浙江地区引发滑坡的降雨强度-历时关系

由于独特的地理位置,复杂的地质、地形和气候背景,浙江成为中国降雨型滑坡（土体和岩体滑动,也包括泥石流和崩塌等）最频发的地区之一。为评价浙江地区的滑坡灾害,本文对该地区1990年至2003年雨量站记录的降雨数据进行了详细分析,确定了引发土体滑坡-泥石流的降雨强度―历时下限。     

灾难性地震和降雨滑坡的体积与运动距离研究

为预测和评价灾难性滑坡的致灾机制,基于国内近年来地震和降雨诱发的灾难性滑坡资料,对地震和降雨滑坡的等价摩擦系数H/L、最大水平运动距离L、最大垂直运动距离H与滑坡体积V的关系进行分析。研究灾难性地震和降雨滑坡的远程运动特征以及不同规模滑坡最大水平运动和垂直运动的优势距离。研究结果表明,滑坡的H/L、L、H与V具有幂律关系,其中H/L与V具有负幂律关系,L、H与V具有正幂律关系。同一规模等级的地震和降雨诱发灾难性滑坡的水平和垂直运动距离不同。以H/L＝0.42作为滑坡远程运动的标准,地震诱发的灾难性滑坡与滑坡远程运动的关系较小,而降雨诱发灾难性滑坡与滑坡的远程运动的关系较大。根据滑坡运动距离的累积分布表,以80%滑坡的运动距离所分布的范围,建立了滑坡不同规模等级的优势运动距离区间,同规模等级的地震滑坡和降雨滑坡在水平和垂直运动的优势距离区间上存在差异。灾难性滑坡的运程不仅受滑坡体积的控制还与其诱发机制相关,其研究成果可为由地震和降雨诱发的灾难性滑坡的致灾区域和致灾强度的预测和评价提供参考依据。     

Application of the MoniFLaIR early warning system for rainfall-induced landslides in Piedmont region (Italy)

Landslides triggered by rainfall can be foreseen by modeling the relationship between the time occurrence of landslides and rainfall. This paper deals with the argument by adopting a hydrological model called Forecasting of Landslides Induced by Rainfall (FLaIR). The model is applicable for forecasting recurrent landslides and it is based on the identification of a mobility function Y(.) that links the occurrence of a slope movement to the antecedent rainfall. Once the mobility function is defined, it is possible to define its critical values, the exceeding of which indicates that new mobilizations could occur. The FLaIR model has been used to study some phenomena that happened in Lanzo Valleys, a Western Alps sector of the Piedmont region (Northern Italy) where slope debris flows are the predominant landslide type. The study has led to the development of an early warning system, called MoniFLaIR, for real-time monitoring and forecasting of slope hazard. This article describes some details of the system and its performance.     

The determination of statistical and deterministic hydrological landslide-triggering thresholds

 Hydrological landslide-triggering thresholds separate combinations of daily and antecedent rainfall or of rainfall intensity and duration that triggered landslides from those that failed to trigger landslides. They are required for the development of landslide early warning systems. When a large data set on rainfall and landslide occurrence is available, hydrological triggering thresholds are determined in a statistical way. When the data on landslide occurrence is limited, deterministic models have to be used. For shallow landslides directly triggered by percolating rainfall, triggering thresholds can be established by means of one-dimensional hydrological models linked to the infinite slope model. In the case of relatively deep landslides located in topographic hollows and triggered by a slow accumulation of water at the soil-bedrock contact, simple correlations between landslide occurrence and rainfall can no longer be established. Therefore real-time failure probabilities have to be determined using hydrological catchment models in combination with the infinite slope model. Received: 15 October 1997 · Accepted: 25 June 1997     

滑坡预警判据初步研究--以三峡库区为例

在三峡库区典型地段滑坡灾害调查评价和统计分析的基础上,结合典型滑坡变形发展的阶段性变形现象、标志和临界诱发因素分析,初步提出3个层次的滑坡预警预报判据27条,包括:(1)滑坡空间预测识别判据11条,主要用于滑坡或潜在危岩体空间识别和危险性区划,是滑坡空间预测的基本判据;(2)滑坡状态判据7条,主要用于滑坡单体稳定性评价的亚临界-临界状态预警判据,是滑坡状态预警判据系统的重要组成部分;(3)滑坡临界时间预报判据9条,主要用于单体滑坡剧烈变形或临滑预报,是滑坡时间预报研究的关键判据.     

