Tectono-Thermal Evolution of Late Mesozoic Extensional Granite Domes in Southern Mongolia and the Sino-Mongolian Border Region: Constraints from Low-Temperature Thermochronology
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摘要:
南蒙古-中蒙边界地区是东北亚伸展构造发育的重要区域,晚中生代花岗岩穹隆的形成和热演化对于理解区域伸展机制和动力学具有重要意义。本文采用磷灰石裂变径迹和锆石(U-Th)/He等低温热年代学方法,结合40Ar-39Ar热年代学数据对南蒙古及邻区的罕乌拉、Nartyn和Altanshiree三个花岗岩穹隆的构造热演化过程进行分析,结果显示三个穹隆韧性剪切带的磷灰石裂变径迹年龄为104.94±5.84 Ma、101.30±5.5 Ma、
1101.73 ±6.20 Ma、110.05±7.38 Ma,锆石(U-Th)/He年龄为123.4±7.35 Ma、123.7±7.42 Ma,黑云母40Ar-39Ar年龄为123.9±0.90 Ma、121.3±1.40 Ma,钾长石40Ar-39Ar年龄为122.2±1.20 Ma、104.94±5.84 Ma。热史模拟结果表明,晚中生代三个穹隆均经历了明显的隆升,根据隆升速率的差异可分为三个阶段:①133~125 Ma为中等速度隆升;②125~123 Ma为快速隆升;③123~100 Ma为缓慢隆升。穹隆隆升过程与区域性岩浆作用及伸展构造活动紧密相关:早期岩浆开始侵入并加热地壳,导致地壳岩石圈强度降低,诱导穹隆隆升,后期区域性拉张背景促使穹隆快速隆升,此外,隆升活动与东北亚早白垩世其他典型变质核杂岩伸展事件具有一致性。蒙古鄂霍茨克洋闭合后垮塌引发的岩石圈伸展与古太平洋板块俯冲后回撤可能共同驱动了穹隆的隆升。Abstract:The South Mongolia–Central Mongolia border region is a significant area for the development of extensional tectonics in Northeast Asia. The formation and thermal evolution of Mesozoic granite domes in this region provide key insights into the mechanisms and dynamics of regional extension. This study combines low-temperature thermochronology techniques, including apatite fission-track dating, zircon (U-Th)/He dating and 40Ar-39Ar dating to analyze the tectonothermal evolution of three granite domes—Hannuula, Nartyn, and Altanshiree—across South Mongolia and adjacent areas. The results indicate that the ages of apatite fission-track dating from the ductile shear zones of these domes are 104.94 ± 5.84 Ma, 101.30 ± 5.5 Ma, 110.73 ± 6.20 Ma, and 110.05 ± 7.38 Ma. The zircon (U-Th)/He ages are 123.4 ± 7.35 Ma and 123.7 ± 7.42 Ma, while biotite 40Ar-39Ar ages are 123.9 ± 0.90 Ma and 121.3 ± 1.40 Ma and feldspar 40Ar-39Ar ages are 122.2 ± 1.20 Ma and 104.94 ± 5.84 Ma. Thermal history modeling reveals that all three domes underwent significant uplift during the Late Mesozoic with three distinct uplift phases: (1) moderate uplift between 133 and 125 Ma, (2) rapid uplift from 125 to 123 Ma, and (3) slow uplift from 123 to 100 Ma. The uplift of the domes is closely linked to regional magmatism and extensional tectonics. Early magmatic intrusions heated the crust, reducing lithospheric strength and inducing dome uplift, while regional extension during the later stages promoted rapid uplift. Furthermore, the uplift events are consistent with other extensional events associated with metamorphic core complexes in Northeast Asia during the Early Cretaceous. Lithospheric extension triggered by the collapse of the Mongol-Okhotsk Ocean and the rollback of the subducted Pacific Plate likely played a key role in driving the uplift of these granite domes.
