Climatic and environmental changes during Heinrich 6 recorded by δ13C in stalagmites from Jinfo Cave, Chongqing
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摘要: 在末次冰期发生的6次海因里希事件(Heinrich事件,简称H事件)中，H6事件发生年代最早，对其研究较少，利用高分辨率石笋记录研究H6事件期间的气候环境变化，有助于理解高低纬度气候变化对H事件的响应过程。本文基于重庆市金佛洞石笋JF2017铀系测年数据和碳同位素数据，重建H6事件期间中国西南地区季风气候环境的演化过程。结果显示：石笋JF2017的δ13C值在61 811±204 a B.P.~59 716±159 a B.P.时段显著偏重，持续大约2 095 a，暗示该时段季风减弱和生态植被退化，对应北大西洋H6事件。H6事件期间，石笋JF2017的δ13C记录在61 811~60 848 a B.P.期间开始先逐渐偏重至最大值，随后发生短时间尺度的波动变化；最后在60 848~59 716 a B.P.期间缓慢偏轻至H6事件结束，整体呈现先逐渐偏重后又缓慢偏轻的趋势，内部存在百年际尺度的气候震荡，表明H6事件期间当地气候不稳定。石笋JF2017记录的百年际尺度的季风气候变化可能与热带辐合带和北大西洋经向翻转环流密切相关。
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关键词:
- 亚洲季风 /
- Heinrich 6 /
- 石笋δ13C /
- 金佛洞 /
- 中国西南
Abstract:Of the six Heinrich events (H event for short) occurring during the last glacial period, H6 event has been less studied due to its earliest occurrence time. However, using high-resolution stalagmite records to study climate and environmental changes during H6 event can help people understand the response of climate change at high and low latitudes to the H events. In recent years, much progress has been made in terms of studies on paleoclimate and environmental changes with use of stalagmite δ13C records. Besides, some stalagmite δ13C records have been used to study the climate change of the events on a millennium scale during the last glacial period, but there is no detailed record of the high-resolution climate change in Southwest China during the H6 event. In this case, the JF2017 stalagmite δ13C may provide an excellent proxy for exploring the climate and environmental change during H6 event. Based on the dating data of uranium series and carbon isotope data of JF2017 stalagmite in Jinfo Cave, we analyzed the climate significance indicated by stalagmite δ13C, and reconstructed the evolution process of the monsoon climate environment in Southwest China during H6 event. Developed from the Jinfo-Yangzi Cave system, Jinfo Cave is located in Jinfo Mountain, Nanchuan district, Chongqing, Southwest China, with an altitude of 2,080 m. The Jinfo Mountain area belongs to subtropical humid monsoon climate, which is jointly affected by the Indian Summer Monsoon and the East Asian Summer Monsoon (ASM). In this study, the age samples of stalagmites were tested by Neptune Plus Multi-receiver Plasma Inductively Coupled Mass Spectrometry (MC-ICP-MS), and the carbon isotopes of stalagmites