Susceptibility evaluation of valley debris flow based on dual-channel network with fusion attention mechanism
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摘要:
针对泥石流灾害评估问题,文章提出了一种新的轻量化卷积神经网络模型——融合注意力机制的双通道网络(dual-channel fusion attention mechanism network,DCFAMNet),旨在快速识别沟谷型泥石流灾害。首先,根据历史泥石流点记录,以沟谷数字高程图像(digital elevation map,DEM)及遥感影像为数据源,设计以双通道网络结构为基础技术框架,在DEM图像特征提取通道引入通道注意力机制强调图像特征的网络通道权重,在遥感影像特征通道引入3D卷积块提取沟谷的地表信息,在特征融合阶段利用深度可分离卷积进行更多的特征信息交互。其次,对相关流域的潜在威胁沟谷作出易发性预测,绘制泥石流灾害易发性图。最后,可视化DCFAMNet提取到的沟谷坡向、曲率、坡度等深层特征定位目标关键特征。结果表明,利用DCFAMNet结合GIS技术对泥石流沟谷的识别率可达到80%,AUC值为0.75,表现良好。保存模型最佳参数评估相关沟谷易发性,通过ArcGIS做可视化分析将泥石流灾害分为5个评价等级,并确定泥石流极高易发性,得出高易发区主要分布在贡山县独龙江干流、福贡县怒江干流等水系区域,兰坪县相对较安全。结果可为山区泥石流防灾减灾工作提供有用的参考和依据。
Abstract:In addressing the issue of debris flow disaster assessment, this paper proposes a novel lightweight convolutional neural network model, the Dual-Channel Fusion Attention Mechanism Network (DCFAMNet), designed to rapidly identifying the susceptibility of gully-type debris flows. The main contributions of this paper are as follows: Firstly, based on historical debris flow records and using Digital Elevation Maps (DEMs) and remote sensing images as data sources, a dual-channel network structure is designed as the basic technical framework. Within the DEM image feature extraction channel, a channel attention mechanism is introduced to emphasize the channel weights of the image features, while in the remote sensing image feature extraction channel, 3D convolutional blocks are employed to extract the surface information of the gullies. In the feature fusion stage, depthwise separable convolutions are used to facilitate more interaction of feature information. Secondly, the susceptibility prediction of potential threats gullies in the related basins is made, and susceptibility maps of debris flow disasters are generated. Finally, DCFAMNet visualizes the extracted deep features such as gully slope, curvature, and slope orientation. Experimental results indicate that, by integrating the DCFAMNet with GIS technology, the identification rate for debris flow gullies can reach up to 80%, with an AUC value of 0.75, indicating good performance. The best parameters of