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气候变暖是目前世界上最主要的环境问题之一,减少碳排放和增加区域生态系统碳汇是控制气候变暖的主要措施。农田生态系统作为陆地生态系统的重要组成部分,对区域碳循环起着至关重要的作用。农田生态系统既是碳源也是碳汇,准确估算农田生态系统碳变化是科学制定农业减排增汇战略的前提和基础(赵宇,2018)。
以往研究中,净碳汇法是估算农田生态系统碳汇的常见方法,如王桂波等(2012)、康霞(2018)、翁领燕等(2018)利用该方法分别对陕西省耕地、甘肃省和江苏省农田生态系统碳源汇测算,并分析其时空变化;李艳春等(2014)、茹艺(2015)、王莉等(2022)分别分析福建省、黑龙江省和河南省农田生态系统的碳源汇时空变化及其影响因素;王敬哲等(2016)、李明琦等(2018)和郭永奇(2021)分别测算新疆、云南省和河南省农田生态系统碳源汇,并分析碳足迹变化。然而农田生态系统固碳潜力主要集中在农田土壤(赵明月等,2022),一些学者也通过自建经验公式、固碳速率法测算农田土壤碳汇量,如韩冰等(2008)通过建立公式估算农田土壤的固碳能力和潜力。李彦娥等(2023)、邱子健等(2024)采用固碳速率法分别对西北地区、江苏省农田土壤生态系统碳汇量进行核算。谭美秋等(2022)发现固碳速率法测算的河南省农田土壤与净碳汇法核算的农田生态系统碳汇结果呈现相似规律。上述农田碳汇核算研究主要集中于单一方法的测算,多方法对比研究缺乏,无法筛选出精准评估区域农田生态系统碳汇的适宜方法。
陕西省地处西北地区,其耕地占全省土地总面积的19.34%,准确估算其农田生态系统碳汇是实现陕西省“双碳”目标的重要战略选择。笔者采用净碳汇法和固碳速率法分别核算陕西省农田生态系统碳汇,并分析两种方法的固碳时空分布差异,筛选出评估区域农田生态系统碳汇的适宜方法,为陕西省农田碳减排管理政策提供参考和技术支撑。
1. 数据来源
本研究中涉及到的农田面积、主要作物产量、化肥施用量、农用物资使用量、农田翻耕面积、灌溉面积等均来源于2005~2020年的《陕西省统计年鉴》(htts://tjj.shaanxi.gov.cn),其中化肥为折纯量。
2. 研究方法
2.1 净碳汇法
净碳汇是指各种农作物在全生育期过程中碳吸收量与自然生长排放及农业投入产生的碳排放量的差值。
$$ {C_{\text{t}}} = {E_t}{\text{ - }}{T_t} $$ (1) 式(1)中:$ {C_{\text{t}}} $为农田生态系统碳汇,tC;$ {E_{\text{t}}} $为碳吸收量,tC;$ {T_{\text{t}}} $为碳排放量,tC。
2.1.1 碳吸收量估算
$$ {E_{\text{t}}} = \sum\limits_{i = 1}^n {{E_i}} = \sum\limits_{i = 1}^n {{C_i}} \times {Y_i} \times {W_i}/{H_i} $$ (2) 式(2)中:$ {E_i} $为i类农作物碳吸收量,tC;$ {C_i} $为i类农作物碳吸收率;$ {Y_i} $为i类农作物经济产量,t;$ {W_i} $为作物干重比;$ {H_i} $为i类农作物经济系数。碳吸收率、干重比、经济系数主要参考前人研究资料(郭永奇,2021),具体见表1。
表 1 主要农作物的经济系数、干重比、碳吸收率Table 1. Economic coefficient, dry weight ratio and carbon absorption rate of main crops作物种类 经济系数 干重比 碳吸收率 作物种类 经济系数 干重比 碳吸收率 水稻 0.41 0.86 0.45 棉花 0.45 0.92 0.10 麦类 0.49 0.87 0.40 麻类 0.45 0.88 0.15 玉米 0.47 0.86 0.40 烟叶 0.45 0.84 0.55 豆类 0.45 0.87 0.35 其他谷物 0.45 0.83 0.35 油料 0.45 0.90 0.25 蔬菜 0.83 0.15 0.45 2.1.2 碳排放量估算