were tested by Delta-V-Plus gas isotope ratio mass spectrometer and carbonate automatic sampling device Kiel-IV online. The experimental results are based on Vienna Pee Dee Belemnite (V-PDB) with δ13C experimental analysis error<0.06 ‰(2σ). According to data analysis, the variation range of stalagmite JF2017 δ13C is from -1.44 ‰ to -4.92 ‰, with an average value of -3.40 ‰. JF2017 stalagmite δ13C fluctuates significantly, and change amplitude is -3.48 ‰. JF2017 stalagmite δ13C records show that the value mainly presents a negative-positive-negative-positive trend during the period of 63,000-58,000 a B.P. Due to the complex influence factors of stalagmite δ13C, we compared JF2017 stalagmite δ13C records with JFYK7 stalagmite δ18O records in Yangkou Cave which belongs to the same cave system as Jinfo Cave in order to further analyze the climate significance of stalagmite JF2017 δ13C. The comparison indicates a similar variation between the stalagmite stable isotope records of Yangkou Cave and JF2017 stalagmite δ13C records. Through previous research, JFYK7 stalagmite δ18O is indicative of variations in ASM intensity, and the positive shift in stalagmite δ18O corresponds to weakened ASM. In this study, we observed that the stalagmite JF2017 δ13C value was slightly positive, corresponding to the positive JFYK7 stalagmite δ18O. Therefore, under the background of weakening ASM, JF2017 stalagmite δ13C records can reflect the change of ecological environment caused by the change of regional hydrological climate. In addition, the comparison between JF2017 stalagmite and the stalagmites from other caves indicate that JF2017 stalagmite δ13C records were comparable with other climate records during H6 event. From 61,811 a B.P.to 60,848 a B.P., the values gradually became heavier, reaching to the maximum, and then fluctuated on a short-time scale. Finally, from 60,848 a B.P. to 59,716 a B.P., they became slightly lighter to the end of H6 event. The comparison between JF2017 stalagmite δ13C and other relevant records in the rest of the world shows fluctuations in the stalagmite δ13C record, so do the corresponding ITCZ and AMOC. This study shows that the stalagmite JF2017 δ13C value was significantly heavier during the 61,811 ± 204 a B.P.