the model are retained for assessing the susceptbility scores of the relevant gullies. Through visualization analysis in ArcGIS, the debris flow disaster risk is categorized into five assessment levels. It is determined that the extremely high susceptibility and high susceptibility zones for debris flows are primarily distributed in the mainstream of the Dulong River in Gongshan County and the mainstream of the Nujiang River in Fugong County, while Lanping County is relatively safe. The findings of this research can provide valuable insights and foundations for the prevention and mitigation of debris flow disasters in mountainous regions.
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0. 引言
泥石流作为常见的山区地质灾害[1],因洪流中夹杂着巨量砂石,且暴发突然,对山区人民的生产和生活造成巨大威胁。尤其在震后,由于地壳运动引发山体破坏,导致碎石垮塌、树木根部受损,地震引起的大量滑坡、崩塌产生了丰富的松散物源,导致震后较震前引发泥石流的可能性更大[2]。除此之外,随着我国工程技术的突飞猛进,西部山区的开发及“一带一路”建设如火如荼的进行,在指定场地集中堆放项目施工过程中产生的大量级配宽、体量大的弃土弃渣,也产生了大量的固体物质,在强降雨或上游来水的动力激发下,极易造成研究区水土流失,进而破坏周边各类基础措施、威胁人民生命财产安全[3]、阻碍社会经济发展与技术进步,对人类生存造成严重影响[4]。
彝海镇在强对流天气作用下,2020年6月26日晚21时—27日凌晨3时安宁河流域内的彝海镇大马乌村和大堡子村出现历史上罕见的突发性特大暴雨。安宁河起源于四川省凉山州冕宁县彝海镇东侧的山谷内,其上游河道较窄,水位快速上涨,特大暴雨使安宁河冲毁原来的河道,洪水携带着大量的石块和树干冲毁了大量房屋、农作物及基础设施等(图1)。大堡子村在7h内降雨量可达145.7 mm,大马乌村降雨量高达197.5 mm。泥石流巨大的冲击力造成彝海镇及高阳街道近万人受灾,其中16人遇难、6人失踪。另外倒塌房屋670余间;因灾受损农作物上千ha;堤防、道路、电力线路受损严重,累计超50km;损坏便桥6座。直接经济损失高达7.4亿元[5]。
1. 研究区概况
位于四川省凉山彝族自治州冕宁县境东南部彝海镇安宁河畔的大马乌村和大堡子村(图2),东邻越西县板桥乡、中所镇,西壤大桥镇,南接高阳街道,北连石棉县栗子坪[6]。
1.1 气候及降雨特征
彝海镇隶属四川凉山彝族自治州冕宁县,域内地势悬殊,高海拔地区常年日照充足,年均气温变化幅度小,而早晚温差较大,具有干湿分明寒带气候特点;低海拔地区表现为冬暖夏凉、季节变化不明显等亚热带气候特征[7]。研究区境内气候具有小范围差异:东部平原地区处于安宁河流域,因西南季风沿安宁河北上的暖湿气流,安宁河流域平原区具有利于农业发展的半湿润且温暖较好气候条件;北部海拔较高,气候较为湿润;西部雅砻江高山峡谷区,因地形高差悬殊,气候呈垂直分布,存在江边亚热带气候及山顶寒温带气候。
冕宁县降雨情况及蒸发量因地貌不同存在差异。东部、北部及山谷地区年均降雨量丰富,而西部、南部及河谷地带常年干旱。湿气在北部高山拦阻下形成雨屏,因此北部地区不易蒸发。彝海镇处于冕宁县北部,其降水量丰富,年均降雨量超1200 mm,见图3(a),日最大164 mm,小时最大150 mm。在年降雨中,大多为夜间降雨,超全年总降雨量7成[8]。
图 3 研究区基本概况[8]Figure 3. Basic situation in research area1.2 地形地貌及地质构造情况
冕宁县地处青藏高原东缘,面朝雅砻江[9],地势总体呈南低北高,全区总面积90%为山地地貌,地形起伏较大,最大高差悬殊达4 km,研究区处于2500 ~ 3000 m高程处,见图3(b)。在南北向地质构造的影响下,北西—南东走向的雪山在进入冕宁县域后呈树枝状形式,由西到东,分为3条并列的南北走向山脉,且在3山间夹着两条南北向的径流,总体展布为锦屏山、雅砻江、牦牛山、安宁河、小相岭等三山夹两水的地势形式。研究区内地质构造与康滇构造体系斜接,归属于川滇构造体系,其断裂主要是因东西向存在挤压应力,而形成的南北向的断裂和褶皱。