农田碳排放主要包括农用物资投入引起的碳排放、土壤N2O排放、稻田CH4排放及稻田土壤呼吸4方面。其中1 t N2O 和1 t CH4所引发的温室效应相当于
81.2727 tC和6.8182 tC 所产生的温室效应(谭美秋等,2022)。碳排放量估算:
$$ {T_{\text{t}}} = \sum\limits_{i = 1}^n {{T_i}} = \sum\limits_{i = 1}^n {{Q_i}} \times {R_i} $$ (3) 式(3)中:$ {T_i} $为第i类碳排放量,tC;$ {Q_i} $为第i类碳排放源数量;$ {Q_i} $为第i类碳源的碳排放系数。
① 农用物资投入引起的碳排放主要包括化肥、农药、农膜、农用柴油、农田翻耕和农田灌溉6方面,对应的碳排放系数见表2。
② 土壤N2O排放
农作物种植过程中易对土壤表层产生破坏,从而导致大量温室气体流失到大气中,尤以N2O最为突出。各类农作物土壤N2O排放系数(谭美秋等,2022)见表3。
表 3 各类农作物土壤N2O排放系数Table 3. N2O Emission Coefficients of Various Crop Soils农作物种类 N2O排放系数
(Kg/km2)农作物种类 N2O排放系数
(Kg/km2)水稻 24 玉米 253 麦类 205 蔬菜 421 豆类 77 其他旱地作物 95 ③ 稻田CH4排放
农田生态系统CH4排放主要来源于稻田种植。根据以往研究结果(田云等,2013),陕西省水稻属中季稻,CH4排放系数取12.51 g/m2。
④ 稻田土壤呼吸
土壤呼吸主要为稻田CO2排放,稻田土壤呼吸的碳排放系数为
1023 t/km2(吴贤荣等,2014;谭美秋等,2022)。2.2 固碳速率法
根据《中华人民共和国国家标准——生态系统评估生态系统生产总值(GEP)核算技术规范》(GB/T 1.1-2020),本研究只考虑农田的土壤碳汇,不考虑农田植被的碳汇,也就是农田生态系统的净碳汇量,其计算公式为:
$$ CSCS = \left( {BSS + SCS{R_n} + PR \times SCS{R_s}} \right) \times SC $$ (4) 式(4)中:CSCS为农田碳汇量,tC/a;BSS为无固碳措施下固碳速率,tC/(km2·a);$ SCS{R_n} $为施用化肥固碳速率,tC/(km2·a);$ SCS{R_s} $为秸秆还田固碳速率,tC/(km2·a);PR为秸秆还田率;SC为农田面积( km2)。
$$ BS S = NSC \times BD \times H \times 0.1 $$ (5) 式(5)中:NSC为土壤有机碳的变化;BD为土壤容重g/cm3;H为土壤厚度,取20 cm。
$$ SCS{R_n} = 0.635\;2 \times TNF - 1.083\;4$$ (6) 式(6)中:TNF为单位面积耕地化学氮肥、复合肥总施用量kg/(km2·a)
$$ TNF = {{\left( {NF + CF \times 0.3} \right)} \mathord{\left/ {\vphantom {{\left( {NF + CF \times 0.3} \right)} {{S_p}}}} \right. } {{S_p}}} $$ (7) 式(7)中:Sp为耕作面积,km2;NF和CF为化学氮肥和复合肥施用量,t。
$$ SCS{R_n} = 17.116 \times S + 30.553 $$ (8) 式(8)中:S为单位面积秸秆还田量t/( km2·a)。
$$ S = {{\sum\nolimits_{j = 1}^n {{C_{yj}} \times SG{R_j}} } \mathord{\left/ {\vphantom {{\sum\nolimits_{j = 1}^n {{C_{yj}} \times SG{R_j}} } {{S_p}}}} \right. } {{S_p}}} $$ (9) 式(9)中:$ {C_{yj}} $为作物j在当年的产量,t;$ SG{R_j} $为作物j的草谷比,$ {S_p} $为耕作面积。