-59,716 ± 159 a B.P. period, lasting for about 2,095 a, which may suggest that the monsoon weakened and the ecological vegetation degraded during this period. This result corresponds to the North Atlantic H6 event. During H6 event, stalagmite JF2017 δ13C records became heavy to the maximum value during the period of 61,811-60,848 a B.P., followed by the fluctuation change on the short-time scale. During the period of 60,848-59,716 a B.P., the values slowly became lighter to the end of the H6 event. The trend of becoming light after a gradual increase of weight and a climate shock inside on a centennial scale indicate that the local climate was unstable during H6 event. The monsoon climate change on a centennial scale recorded by stalagmite JF2017 may be closely related to the Intertropical Convergence Zone (ITCZ) and the North Atlantic meridional overturning circulation (AMOC). -
Key words:
- Asian monsoon /
- Heinrich 6 /
- stalagmite δ13C /
- Jinfo Cave /
- Southwest China
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0. 引 言
我国是地质灾害分布最广的国家之一。中国地质灾害类型多样，且破坏性强，尤其是在地貌演化迅速、极端降雨频发的山区，崩塌、滑坡等斜坡类地质灾害频发，严重威胁着山区人民的生命财产安全、基础设施建设，制约着当地经济的发展[1]。广西壮族自治区平果市为低山丘陵和岩溶地貌，具有地质构造复杂、地震活动频繁等特点，加之人类工程活动剧烈，致使区内地质灾害多发。因此，开展地质灾害易发性评价，可为平果市地质灾害防治管理提供基础依据，可为实现平果市经济社会发展和生态文明建设提供地质安全保障。
近年来，随着3S技术快速发展，研究人员将其应用于滑坡易发性制图以预测滑坡发生的概率。地质灾害易发性评价的算法主要分为基于专家知识模型、确定性模型、数理统计模型和机器学习模型[2-3]。模型的选择会直接影响灾害预测结果的准确性，每种模型都有各自的优点和缺点。基于专家知识模型是定性方法，常用的模型为层次分析法和加权线性组合法等[4-5]，其内涵是专家基于经验赋值因子的权重，预测结果受人为影响较大。确定性模型主要是根据斜坡岩土体的物理力学参数以及地下水特征等有关数据来预测小区域范围内斜坡的稳定性[6]。数理统计模型中，频率比[7]、信息量[8]、确定系数[9]、逻辑回归等模型能够计算滑坡与影响因子的空间关系而被广泛使用。但是其所需滑坡样本数据量较大，且以往的统计模型只是对因子进行分级后，简单地串联起来影响因子，很难处理复杂的滑坡大数据。机器学习模型能够智能模拟和确定滑坡大数据非线性特征，从而提高模型的精确度，如决策树[10]、随机森林[11]、神经网络[12]和支持向量机[13]已被广泛应用于滑坡易发性评价。但是机器学习模型具有黑箱子特征，很难定量反映滑坡与地质环境因子之间的统计关系。最近，一些学者考虑不同模型的优缺点，将不同模型进行耦合来评估地质灾害的易发性，从而提高模型的精确度。如秦红富等[14]采用确定系数和逻辑回归模型相结合的方法来预测云南省宁洱哈尼族彝族自治县地质灾害的易发性；赵晓燕等[15]采用确定系数、信息量以及确定系数与信息量耦合模型预测攀枝花市矿山地质灾害，结果表明耦合模型的预测精度最高。
鉴于此，本文基于信息量(IV)和多层感知机分类器(MLPClassifier，MLPC)模型，提出了一种新的耦合模型(IV-MLPC)来预测平果市的地质灾害的易发性。首先选取地形、地质和环境等相关的9个影响因子并进行分级，然后计算地质灾害在各分级的IV值以揭示地质灾害与因子的空间关系，再通过应用多层感知机分类器模型进行平果市地质灾害易发性评价，最后采用ROC曲线来检验评价结果的合理性。
1. 研究方法
1.1 信息量
信息量法的原理是利用过去的地质灾害，对灾害因子进行统计分析，把影响因子的实测值转化为反映斜坡稳定性的信息量值，并推算出地理环境、地质条件和水文气象等对地质灾害诱发作用的大小，以此“信息量(IV)”来反映地质灾害的易发性[16]。在地质灾害易发性评价中，信息量法公式一般被简化为：
IVAi→B=lnNi/NiNNSi/SiSS (1) 式中：IVAi→B为因子A在i区间内地质灾害B发生的信息量；Ni为Ai中发生地质灾害的数量；Si为Ai的栅格总数；N表示研究区内地质灾害总数；S表示研究区栅格数。信息量值IV>0时，表明地质灾害易于发生；IV<0时，表明地质灾害相对不易于发生；IV值则越大，说明地质灾害易发性越高，反之则说明地质灾害易发性较低。
1.2 多层感知机分类器
多层感知机(MLP)神经网络是一种前向结构的人工神经网络，包括输入层、中间隐藏层和输出层三层全连接结构[17]。神经网络主要有三个基本要素：权重、偏置和激活函数。多层感知机分类器是MLP神经网络中用于分类的模块。该模型使用BFGS方法最小化给定的损失函数加上二次惩罚，并使用优化类训练具有一个隐藏层的多层感知器。为了提高速度，使用逻辑函数的近似版本作为隐藏层的默认激活函数，但可以指定其他激活函数。在输出层，使用sigmoid函数进行分类。多层感知机分类器具有高度的并行处理、良好的容错性和非常强的自适应、自学习功能，在地质灾害易发性评价中具有广泛的应用前景。