冕宁县地层以出露较齐全、厚度大和岩石成岩时代多为特征,总厚度9 567 m左右,出露地层主要包括第四纪松散堆积物(Q)、二叠系(P)、三叠系(T)和震旦系(Z),且区内侵入岩较为发育,这种岩性比较松散,更容易发生泥石流。
1.3 泥石流体量
此次泥石流是由局地突发性强降雨引发,其容重及黏粒成分直接决定泥石流搬运能力,经现场勘察得知,泥石流在搬运中近9成为尺寸较小的石块(图4)。通过室内颗粒级配实验,分析研究区保存完整的细颗粒泥石流沉积物的黏粒(粒径小于5×10−3 mm)成分[10]得知,此次泥石流的黏粒含量在5.0%~10.5%。根据相关勘查技术教材[11]及技术规范[12]计算泥石流容重,选择计算公式如式(1)。
(1) 式中:
——泥石流容重/(g·cm−3);x—泥石流沉积物中的粒径小于5×10−3 mm的 黏粒含量。
经过计算可知泥石流容重在2.15 g/cm3左右。
利用形态调查法[13],在安宁河沟下游村庄里,进行测量流经村庄的泥痕高度(图5)、安宁河沟沟床纵比降及泥石流过流断面面积等,从而计算泥石流峰值流量[14],计算公式如式(2)。
(2) 式中:
——泥石流峰值流量/(m3·s−1); ——泥石流过流断面面积/m2; ——泥石流断面平均流速/(m·s−1)。通过式(3)计算:
(3) 式中:
—泥石流沟的沟床糙率(石块粒径大多在是、 10 cm左右,挟有个别2~3 m的大石块, 0.5 m<Hc<2 m),在此取为0.077;Hc——泥深/m;
——泥石流沟床比降/‰(约150‰)。经过计算可知本次泥石流水流经安宁河沟口流速为2.69 m/s,峰值流量为171.21 m3/s,冲出量为14.3×104 m3[15],而安宁河沟口流量设计峰值为96.80 m3/s,流速为2.69 m/s,冲出量为9.77×104 m3。
2. 灾害成因分析
地形陡峭、地势高差悬殊的地区,经过长期风化作用,陡坡上岩土体碎裂严重,为泥石流的发育提供大量松散固体物源,除此之外,岩土工程施工及过度开采也会产生大量废渣,在突发性暴雨作用下,极易形成泥石流。因此,泥石流暴发的高峰期一般在特定地形地质条件地区的汛期(冕宁县主要集中在6—9月,见图6)。彝海镇泥石流的发生主要受地形地貌条件、人类不规范工程活动、突发性强降雨等影响。
2.1 地形条件
特定的地形有利于提供充足的水动力及松散物源。根据曹晨等[16]及余斌等[17], 25° ~ 45°的岸坡坡度是引发泥石流的最佳条件。通过收集当地年鉴及勘察可知,安宁河流域在2008年之后历经多次地震,沟谷呈深V切割状,流域属高山侵蚀地貌,两侧岸坡坡度30°左右(图7)。岸坡上破碎岩体势能较大,流域内曾多次发生崩滑,上游沟床纵比降较大(100‰~300‰),岸坡坡脚处在沟道径流强烈冲刷下,沟床内产生大量固体物质。在强降雨作用下沟谷间容易形成汇流,这将提供充足的水源动力,雨水储存在沟道形成区,增加岸坡堆积物孔隙水压力,破坏了岩土体的静态平衡,产生大量的松散物源。在经过多次地震后,沟道出现淤积和堵塞,导致泥石流形成区扩大。
另外,因流域上游可形成汇流的各支沟沟床纵比降、沟长及沟道形态不同,流域内水动力条件及输沙能力提高,易形成洪峰。因此,安宁河沟的地形条件是泥石流发生的主要原因之一。
2.2 人类活动的影响
人类的频繁活动改变了当地土体强度,因建筑施工,产生了大量的工业废料,在大雨的冲刷下易形成泥石流。冕宁县处于高海拔山区,县域面积4422.742 km2,人口近32万人,人口分布不均,河谷地带居民居多,而山地地区较少,乡镇、街道错落在安宁河两岸开阔的台地或坡地。
多年来,受历史及地理位置的限制,区内经济交通、工业化较落后,以农业为主要的经济收入。其主要工程活动为:农业活动、水电站的开发及应用、修房筑路以及矿产资源开采等。如今,随着生产能力的提升,县境内实现“路路通”,因缺乏专业的技术支持,筑路时的不合理削坡,导致区域内形成大量的人工边坡,且大多数缺乏有效的工程防护结构,安全系数较低。因此,安宁河流域附近的不规范工程活动是泥石流发生的主要原因之一。
2.3 物源条件
丰富的松散物源可以在充足的水动力下形成泥石流灾害。结合地面调查、遥感分析、资料收集整理得知,安宁河沟上游岸坡,在地震作用及长久的风化作用下,产生了大量的破碎岩体,且局部塌落,上游沟道内出现淤积和堵塞,导致泥石流形成区不断扩大。在2008年后,冕宁县共发生137次滑坡地质灾害,其中安宁河流域高达47次,除此之外,因交通不便,当地村民在进行工程活动后产生的大量工业废料未能及时运出,选择堆积在安宁河沟中下游附近,部分崩滑松散物源及工业废料,在强降雨和泥石流的冲刷下在沟道内形成近40万m3的堆积物源,其中动储量超10万m3,在泥石流流通区,沟道中松散物质持续补给泥石流,山洪携带的泥沙物质随之增多,增大了泥石流的规模和破坏力,进而发生泥石流灾害。
2.4 泥石流成因分析
结合陈宁生等[18]如何判别泥石流流体性质的方式,按照容重指标得出“6·26”泥石流为大型中频黏性泥石流。区域内地震频发且人类工程活动及农业活动频繁,在安宁河沟道中产生大量崩滑松散物源及工业废料,在暴雨洪水的冲刷下形成堆积物源。由于沟谷纵坡大、下游地形陡峻、充沛的降雨(年均1 200 mm以上)等,泥石流在此地极易发育。