表 5 不同作物的草谷比Table 5. Grass to Grain Ratio of Different Crops作物 草谷比 作物 草谷比 水稻 0.623 油料 2.0 麦类 1.366 棉花 8.1 玉米 2.0 豆类 1.57 薯类 0.5 麻类 8.10 烟叶 1.0 其他谷物 0.85 3. 结果与分析
3.1 净碳汇法计算结果
从2020年陕西省农田生态系统碳排放情况(表6)可知,全省农田生态系统碳吸收明显大于碳排放,整体呈碳汇状态,净碳汇达507.66万tC,碳汇强度131 tC/km2。这与王桂波等(2012)采用该方法核算的结果基本一致。碳吸收贡献由大到小依次为玉米、麦类、蔬菜、其他谷物、稻谷、油料、豆类、烟叶、麻类和棉花,其中玉米、麦类和蔬菜碳汇贡献率分别达40.15%、26.26%和14.11%。玉米和麦类为区域主要粮食作物,种植面积大,碳汇能力强。蔬菜作为农民创收的主要来源,种植面积大,其碳汇能力优于其他农作物。碳排放贡献由大到小依次为化肥、稻田土壤呼吸、土壤硝化、翻耕、柴油、灌溉、农膜、稻田甲烷排放和农药,其中化肥、稻田土壤呼吸、土壤硝化、翻耕影响较大,贡献率分别达27.97%、17.32%、17.11%和16.76%。
表 6 2020年陕西省农田生态系统碳吸收、碳排放核算表Table 6. Accounting table for carbon absorption and emissions of farmland ecosystems in Shaanxi Province碳吸收量(万t) 碳排放量(万t) 碳汇量(万t) 稻谷 75.20 农资投入 化肥 173.56 麦类 296.33 农药 5.90 玉米 453.04 农膜 23.17 豆类 20.04 柴油 55.69 油料 29.64 翻耕 104.01 棉花 0.01 灌溉 35.62 麻类 0.02 自然排放 土壤N2O排放 106.18 烟叶 5.43 稻田CH4排放 8.96 其他谷物 89.34 稻田土壤呼吸 107.51 蔬菜 159.21 小计 1128.26 620.60 507.66 全省碳吸收量分布(图1)显示,渭南最高(213.81万tC),榆林次之(178.72万tC),接着咸阳、西安、汉中和宝鸡,其余均低于100万tC。碳吸收能力的差异主要与不同区域农作物种类、产量和播种面积有关,中部地区主要以玉米和麦类种植为主,占比达75%以上;陕北主要以玉米为主,其他谷物和蔬菜次之;陕南差异较大,汉中以稻谷、玉米和蔬菜为主;商洛主要为玉米、麦类、蔬菜和其他谷物;安康则主要为玉米、稻谷和蔬菜。
全省碳排放量分布情况(图2)显示,汉中最高(127.06万tC),渭南次之(113.93万tC),咸阳第三(81.95万tC),铜川最低(10.62万tC)。各地碳排放量组成结构有所差异,除汉中、安康、榆林外,其他地区均以化肥碳排放最多;汉中和安康则以稻田土壤呼吸最多;榆林以土壤翻耕和硝化为主。
空间分布来看(图3),除汉中外其他各地市农田均表现为碳汇状态,碳汇高值区域主要分布在榆林、渭南、西安、咸阳、宝鸡;低值区域主要分布于商洛、安康、延安、铜川;汉中处于碳源状态。由于不同行政区域农田面积相差较大,相比碳汇总量,其碳汇强度即单位面积内的碳汇量更能客观反映其碳汇水平。全省碳汇强度整体呈现“中间高,南北低”的分布格局,西安最高(238 tC/km2),汉中最低(−4 tC/km2),由大到小依次为西安、宝鸡、铜川、咸阳、渭南、榆林、商洛、延安、安康和汉中。安康和汉中碳汇强度最低,这与区域水稻种植面积较大,导致稻田土壤呼吸碳排放较多所致。
3.2 固碳速率法计算结果
固碳速率法计算得出2020年陕西省农田生态系统碳汇为76.51万tC,碳汇强度25 tC/km2,其中66.6%来自施用氮肥和复合肥导致的碳吸收,27.3%来自秸秆还田带来的碳封存,6.1%来自无措施的土壤固碳。