2. 研究区概况及数据源
2.1 研究区概况
平果市地处红水河及右江中游，地理位置为东经107°21′~107°51′，北纬23°12′~23°51′，面积约2 485 km2。研究区属南亚热带季风气候区[18]，年平均气温在18~22 ℃之间，年平均雨量在1 200~1 450 mm，降雨主要集中在5至10月，地表径流总量14.286×108 m3。平果市地势大致为中部高，南北低，从西北向东南部倾斜。平果市地貌分岩溶地貌和非岩溶地貌两大类型，根据其成因与形态组合以及地形高程可细分为：峰丛洼地谷地、峰林谷地、溶岭谷地、溶垄谷地、低山丘陵五种地貌类型。峰丛洼地谷地广泛分布于平果市的中部、北部和西南地区，占全市面积的43.8%。峰丛基底相连，山峰密集呈簇状、塔状，洼地、谷地镶嵌其间。溶垄谷地分布于太平、果化、新安等地，面积占全市面积的17.4%。岩层溶蚀较弱，剥蚀较强，形成沿岩层发育的垄状地貌。低山丘陵分布于平果市的北部及南部，分布面积占全市面积的31%。峰林谷地和溶岭谷地分布面积相对较少，分别占平果市的3.4%和4.4%。境内出露的地层为泥盆系、石炭系、二叠系、三叠系、白垩系、古近系和第四系。岩性以泥岩、砂岩、灰岩和白云岩为主。受地质构造影响，岩层褶皱裂隙发育，致使岩石破碎，岩体完整性降低。加之剧烈的人类工程活动(如人工采石、工民建筑、矿山开采等)，为地质灾害的发育提供了有利条件，严重威胁着区内人民的生命财产安全与社会经济的发展。
2.2 数据来源
在研究区1∶50 000地质灾害详细调查的基础上，利用2020年高分二号、北京二号等国产高分遥感数据，开展平果市地质灾害遥感解译，共探测地质灾害255处（图1），无规模等级特大型、大型的地质灾害，规模为中型的2处，小型253处。其中崩塌189处，规模等级均为小型（图2）；滑坡62处，规模为小型60处（图3）；岩溶塌陷4处，规模均为小型；无泥石流。海城乡发育地质灾害最多（47处），其次为太平镇（42处），最少的为黎明乡（9处）。区内滑坡按运动形式包括牵引式滑坡（39处）和推移式滑坡（23处），按物质组成分为岩土质滑坡（9处）和土质滑坡（53处），滑坡厚度<5 m的数量为54处，5~10 m的数量为8处。崩塌按照物质组成分为岩质崩塌（169处）和土质崩塌（20处）。由于岩溶塌陷发育数量较少，且其发育机理与崩塌滑坡等斜坡类灾害差异性较大[19-20]，因此本研究针对251处崩塌滑坡进行斜坡类地质灾害易发性预测。251处斜坡类地质灾害点随机选择80%的数据作为训练样本，其余20%的样本用于模型精度的验证。此外，由于机器学习用于地质灾害预测是典型的二元分类问题，因此相同数量的非地质灾害点(n=251)在ArcGIS平台中被随机生成，并以80∶20的比例划分为训练样本和验证样本。
图 1 研究区地理位置及斜坡类地质灾害编录Figure 1. Location of the study area and hillslope geo-hazard inventory开展地质灾害易发性评估的数据来源主要包括：①从美国阿拉斯加实验室(
https://search.asf.alaska.edu )下载ALOS 12.5 m×12.5 m分辨率表面高程模型数据，并在ArcGIS软件中获得坡度、坡向、曲率、河流等地形和水文因子；②在中国地质调查局获得1∶200 000地质图，用于提取岩性、断层等因子；③在国家气象数据中心获得年均降雨量数据，并在ArcGIS中采用插值获取研究区降雨因子；④高分二号、北京二号等国产高分遥感数据用于获得研究区土层厚度。3. 灾害的空间分布特征及灾害影响因子的选取
3.1 灾害的空间分布特征
结合相关研究及地质灾害详查报告[21-22]，研究区内地质灾害受中低山地形地貌控制，由于构造发育、降雨集中、岩性多样且破碎，为地质灾害的发育提供了有利条件。尽管人类活动也是诱发崩塌滑坡的重要因素之一，但是某个地区的人类活动程度很难定量化。一些滑坡易发性研究采用距道路的距离代表人类工程活动，根据实地勘查，道路对平果市崩滑灾害的影响较小。因此，本研究选择高程、坡度、坡向、曲率、工程地质岩组、距断层距离、土层厚度、距河流距离和降雨共9个地质环境因子（图4，表1）评价平果市斜坡地质灾害的易发性。
表 1 崩滑灾害影响因子分级及IV值Table 1. The classes and IV of factors因子 因子分级 分级栅格 分级栅格占比/% 灾害数目 灾害占比/% IV 高程/m 50~150 1 801 053 11.324 15 5.976 −0.639 150~250 3 231 404 20.318 60 23.904 0.163 250~350 4 206 902 26.451 97 38.645 0.379 350~450 3 916 670 24.627 61 24.303 −0.013 450~550 1 995 484 12.547 14 5.578 −0.811 550~650 649 804 4.086 4 1.594 −0.941 650~896 102 919 0.647 0 0 0 坡度/° 0~10 4 306 296 27.076 2 0.797 −3.526 10~20 4 354 216 27.378 8 3.187 −2.151 20~30 3 869 997 24.333 24 9.562 −0.934 30~40 2 037 339 12.81 46 18.327 0.358 40~50 965 