
56.85±1.87
4.63±0.80-
2.90±0.29
Röhringer et al(2012)
the Gissar Range820.2±1.8-
54.5±4.8
10.6±0.97-
29.06±2.63
23.46±1.23-
59.62±3.02
3.96±0.55-
2.25±0.27
Zech et al(2013)
Kitschi-Kurymdu1214.0±1.4-
56.1±5.3
1.22±0.14-
5.57±0.59
16.34±8.76-
63.17±6.53
4.41±0.70-
2.11±0.27
Zech et al(2012)
Inylchek Valley1615.2±0.9-
160.3±9.5
3.42±0.23-
38.72±2.36
16.25±3.87-
178.33±2.34
5.02±0.67-
1.40±0.12
Lifton et al(2014)
Urumqi River1110.1±1.0-
20.9±1.9
28.08±5.2-
58.4±11.73
11.45±5.65-
23.53±5.32
4.92±0.85-
3.86±0.54
Kong et al(2009)
(2)作为TCN测年采样指示器,Gordona(2005)研究的第一步就涉及到对风化晕侵蚀的发生和程度的评估。在土壤环境中,其建立了土壤中自然条件下近似大小的土壤侵蚀,与在地表环境下的风化晕侵蚀的性质进行了对照试验。在这些环境中,岩石样品被取样用于宇宙的年代测定。Phillips(1996)在内华达山获取了36个26Cl年代,得出风化晕形成的开始时间。Liu(2003)和Marston(2003)利用在风化晕上的清漆微层序列,获得了最后一次风化晕侵蚀事件发生的时间。研究结果表明,超过90%的漂砾表面经历了足够的侵蚀,这在一定程度上使得我们对宇宙成因核素年代法设置中使用的侵蚀速率为零时的侵蚀性年龄存在疑问。同样的,Etiennes(2002)研究显示了异常厚度的风化晕,暗示了二元性风化和二元性核素积累的可能性。因此,将风化晕作为一种计时工具的统计异常值的趋势可为样本是否适合用于宇宙起源的核素测定提供关键测试。
4   结论
通过对仁措湖地区样品采集及数理分析,建立起该地区的花岗岩风化晕厚度生长与侵蚀模型,并得出该地区风化晕平均生长速率为4.88 mm/10 ka;平均侵蚀速率为2.15 mm/ka。通过此模型对仁措湖地区的年代进行计算,得出该地区花岗岩风化时间约为(236±88)—(19834±1560) a BP。
运用风化晕生长侵蚀模型与TCN测年理论模型相结合,对青藏高原及其周边山地10Be年代结果进行恢复,研究表明:利用此模型,整个青藏高原的冰川年代结果平均提高了10%左右,根据已有年代结果恢复出青藏高原冰川沉积物至少侵蚀速率,青藏高原东北部平均至少侵蚀速率为(2.61±0.05) mm/ka、青藏高原东南部为(3.43±0.70) mm/ka、青藏高原中部为(3.42±0.34) mm/ka、喜马拉雅中部为(3.71±0.72) mm/ka、高原西部为(3.14±0.52) mm/ka、喜马拉雅山脉西部为(3.36±0.67) mm/ka、帕米尔高原东部为(3.45±0.59) mm/ka、帕米尔高原西部为(3.11±0.41) mm/ka及天山为(3.63±0.53) mm/ka,这在一定程度上降低了理论模型假定侵蚀速率为零计算年代的不确定性,给提高测年精度提供了一个外在合理化因素。
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稿件与作者信息
张 威
ZHANG Wei
李亚鹏
LI Yapeng
lyplnnu@163.com
柴 乐
CHAI Le
乔静茹
QIAO Jingru
唐倩玉
TANG Qianyu
国家自然科学基金项目(41671005,41271093,41270743)
National Natural Science Foundation of China (41671005, 41271093, 41270743)
出版历史
出版时间: 2018年12月21日 (版本3
参考文献列表中查看
地球环境学报
Journal of Earth Environment