综上,彝海镇泥石流成因如下:(1)陡峭的地形:安宁河沟沟谷呈深V切割状,地形陡峭,沟道纵比降较大;(2)丰富的松散物源:在地震频发及人类频繁的工业活动下破坏了流域内坡体及岩土体结构,区域内农业为主,居民退林还耕现象严重(图8),降低了土体的稳定性,增加了松散物源;(3)充足的水动力:“6·26”泥石流前7小时内,研究区遭遇突发性强降雨,超过了区域降雨阈值(安全临界降雨量)(图9),导致安宁河洪水泛滥,洪水携带着松散物源向下游冲刷,其流量大大超过了安宁河沟设计峰值流量,最终冲毁堤坝并发育为泥石流。
灾害发生后,通过对受灾区进行详细勘察,发现研究区破坏严重的原因,主要是沟谷内居民房屋选址不佳、砖木结构房屋结构强度低;其次居民防灾知识薄弱、群测群防水平较低、逃生线路选择不当等。经过对安宁河流域详细勘察,沟道岸坡坡度大、地形陡峭,目前仍存在大量可参与泥石流活动的动储量(图10),“6·26”泥石流后处于相对稳定状态,但若再次遭遇突发性特大强降雨,将存在再次引发泥石流的可能。历经多次地震和强降雨,沟道内的松散物源有所增加,特别是崩滑作用形成的固体物质剧增,缩短了泥石流的发育周期。
3. 防治措施建议
泥石流严重影响人民的正常生活,基本主要进行拦挡、排泄工程治理。除此之外,对边坡做支护处理也是非常必要,降低为泥石流的发育提供松散物源的趋势。结合现场实地考察分析,得出建议方案为:(1)因当地居民的生产生活,安宁河沟道受损严重,沟道旁修建的土路密实性及抗冲刷能力较差,当遇到突发性暴雨时,沟道出现堵溃,洪水将增强泥石流规模,对下游居民造成严重损失。因此需要对整条沟道进行定期清淤工作,并且在合适位置修建排洪通道,以便遇到大暴雨时,可以高效排洪,降低泥石流对下游的伤害。(2)处理沟道中的桥墩,在外表面加装橡胶保护,提高其抗冲刷能力,以防河道被摧毁。(3)在岩土工程施工或开采资源工作中尽可能避开雨季,对安宁河流域的降雨情况实时监测,及时预警,以防灾害发生。(4)当地主要收入以种植农作物为主,导致土质疏松,稳定性较差,因此可以通过栽种树木的方式加固土壤,以便提高其稳定性,减少松散物源,降低泥石流发生的可能性[19]。(5)目前已建的防护堤被冲毁、淤满,需重修防护堤,且要加高防护安全高度。拟在泥石流堆积区沟道左岸重建防护堤,可提高沟道泥石流过流和泄洪能力及保护下游农作物。(6)采用挡土墙及抗滑桩等对未来工程建设产生的人工边坡进行防护。(7)原先居民房屋多为砖木结构,损失惨重,因此灾后重建要在安全地带建设钢混材料房屋及构筑生活区,以便提高抗震和抗冲刷能力。(8)提高群众安全意识,做好安全演练,提高居民群测群防水平。
4. 结论
(1)2020年6月26日,冕宁县彝海镇发生大型泥石流,冲毁了河道、居民房屋及农田,严重威胁下游人员生命安全。判断此次是由局地强降雨造成的发生于夜间、激发时间短、持续时间长、造成损失惨重的暴雨沟谷大型中频黏性泥石流。经过形态调查法,得出泥石流容重在2.15 g/cm左右,流经安宁河沟口峰值流量为171.21 m3/s,冲出量为14.3×104 m3,严重超出了安宁河沟口设计峰值流量。
(2)其主要成因为安宁河沟沟谷呈深切割状,地势陡峭,坡度及沟床纵比降较大,为泥石流发育提供了良好的自然条件。且区域内地震频发及人类活动频繁,所以该流域松散固体物质丰富,在特大暴雨下,引发大型泥石流。
(3)6月26日暴发的大型泥石流,造成了重大人员伤亡及财产损失,主要原因为:①当地居民防灾减灾意识相对较为薄弱;②房屋选址不当(大部分房屋位于泥石流流通区)以及房屋建筑结构安全性较差;③泥石流灾害相关知识较为匮乏,灾害发生时不能正确选择逃生路线。
(4)经过无人机勘察现场可知,此次泥石流冲出量为14.3万m3,仅占动储量的43%、流域物源总量的20.9%,安宁河沟道内仍有近50万 m3的松散物源,超27万m3动储量将再次引发泥石流,尤其在中、下游局部沟道堵塞情况较严重。若再次遇到突发性强降雨,将再次遭遇泥石流袭击,特别在未来5—10 a将频繁暴发。
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表 1 样本分类
Table 1 Sample classification
所属类别 正样本 负样本 类别 0 1 2 3 4 5 流域面积/km2 (1, 24] (26, 64] (69, 109] (1, 12] (12, 27] (30, 45] 数据增强/个 45 51 44 48 46 54 表 2 正负2分类测试混淆矩阵
Table 2 Confusion matrix for two-class testing (Positive, negative)
预测值 真实值 正 负 正 110 20 负 37 113 表 3 6分类测试混淆矩阵
Table 3 Confusion matrix for 6-category testing
预测值 真实值 0 1 2 3 4 5 0 27 1 0 10 7 1 1 0 23 1 0 5 14 2 0 0 58 0 0 0 3 5 0 0 38 3 0 4 5 0 0 0 29 2 5 10 0 0 0 2 39 表 4 试验结果