326 6.070 130 51.793 2.144 50~60 311 711 1.960 36 14.343 1.990 60~80 59 351 0.373 5 1.992 1.675 坡向 平面 206 476 1.298 0 0 0 北 2 015 082 12.67 26 10.359 −0.201 东北 1 863 358 11.716 26 10.359 −0.123 东 1 820 559 11.447 37 14.741 0.253 东南 2 250 940 14.153 30 11.952 −0.169 南 2 189 502 13.767 46 18.327 0.286 西南 2 002 428 12.591 36 14.343 0.130 西 1 644 770 10.342 19 7.570 −0.312 西北 1 911 121 12.016 31 12.351 0.027 曲率 −60.21~−0.05 6 661 594 41.886 131 52.191 0.220 −0.05~0.05 3 046 564 19.156 52 20.717 0.078 0.05~62.72 6 196 078 38.959 68 27.092 −0.363 工程地质岩组 岩组i 58 356 0.367 1 0.398 0.082 岩组ii 5 033 581 31.649 63 25.100 −0.232 岩组iii 3 273 833 20.585 34 13.546 −0.418 岩组iv 7 538 466 47.399 153 60.956 0.252 距断层距离/m 0~500 3 722 914 23.408 66 57.769 0.903 500~1 000 2 845 856 17.894 38 23.506 0.273 1 000~1 500 2 159 709 13.579 29 14.343 0.055 1 500~2 000 1 688 184 10.615 24 4.382 −0.885 2 000~2 500 1 344 082 8.451 21 8.367 −0.010 >2500 4 143 491 26.053 73 29.084 0.110 土层厚度/m 0~1 7 508 534 47.211 145 57.769 0.202 1~3 4 051 848 25.477 59 23.506 −0.081 3~5 3 647 726 22.936 36 14.343 −0.469 ＞5 696 128 4.377 11 4.382 0.001 距河流距离/m 0~250 5 831 693 36.668 152 60.558 0.502 250~500 3 734 620 23.482 29 11.554 −0.709 500~750 2 264 781 14.240 18 7.171 −0.686 750~1 000 1 354 485 8.517 10 3.984 −0.760 1 000~1 250 826 642 5.198 9 3.586 −0.371 >1 250 1 892 015 11.896 33 13.147 0.100 降雨/mm 1 223~1 273 1 415 863 8.902 11 4.382 −0.709 1 273~1 323 7 487 006 47.076 103 41.036 −0.137 1 323~1 373 6 096 852 38.335 119 47.410 0.212 1 373~1 428 904 515 5.687 18 7.171 0.232 （1）高程：高程是影响斜坡破坏的最重要的地形因子，不同高程斜坡的重力势能、集水能力及人类活动的强度也有所不同。研究区高程范围在50~896 m，以50 m为间隔划分为7个等级（图4a）。其中等级150~250 m及250~350 m的区域IV>0，表明发生地质灾害的可能性较大（表1）。这可能是因为这个区域人类工程活动较频繁，更易诱发地质灾害。
（2）坡度：坡度是控制地质灾害发育程度和地质灾害发育类型的一个重要因素。坡度可改变斜坡的应力分布状态，随着坡度的增大，坡面附近应力带范围随之扩大，坡脚应力集中并随之增高，斜坡越陡，就越易产生变形破坏。以10°为间隔将研究区坡度划分为7个等级，其中坡度等级为40°~50°时，信息量值较大（IV=2.144）（表1），表明这个坡度发生地质灾害的可能性最大。此外当坡度>30°时，IV值>0，这是因为崩塌多发育于较大坡度的斜坡（图4b）。
（3）坡向：不同坡向的斜坡受到外界环境影响程度不同，使得地质灾害的孕灾环境在不同坡向上存在显著差异[23]。研究区斜坡在东、南、西南和西北向的IV值>0，表明地质灾害在这些坡向发生的概率最大（图4c）。
（4）曲率：曲率是斜坡的几何形状参数，反映了坡面坡度变化程度及复杂程度。研究区的曲率是−60.16~62.72，其中斜坡曲率<−0.05，−0.05~0.05，>0.05时，分别表示凹形、平面和凸形斜坡（图4d）。研究区凹形斜坡的IV值最大（表1），表明这些区域最易发生地质灾害。
（5）工程地质岩组：岩性是斜坡的物质组成基础，不同的岩性的抗风化能力、物理力学强度等具有差异性。根据岩性特征将研究区的岩土体划分为四个岩组（图4e）：黏性土单层土体（i），中厚层状坚硬—半坚硬砂岩夹软质泥、页岩岩组（ii），中厚层状弱—中等岩溶化坚硬—半坚硬灰岩、白云岩夹硅质岩、砂岩岩组（iii），中厚层状中—强岩溶化坚硬—半坚硬灰岩、白云岩岩组（iv）。其中岩组iv的信息量值最大（表1），主要是因为这些地区岩石节理裂隙发育，经过长期风化、溶蚀作用，岩石整体性差，岩溶石山上易发生崩塌滑坡灾害。其次，岩组ii中发生的崩滑灾害也较多，这是因为岩层中含有泥岩和页岩等软弱层，更易于崩滑灾害的发生。