空间分布来看(图4),碳汇高值区域主要分布在渭南、咸阳、榆林、西安、宝鸡、延安;低值区域主要分布于安康、铜川、商洛。全省碳汇强度整体呈现“中间高,南北低”的分布格局,咸阳最高(42 tC/km2),榆林最低(13 tC/km2),由大到小依次为咸阳、西安、铜川、延安、渭南、宝鸡、汉中、安康、商洛和榆林。各地市碳汇组成结构有所差异,但均以施用化肥碳汇为主(48.5%~82.3%),其次为秸秆还田碳汇(13.0%~43.0%),无措施碳汇占比较少(3.6%~12.1%)。各地市碳汇组成结构差异,主要与区域经济投入、作物产量、农田管理措施有关。
3.3 两种方法结果对比分析
3.3.1 时间分布
从图5碳汇总量看,采用净碳汇核算的陕西省农田生态系统碳汇(452.81~507.66万tC)远大于固碳速率法核算的结果(58.83~82.28万tC),固碳速率法核算值仅占净碳汇核算值的12.8%~18.2%。
从时间分布看,净碳汇法得出2005~2020年碳汇总量整体呈波动上升趋势,而固碳速率法表现为先上升后下降态势,其中净碳汇法在2020年达到峰值,而固碳速率法在2015年最高。碳汇强度方面净碳汇法表现为持续上升态势,而固碳速率法表现为先上升后下降态势。
造成两者碳汇量及时空变化趋势差异的主要原因是形成碳汇的结构组成不同。净碳汇法除受作物产量因素外,还与农资投入、作物种类、土壤呼吸等因素有关;而固碳速率法主要与施肥、秸秆还田因素有关。
3.3.2 空间分布
从空间分布看,全省碳汇强度整体均是“中间高,南北低”的分布格局。净碳汇法整体呈现中部>北部>南部,而固碳速率法则表现为中部>南部>北部。究其原因,净碳汇法碳排放考虑稻田土壤呼吸及甲烷排放,从而使南部稻田种植面积较大的汉中、安康净碳汇量减少。这与王桂波、康苗苗等未考虑稻田土壤呼吸得出的结果一致(康苗苗等,2011;王桂波等,2012)。研究认为净碳汇法更适用于区域有水稻种植的农田生态系统碳汇的核算。
各地市碳汇强度两种方法得出的结果有所差异,净碳汇法中西安和宝鸡最高,汉中最低;而固碳速率法中咸阳和西安最高,榆林最低。净碳汇法中碳吸收主要考虑作物全生育期的碳吸收,以玉米、麦类和蔬菜贡献率为主;碳排放主要影响因素为化肥、稻田土壤呼吸、土壤硝化、翻耕等。说明净碳汇法中区域耕地数量和质量决定着其农田生态系统的碳汇能力,西安和宝鸡地处关中平原,土壤肥沃,碳汇强度高,而汉中水稻种植面积大、土壤呼吸碳排放量高导致其碳汇强度小。固碳速率法中主要以施用化肥碳汇和秸秆还田碳汇为主,说明其碳汇能力主要取决于农田经济投入和耕地质量。咸阳、西安经济发达,土壤肥沃,其碳汇强度高;榆林地处黄土高原与毛乌素沙地过渡带,土壤贫瘠,耕地撂荒现象普遍,加之,该地区人口密度低,能源资源丰富,相对来说农业经济投入较少,导致其碳汇强度低。
4. 讨论
净碳汇法是估算农作物全生育期碳吸收与碳排放的差值,从整个农田生态系统产出平衡来考虑。该方法分别计算农田生态系统的碳源和碳汇,可清晰识别其碳排放和碳吸收的各项具体情况,剖析碳源和碳汇的内在原因,但涉及核算数据多,计算过程复杂。固碳速率法可直接计算农田土壤生态系统碳汇,涉及数据少,但其碳汇能力大小主要取决于化肥施用量、秸秆还田率及其土壤本身的固碳率。土壤固碳率与土壤有机碳变化有关,本研究过程中受数据限制土壤有机碳变化采用全国的平均值,秸秆还田率采用2011年全省平均值42.6%,各地区土壤固碳率差异性被显著降低。
本研究采用固碳速率法核算的全省平均碳汇强度25 tC/km2·a,核算值偏低主要未考虑免耕因素和秸秆还田率偏低等原因。金琳等(2008)得出西北地区免耕措施固碳强度为91 tC/km2·a,韩冰等(2008)研究采用免耕措施,全国平均农田土壤固碳强度为28 tC/km2·a。受数据限制,秸秆还田率采用2011年数据(42.6%)核算的碳汇强度为7 tC/km2·a。近些年随着秸秆禁止焚烧、秸秆综合利用等措施的实施,秸秆还田率已有所提高。谭美秋等(2022)采用秸秆还田率61%核算的固碳强度为27 tC/km2·a。故考虑免耕因素及秸秆还田率提高的条件下,按最小值估算,本研究采用固碳速率法核算的碳汇强度可能达到73 tC/km2·a,尽管与净碳汇法核算的131 tC/km2仍有一定差异,但核算结果单位基本一致,有一定可比性。
稻田种植是农田生态系统CH4排放的主要来源(王莉等,2022),陕南部分地区水稻种植面积大,由稻田种植引起的CO2排放占比较高,其碳排放实际情况与净碳汇法的测算结果更相符,而固碳速率法未考虑这部分因素,故净碳汇法目前更适用于核算陕西省农田生态系统碳汇。