Table 4 Summary of experimental results
数据(90∶10) 数据(80∶20) 数据(70∶30) Test2-acc Test6-acc Test2-acc Test6-acc Test2-acc Test6-acc DCFAMNet 80%±4% 76%±4% 68%±5% 64%±5% 62%±5% 60%±5% ResNet18[20] 76%±3% 70%±3% 64%±3% 64%±3% 57%±5% 57%±5% ResNet34 78%±5% 71%±3% 69%±a>% 65%±5% 60%±5% 58%±5% ShuffleNet[21] 72%±6% 70%±6% 60%±8% 56%±8% 55%±6% 51%±6% SENet[22] 78%±4% 65%±4% 62%±4% 54%±4% 54%±5% 48%±5% 注:DCFAMNet为轻量型卷积神经网络—融合注意力机制的双通道网络(Dual-Channel Fusion Attention Mechanism Network),ResNet指网络模型Residual Network,ShuffleNet指网络模型ShufleNet Volution,SENet指网络模型Squeeze-and-Excitation Network。 表 5 模型性能
Table 5 Summary of model performance
Precision-2 Recall-2 F1-score-2 Kappa-2 Precision-6 Recall-6 DCFAMNet 0.75 0.85 0.79 0.59 0.75 0.75 ResNet18 0.68 0.79 0.73 0.43 0.66 0.68 ResNet34 0.68 0.85 0.78 0.58 0.68 0.74 ShuffleNet 0.80 0.69 0.79 0.45 0.78 0.65 SENet 0.69 0.83 0.78 0.56 0.65 0.73 表 6 消融试验结果
Table 6 Pertubation experiment results
precision recall F1-score kappa Test2-acc Test6-acc basic Net 0.66 0.50 0.59 0.40 65%±5% 60%±5% with ECA 0.75 0.81 0.74 0.50 75%±4% 70%±4% with 3DCNN 0.66 0.55 0.60 0.45 72%±5% 68%±5% with DepSep 0.72 0.71 0.75 0.48 71%±3% 71%±3% DCFAMNet 0.75 0.85 0.79 0.59 80%±4% 76%±4% 注:basic Net为基础网络模型,ECA表示Efficient Channel Attention,3DCNN为3D卷积,DepSep为深度卷积,DCFAMNet为轻量型卷积神经网络—融合注意力机制的双通道网络(Dual-Channel Fusion Attention Mechanism Network)。 表 7 注意力模型对比结果
Table 7 Comparison results of attention models
注意力模型 Test2-acc Test6-acc basic Net 75%±4% 71%±4% With SE 76%±3% 72%±3% With CBAM 78%±5% 75%±5% With ECA 80%±4% 76%±4% 注:basic Net为基础网络模型,SE表示Squeeze-and-Excitation,CBAM表示Convolutional Block Attention Module,ECA表示Efficient Channel Attention。 表 8 地貌条件、物源条件
Table 8 Geomorphic conditions and provenance conditions
地貌条件和物源条件 练登大沟 腊早村 石缸河 主沟长度/km 10.300 7.824 24.342 面积/km2 16.260 16.450 87.174 高程差/km 1.386 2.426 2.590 坡降比 0.130 0.310 0.106 平均坡度/(°) 16.170 21.200 12.160 Melton指数 0.340 0.598 0.277 土壤条件 不饱和雏形土、简育高活性淋溶土 高活性淋溶土 高活性淋溶土、铁质低活性强酸土、腐殖质低活性强酸土、
简育高活性强酸土、饱和雏形土地层岩性 板岩、千枚岩、杂砂岩、长石砂岩、沙岩、
石灰岩和其他碳酸盐岩片麻岩、板岩、千枚岩、
片岩、花岗岩花岗岩、玄武岩、片麻岩、板岩、千枚岩、砂岩、杂砂岩、
长石砂岩、页岩、石灰石、其他碳酸盐岩植被条件[26] 疏林地、高覆盖度草地、其它建设用地 有林地、灌木林、疏林地、
高覆盖度草地水田、旱地、有林地、疏林地、高覆盖度草地、中覆盖度草地 -
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