（6）距断层距离：断层控制岩体的完整性，活动断层易引发地震，诱发大量的地质灾害。因此，断层与地质灾害的发育具有密切的关系。本研究以500 m为等间隔，将距断层距离划分为6个等级，其中在0~500 m范围内，地质灾害分布的数量最多（图4f）。这是由于断裂带内构造节理发育、岩体破碎、岩石风化作用更强烈，所以发生量明显高于其他区域。平果市内断裂构造主要为压性断裂，且地质灾害在压性断裂周边分布的聚集性明显强于其他断裂。
（7）土层厚度d：土层是斜坡的物质组成，其厚度可反映风化程度及形成滑坡的规模。根据工程钻孔揭露情况综合统计分析，平果市碎屑岩区风化层厚度总体呈条带状，泥、页岩区，风化土层厚度较大，一般大于5 m；砂岩区、泥岩夹灰岩区次之，厚度相对较薄，厚度为2~5 m。将研究区土层厚度划分为4个等级（图4 g）：d≤1 m，1 m<d≤3 m，3 m<d≤5 m和d＞5 m。等级≤1 m的区域的IV值最大（表1），地质灾害发生的密度也较大。
（8）距河流距离：河流可侵蚀坡脚，软化坡脚岩体，降低岩土体的抗剪强度，从而使得斜坡破坏。研究区的地质灾害在距河流距离0~250 m范围内最多，且IV值最大（图4 h，表1）。
（9）降雨：降雨是地质灾害主要诱发因素之一，降雨将沿岩体裂隙快速地流入崩、滑体内，裂隙内的积水就会产生有压渗透，大大地增加了降雨入渗量。同时，斜坡体的含水量将迅速增加，基质吸力大幅度降低，土体的抗剪强度减小，边坡的稳定性降低，就会在较短时间内发生破坏。研究区的降雨量1 223~1 427 mm，以50 mm为间隔，将其划分为四个等级（图4i）。随着降雨量的增大，IV值增大，在等级1 373~1 428 mm时，IV值最大，表明降雨是崩滑灾害重要的诱因（表1）。
3.2 评价因子的共线性诊断
因子之间存在共线性会影响模型的精度，因此，需要在模型计算前对因子进行共线性诊断，筛选出相互独立的因子。本研究采用80%的地质灾害样本和非地质灾害样本组成的训练数据集，提取每个因子的分级后的等级值后，在SPSS软件中获取方差膨胀系数(VIF)和容忍度(TOL)进行共线性诊断(表2)。由表2可知：所有因子的TOL>0.1或VIF<10，因此所使用的9个因子相互独立，可用于地质灾害易发性评价。
表 2 地质灾害影响因子的共线性诊断Table 2. Multicollinearity diagnosis of influencing factors of geo-hazards影响因子 TOL VIF 高程 0.937 1.067 坡度 0.895 1.117 坡向 0.980 1.021 曲率 0.964 1.038 工程地质岩组 0.855 1.169 距断层距离 0.987 1.013 土层厚度 0.960 1.042 距河流距离 0.896 1.116 降雨 0.860 1.163 4. 地质灾害易发性评价结果
4.1 斜坡类地质灾害易发性分区
根据随机选取的训练样本，采用IV和多层感知机分类器模型耦合的IV-MLPC方法进行平果市地质灾害易发性评价，计算得到整个研究区的地质灾害易发性指数，其值是0.02~0.94，对应着崩滑灾害易发性从低到高。目前，灾害易发性分区多是采用自然断点法或均值法划分为四至六个等级，而最常使用的是五个等级（极低易发区、低易发区、中易发区、高易发区和极高易发区），本研究在GIS平台中采用自然断点法将其划分为五个等级(图5)。表3统计了各易发区的灾害点占比和面积占比来定量分析研究区地质灾害易发性。由表3可知：灾害极低易发区所占面积最大，为36.24%，其次是低易发区(30.54%)、极高易发区(25.39%)、中易发区(4.34%)和高易发区(3.49%)。极高易发区主要分布于地形陡峭的山区和平原的结合部位，如研究区北部的黎明乡、同老乡和榜圩乡，中部的海城乡、旧城镇和坡造镇以及南部的新安镇。这些区域的地质环境复杂，多位于海拔150~350 m，坡度40°~60°，河流两岸500 m范围内，距离断层1 000 m范围内，土层厚度d≤1 m以及中厚层状、中—强岩溶化坚、硬—半坚硬灰岩、白云岩岩组（iv）。在极高易发区中，灾害点数分布更为集中，共有162个崩滑灾害点，占比64.54%。高易发区发育的灾害点占比为8.37%，面积占比为3.49%，主要分布在河谷两岸1 500 m范围内、高程>450 m，坡度30°~50°。仅有4个崩滑灾害点落在中易发区，中易发区主要分布在高程150~250 m、土层厚度d>5 m的区域。低易发区内有55个崩滑灾害点，占比21.91%；低易发区高程主要在50~150 m、中厚层状、弱—中等岩溶化、坚硬—半坚硬灰岩、白云岩夹硅质岩、砂岩岩组（iii）等区域。极低易发区有9个崩滑灾害点，主要位于果化镇、新安镇和马头镇等地势相对平坦的区域，如高程50~150 m和坡度为0°~20°的地区。通过计算各易发性分区的灾害点占比与面积占比可获得各易发性分区的频率比。从极低易发区至极高易发区，灾害发生的频率比呈增大的趋势，表明信息量—多层感知机分类器耦合模型能够有效地确定研究区不同的易发等级。
表 3 研究区地质灾害易发区划统计Table 3. Statistics of geo-hazard susceptibility zoning in the study area易发性等级 灾害个数 灾害占比/% 面积/km2 面积占比/% 频率比 地质环境条件 极低易发区 9 3.59 900.58 36.24 0.10 地势相对平坦的区域，如高程50~150 m和坡度为0°~20°的地区 低易发区 55 21.91 758.82 30.54 0.72 50~150 m、坡度10°~30°、岩组iii 中易发区 4 1.59 107.82 4.34 0.37 高程150~250 m、坡度20°~40°、土层厚度d>5 m 高易发区 21 8.37 86.75 3.49 2.40 河谷两岸1 500 m范围内以及高程>450 m以及坡度30°~50° 极高易发区 162 64.54 631.05 25.39 2.54 高程150~350 m、坡度40°~60°、河流两岸500 m范围内，