5. 结论
(1)净碳汇法和固碳速率法核算值整体均呈碳汇状态,前者核算结果(452.81~507.66万tC)远大于后者核算结果(58.83~82.28万tC),后者核算值仅占前者核算值的12.8%~18.2%。
(2)两种方法碳汇结构的组成差异导致两者的时空变化趋势有所不同。时间序列上,净碳汇法2005~2020年碳汇总量整体呈波动上升趋势,而固碳速率法表现为先上升后下降态势。空间格局上,净碳汇法中西安和宝鸡最高,汉中最低,整体表现为中部>北部>南部;而固碳速率法中咸阳和西安最高,榆林最低,整体表现为中部>南部>北部。
(3)净碳汇法目前更适用于核算陕西省农田生态系统碳汇,2020年其值为507.66万tC。但考虑免耕和秸秆还田率提高等因素,两种方法核算结果单位基本一致,具有可比性。今后应加大农田土壤有机碳变化监测力度,加强农田管理措施碳汇研究,对于固碳速率法精准评估农田土壤生态系统碳汇极其重要。
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图 1 东北亚大地构造简图(据王涛等,2022)
Figure 1. Tectonic Sketch Map of Northeast Asia
图 2 东北亚地区晚中生代变质核杂岩或伸展穹隆构造分布图(据Wang et al., 2012修改)
黑框显示了文中研究的3个花岗岩伸展穹隆
Figure 2. Late Mesozoic Extensional Structures in Northeast Asia
图 3 罕乌拉穹隆构造简图(据杜灿等,2022)
Figure 3. Hanullah dome structure schematic diagram
图 4 Nartyn穹隆构造简图(据Daoudene et al., 2012)
Figure 4. Nartyn dome structure schematic diagram
表 1 南蒙古-中蒙边境花岗岩穹隆韧性剪切带内样品特征及低温年代学信息
Table 1 Characteristics and geochronology of mylonitic samples from the granite domes at the South Mongolia-Sino-Mongolian border
样品 采样地点 GPS点位 岩性 高程
(m)AFT年龄
(Ma)ZHe年龄
(Ma)Ar-Ar年龄
(Ma)N180717-4 罕乌拉穹隆 116°36′44″E
46°03′54″N糜棱岩化细粒黑云
母钾长花岗岩890 104.94±5.84 123.4±7.35 123.9±0.90(Bi)
104.94±5.84(K)N180717-9 罕乌拉穹隆 116°36′44″E
46°03′54″N糜棱岩化中粗粒黑
云母钾长花岗岩890 121.3±1.40(Bi)
122.2±1.20(K)M19713-39.1 Nartyn穹隆 108°37′43.04″E
45°44′28.47″N糜棱岩化闪长岩 1125 101.30±5.51 123.7±7.42 M19715-310.2 Altanshiree穹隆 110°37′55.60″E
45°46′43.36″N花岗质糜棱岩 1109 101.73±6.20 M19715-311.1 Altanshiree穹隆 110°37′56.89″E
45°45′39.60″N花岗质糜棱岩 1117 110.05±7.38 表 2 南蒙古-中蒙边境花岗岩穹隆磷灰石裂变径迹分析结果
Table 2 Apatite fission track data of the granitoid samples from the South Mongolia-Sino-Mongolian border
样号 颗粒数目 Track Density (× 106 tr cm-2) (Pχ2) 裂变径迹
年龄(Ma)平均裂变径
迹长度SD ρs(Ns) ρi(Ni) ρd(Nd) (±1σ) (µm ± 1s.e.)