距离断层1 000 m范围内以及岩组（iv）4.2 模型精度评估
受试者工作特征曲线（ROC）以1-特异性为横轴、以敏感度为纵轴来定量反映不同阈值下模型的综合性能，曲线下的面积（AUC）可以评价模型的预测精度。一般认为：AUC<0.5，表明模型预测失败；AUC=0.5~0.7，预测准确性较低；AUC=0.7~0.9，预测准确性较高；AUC>0.9，说明预测准确性极高[24]。使用验证样本计算的IV-MLPC模型的ROC曲线（图6），得到ROC曲线下的面积AUC=0.809，表明IV-MLPC模型的预测准确度较高，能够较好地预测灾害的易发性。
5. 结 论
本文结合信息量和多层感知机分类器的优点，采用信息量—多层感知机分类器耦合模型对平果市地质灾害进行易发性评价，结论如下：
（1）结合平果市的环境地质条件特征，选择高程、坡度、坡向、曲率、工程地质岩组、距断层距离、土层厚度、距河流距离和降雨共9个因子建立评价单元。方差膨胀系数和容忍度进行因子的共线性诊断可知9个因子不存在共线性。计算各因子分级的信息量值表明，存在某些分级条件具有促进崩滑灾害发生的作用。 平果市最易发生崩滑灾害的条件为高程250~350 m，坡度40°~50°，坡向南向，凹形斜坡（曲率<−0.05），中厚层状中—强岩溶化、坚硬—半坚硬灰岩、白云岩，距断层距离0~500 m，土层厚度0~1 m，距河流距离0~250 m以及降雨1 373~1 428 mm。
（2）基于信息量—多层感知机分类器模型编制平果市斜坡类地质灾害易发性区划图，灾害极高易发区、易发区、中易发区、低易发区和极低易发区占全市总面积分别为25.39%、3.49%、4.34%、30.54%和36.24%。极高易发区主要分布于黎明乡、同老乡、榜圩乡、海城乡、旧城镇和坡造镇和新安镇，区内崩滑灾害点数占总灾害数的64.54%，表明崩滑易发性评价结果与历史崩滑点分布吻合程度高，易发性制图结果更可靠和准确。
（3）通过ROC曲线和AUC对模型预测能力进行检验结果表明，模型评价结果能够很好地预测研究区地质灾害的发生。因此，该模型对于指导平果市和类似地质环境条件地区的地质灾害防治有重要参考意义。
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图 1 重庆金佛洞地理位置示意图(图中黑色五角星指示金佛洞位置)
注：审图号：GS(2020)4619号
Figure 1. Geographical location of Jinfo Cave in Chongqing (Black star indicates the location of Jinfo Cave)
图 2 石笋JF2017剖面图
注：红色实线表示U-Th 测年采样点。
Figure 2. Profile of stalagmite JF2017
Note: Solid in red indicates U-Th dating sampling point.
图 3 石笋JF2017年代模型图
注：深蓝色圆点表示U-Th测年点的年代及深度，深蓝色误差棒表示测年误差(±2σ)；红色实线表示年代模型曲线，蓝色实线表示95%的置信界限。
Figure 3. Chronological model of stalagmite JF2017
Note: Dark blue dots indicate the age and depth of the U-Th dating points; error bars in dark blue indicate the dating error (±2σ); solid line in red indicates the chronological model curve; solid line in blue indicates the 95% confidence limit.
图 4 石笋JF2017 δ13C记录
注：深蓝色曲线为该数据20点滑动平滑，黑色虚线为数据平均值，红色误差棒表示测年误差(±2σ)。
Figure 4. δ13C records of stalagmite JF2017
Note: Dark blue curve indicates that the record is smoothed using a 20-point running mean; the black dotted line indicates the average value of the data; the error bars in red represent dating errors (±2σ).
图 5 石笋JF2017的平衡分馏检验
注：(a)深度为70 mm处生长层的δ13C和δ18O；(b) 深度为81 mm处生长层的δ13C和δ18O (红色点线图为δ13C；深蓝色点线图为δ18O)。
Figure 5. Equilibrium fractionation tests of stalagmite JF2017.
Note: (a) δ13C and δ18O values with a depth of 70 mm; (b) δ13C and δ18O values with a depth of 81 mm (red dotted line: δ13C; dark blue dotted line: δ18O.
图 6 JF2017石笋δ13C记录与其他石笋记录对比
注：(a) 金佛洞石笋JF2017 δ13C(本研究)；(b) 和(c)羊口洞石笋JFYK7 δ18O和 δ13C[9]；(d)和(e) 羊子洞石笋YZ1 δ18O和δ13C[21]；(f)和(g) 永兴洞石笋YX55 δ18O和δ13C[23]；黄色阴影部分表示H6发生的时间段。
Figure 6. Comparison of JF2017 stalagmite δ13C record with other stalagmite records
Note: (a) JF2017 stalagmite δ13C in Jinfo Cave; (b) and (c) JFYK7 stalagmites δ18O and δ13C[9] in Yangkou Cave; (d) and (e) YZ1 stalagmites δ18O and δ13C[21] in Yangzi Cave; (f) and (g) YX55 stalagmites δ18O and δ13C[23] in Yongxing Cave; yellow shadow: the time when H6 occurred.
图 7 JF2017石笋δ13C记录与全球其他气候记录对比
注：(a)金佛洞JF2017石笋δ13C记录(本研究)； (b)羊口洞JFYK7石笋δ18O记录[9] ；(c)65°N夏季太阳辐射[53]；(d)格陵兰冰芯δ18O记录[54]； (e) 北大西洋MD95-2040沉积岩芯SST记录[55]；(f) 北大西洋KNR191-CDH19沉积岩芯231Pa/230Th记录(误差棒表示年代误差)[56]；(g)卡里亚科盆地MD03-2621沉积岩芯反射率记录[57]；橙色虚线表示H6的开始和结束。
Figure 7. Comparison of JF2017 stalagmite δ13C record with other climate records in the rest of world
Note: (a) JF2017 stalagmite δ13C in Jinfo Cave; (b) JFYK7 stalagmite δ18O in Yangkou Cave[9]; (c) summer solar radiation at 65°N[53]; (d) ice core δ18O record in Greenland[54]; (e) SST record of MD95-2040 sediment core in North Atlantic Ocean[55] ; (f) 231Pa/230Th record of KNR191-CDH19 sediment core in North Atlantic Ocean (error bar: age error)[56]; (g) reflectivity of MD03-2621 sediment core in Cariaco Basin[57]; dotted line in orange: the beginning and the end of H6.
表 1 金佛洞石笋JF2017 230Th测年结果
Table 1. 230Th dating results of stalagmite JF2017
样品
编号深度
/mm238U
/ppb232Th
/pptd234U*
(measured)230Th/238U
(activity)230Th Age (a B.P.)
(corrected)JF2017-1 23.0 50 639±100 42±39 −118.1±1.1 0.361 7±0.0010 58 368±235 JF2017-2 28.5 35 390±71 163±41 −120.6±1.1 0.363 7±0.0010 59 050±248 JF2017-3 42.0 52 946±106 2±39 −118.6±1.2 0.365 9±0.0010 59 345±244 JF2017-4 49.0 50 680±59 98±15 −116.1±1.0 0.368 8±0.0006 59 728±159 JF2017-5 61.5 39 643±67 61±33 −113.5±1.1 0.371 6±0.0009 60 076±217 JF2017-6 71.5 38 075±48 182±19 −109.8±1.1 0.376 1±0.0006 60 681±177 JF2017-7 73.5 27 942±57 610±47 −110.5±0.8 0.377 3±0.0011 61 018±261 JF2017-8 88.5 57 510±98 32±31 −119.4±1.1 0.374 6±0.0008 61 339±219 JF2017-9 93.0 34 514±51 383±26 −116.1±1.1 0.378 1±0.0007 61 769±204 JF2017-10 107.5 49 708±94 91±36 −119.5±1.1 0.379 0±0.0010 62 332±248 注：衰变常数取值为λ230Th=9.157 7×10−6a−1；λ234U=2.826 3×10−6a−1；λ238U=1.551 25×10−10a−1；δ234U = ([234U/238U]−1activity)×103，δ234U初始值是根据230Th 年龄获得，即δ234U初始值=δ234U测量值×eλ234×T；初始230Th年龄校正采用地壳230Th/232Th平均比值: 4.4×10−6±2.2×10−6。
Note: The value of the decay constant is λ230Th=9.1577×10−6a−1; λ234U=2.8263×10−6a−1; λ238U=1.55125×10−10a−1; δ234U = ([234U/238U]−1activity) x 103, δ234Uinitial value is obtained based on 230Th age, i.e. δ234Uinitial value = δ234Umeasured value×eλ234×T; The initial 230Th age adjustment was based on the average ratio of 230Th/232Th in the earth's crust: 4.4×10−6±2.2×10−6.
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