(no of tracks)(µm) N180717-4 20 0.9584 (679)1.937( 1372 )1.246( 3987 )<0.01%(99.9%) 104.9±5.5 13.66±0.09(100) 0.94 M19713-39.1 20 1.640(723) 3.359( 1481 )1.219( 3902 )<0.01%(>99.9%) 101.3±5.5 13.70±0.10(100) 1.02 M19715-311.1 20 0.4990 (427)0.9303 (796)1.206( 3860 )<0.01%(>99.9%) 110.1±7.4 13.97±0.15(40) 0.93 M19715-310.2 20 1.138(533) 2.271( 1064 )1.193( 3818 )<0.01%(>99.9%) 101.7±6.2 13.58±0.13(54) 0.97 表 3 南蒙古-中蒙边境花岗岩穹隆锆石(U-Th)/He分析结果
Table 3 Analysed ages of ziron of the granitoid samples from the South Mongolia-Sino-Mongolian border
样品 4He +/− U +/− Th +/− Th/U FT 矫正年龄 +/− 等效半径 ncc ppm ppm factor Ma Ma μm M19713-39.1-2 33.46 0.89 1771.08 45.49 673.08 15.56 0.39 0.64 128.1 7.8 42.4 M19713-39.1-3 18.75 0.48 872.21 19.82 515.24 10.44 0.61 0.66 116.9 7.0 44.3 M19713-39.1-4 60.70 1.63 2149.20 49.33 824.03 16.16 0.39 0.69 126.2 7.6 48.0 M19713-39.1-5 37.31 0.95 1150.77 27.68 587.72 12.19 0.52 0.69 121.1 7.3 49.3 加权平均年龄:123.07±7.42 Ma N1801717-4-1 6.47 0.16 449.93 10.32 173.80 3.70 0.40 0.63 120.8 7.2 40.3 N1801717-4-2 13.52 0.34 1214.71 26.45 527.63 10.08 0.45 0.59 124.4 7.4 37.1 N1801717-4-3 23.41 0.59 847.47 19.27 195.43 3.97 0.24 0.69 124.1 7.4 47.7 N1801717-4-4 7.15 0.18 213.55 4.88 102.70 2.10 0.49 0.69 124.2 7.4 49.4 加权平均年龄:123.40±7.35 Ma 表 4 基于热史模拟法、年龄-封闭温度法和矿物对法的南蒙古-中蒙边境花岗岩穹隆隆升速率
Table 4 The rate of granite dome uplift in the South Mongolia-Sino-Mongolian border on thermal history simulation, age-sealing temperature method and mineral pair method
样号 热史模拟法 年龄-封闭温度法 矿物对法 快速隆升阶段 较缓慢隆升阶段 ZHe AFT Bi Ar-Ar/ZHe ZHe/AFT 时间 速率 时间 速率 年龄 速率 年龄 速率 时间 速率 时间 速率 M180717-4 133~124
124~1231.41
5.71123~105 0.109 123.4 0.038 104.9 0.026 123.9~123.4 5.71 123.4~104.9 0.109 M19713-39.1 133~125
125~1231.79
2.02123~101 0.09 123.7 0.038 101.3 0.027 125~123.7 2.02 123.7~101.3 0.09 M19715-311.1 127~126
126~12314.3
1.27123~110 0.151 110.1 0.025 时间单位为Ma;速率单位为Km/Ma -
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