Frequency–volume relation and prediction of rainfall-induced landslides

Rainfall-induced landslides constitute a major public concern in Hong Kong. This paper investigates two aspects of critical importance to landslide hazard and risk assessment and management: magnitude–cumulative frequency relationship for landslides, and relationship between rainfall and the occurrence of landslides. The results indicated that landslides with a failure volume of not less than 4 m³ have a cumulative frequency–size distribution with a power-law dependence on volume of failure. Analysis of rainfall/landslides showed that the 12-hour rolling rainfall is most important in predicting the number of landslides. Failure volume dependency in the relationships between rainfall and the number of landslides is also presented. However, with an increase in failure volume of landslides, the most important rainfall variable may vary from rainfall of short duration (12-h rolling rainfall) to that of relatively long duration (24-h rolling rainfall).     

基于GIS的滑坡综合预测预报信息系统

由于滑坡的复杂性、随机性和不确定性,加之目前的滑坡灾害预测预报理论研究还存在局限性,大大增加了滑坡预报的难度。本文针对目前滑坡预测预报研究中的不足,提出了滑坡综合预测预报信息系统,并对该系统的结构、功能和内容等进行了详尽地设计和开发。滑坡综合预测预报信息系统以G IS为平台,利用智能决策支持系统的理论和方法将以监测资料为依据的滑坡定量预报与以专家经验知识为依据的滑坡定性预报有机结合起来,做到了各取所长,优势互补,实现了滑坡的综合预测预报,提高了滑坡的预报水平。运用该系统不仅可以判断滑坡体的变形破坏机制、目前的稳定性状况和破坏概率,而且可以对滑坡体的变形演化阶段以及滑动时间作出预测预报,从而给滑坡现场工程师的综合判断和决策提供了重要的参考和有关科学依据的实用化工具。     

Real-time estimation of hazard for landslides triggered by rainfall

 Landslide movements triggered by rainfall can be foreseen in real-time by modelling the relationship between rainfall amount and landslide occurrence. This paper deals with the problem of the reliability of the FLaIR (Forecasting of Landslides Induced by Rainfalls) model when applied to forecasting landslide movements in the usual condition of poor historical information availability. In this case, the identification of the admissibility field for the model parameters, instead of a point estimation, leads to an improvement of the forecasting reliability. Moreover, this approach makes the model capable of taking into account information embodied in periods of heavy rain but without movement. The concepts of informative content and foreseeability of landslide movements are introduced and their duality is analyzed. The effectiveness of the estimation procedure described has been tested by application on two landslides located in southern Italy. Received: 15 October 1996 · Accepted: 25 June 1997     

基于TRIGRS模型的浅层滑坡稳定性分析

采用基于网格的瞬态降雨入渗(TRIGRS)模型,以滑坡灾害频发的陕南安康市东部巴山东段白河县为研究区,探讨模型适用性及不同降雨条件下边坡稳定性空间分布规律。根据中国土壤分布图并结合已有研究,选取模拟所需的水土力学参数。将模拟所得研究区稳定性分布图与实际滑坡目录对比分析进行TRIGRS模型精度评估,分别模拟连阴雨和短时间强降雨两种降雨情景,探讨研究区边坡稳定性空间分布规律,结果表明:1)TRIGRS模型在模拟预测降雨诱发型浅层滑坡时,结合受试者特征ROC曲线进行精度评估,曲线下面积为0.752,说明此模型在白河县进行滑坡模拟时具有一定的合理性与准确性,能反应该地区滑坡灾害的空间分布特征;2)连阴雨情景模拟下,极不稳定区域主要集中在北部低山地貌区,以冷水镇和麻虎镇为主,随降雨历时增加向东部和南部增多,西部仓上镇、西营镇和双丰镇的极不稳定区域面积较少,能承受长时间连续性降雨。短时间强降雨对边坡稳定性的影响更为直接,极不稳定区域随降雨强度增大而增加,以冷水镇和麻虎镇为主要防范区域。结合地形分析,极陡峭区域边坡稳定性最差,无法承受持续性降雨和高强度降雨,较陡峭区域更易受到降雨历时和降雨强度的影响,而平缓区域则能承受长时间及高强度的降雨;3)TRIGRS模型根据不同降雨条件预测易发生滑坡灾害的区域,为滑坡实时预报警系统提供了新的可能方法。     

Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets

An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.     

Early warning system for shallow landslides using rainfall threshold and slope stability analysis

A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent (x-variable) vs. daily rainfall (y-variable) provided the best fit to the data with a threshold equation of y = 80.7–0.1981x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is ∼20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model (PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety (FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions.     

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County,North Carolina

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.     

Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region,Garhwal Himalayas,India

Majority of landslides in the Indian sub-continent are triggered by rainfall. Several attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity-duration and antecedent rainfall models on global, regional and local scales for the occurrence of landslides. However, in the context of the Indian Himalayas, the rainfall thresholds for landslide occurrences are not yet understood fully. Neither on regional scale nor on local scale, establishing such rainfall thresholds for landslide occurrences in Indian Himalayas has yet been attempted. This paper presents an attempt towards deriving local rainfall thresholds for landslides based on daily rainfall data in and around Chamoli-Joshimath region of the Garhwal Himalayas, India. Around 128 landslides taken place in last 4 years from 2009 to 2012 have been studied to derive rainfall thresholds. Out of 128 landslides, however, rainfall events pertaining to 81 landslides were analysed to yield an empirical intensity–duration threshold for landslide occurrences. The rainfall threshold relationship fitted to the lower boundary of the landslide triggering rainfall events is I?=?1.82 D ^?0.23 (I?=?rainfall intensity in millimeters per hour and D?=?duration in hours). It is revealed that for rainfall events of shorter duration (≤24 h) with a rainfall intensity of 0.87 mm/h, the risk of landslide occurrence in this part of the terrain is expected to be high. Also, the role of antecedent rainfall in causing landslides was analysed by considering daily rainfall at failure and different period cumulative rainfall prior to failure considering all 128 landslides. It is observed that a minimum 10-day antecedent rainfall of 55 mm and a 20-day antecedent rainfall of 185 mm are required for the initiation of landslides in this area. These rainfall thresholds presented in this paper may be improved with the hourly rainfall data vis-à-vis landslide occurrences and also data of later years. However, these thresholds may be used in landslide warning systems for this particular region of the Garhwal Himalayas to guide the traffic and provide safety to the tourists travelling along this pilgrim route during monsoon seasons.     

降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模

提高降雨型滑坡危险性预警精度和空间辨识度具有重要意义.以江西宁都县1980—2001年156个降雨型滑坡为例,首先基于传统的EE-D（early effective rainfall-rainfall duration）阈值法计算不同降雨诱发滑坡的时间概率级别;然后以各级别临界降雨阈值曲线对应的时间概率为因变量,并以对应的前期有效降雨量（early effective rainfall,EE）和降雨历时（D）为自变量,采用逻辑回归拟合出上述因变量与自变量之间的非线性关系,得到降雨诱发滑坡的连续概率值;之后对比C5.0决策树和多层感知器的滑坡易发性预测性能;最后利用降雨诱发滑坡的连续概率值与易发性图相耦合以实现连续概率滑坡危险性预警.结果显示:（1）宁都降雨型滑坡连续概率值的逻辑回归方程为1/P=1+e4.062+0.747 4×D-0.079 44×EE,其拟合优度为0.983;（2）2002—2003年的20处用于连续概率阈值测试的降雨型滑坡大都落在连续概率值大于0.7的区域,只有4处落在小于0.7的区域;（3）C5.0决策树预测滑坡易发性的精度显著高于多层感知器;（4）近5年的4次降雨型滑坡的连续概率危险性值都在0.8以上,且高和极高预警区的面积较传统滑坡危险性分区更小.可见连续概率滑坡危险性预警法相较于传统危险性分区法具有更高的预警精度和空间辨识度,且通过叠加滑坡易发性图及其临界降雨阈值可开展实时滑坡危险性预警制图.      

不同降雨条件下黄土高原浅层滑坡危险性预测评价

黄土地区浅层滑坡发育非常广泛,由于其具有分布规律性差、前期变形迹象小、分布范围大、面小点多等特征,目前还无法进行有效预测,因此给黄土地区工程安全带来严重威胁。根据无限边坡模型,结合降雨入渗-土体强度衰减规律和GIS（地理信息系统）技术,构建了不同降雨条件下黄土地区浅层滑坡发育危险性评价模型,并将该评价模型应用到延河一级支流幸福川流域,预测在有效降雨量30、50、100、200 mm条件下,该流域浅层滑坡发育程度,并与当前较为流行的SINMAP模型(地形稳定性模型)进行对比。结果表明：①不稳定和潜在不稳定浅层滑坡主要分布在末级河流的两侧和源头,稳定和较稳定区域主要分布在一级河流河道两侧和塬面上;通过对比分析,SINMAP模型计算的结果与本文建立的模型在降雨强度30 mm时的计算结果较为一致。②在本文建立的模型评价结果中,随着有效降雨量的增加,F_s（稳定性系数）<1.00的不稳定区域所占比例逐渐增加,从30 mm的1.12%到200 mm的4.79%;相反,稳定区域则出现逐渐减少的趋势。③根据已发生灾害点的分布,随着有效降雨量的增加,研究区域已发生的灾害点分布在F_s<1.25的比例明显增加,从30 mm的62%到200 mm的88%,在SINMAP评价模型中,研究区域已发生的灾害点的64%分布在不稳定和潜在不稳定区域内,说明本文所建立的评价模型具有一定的精度。通过与SINMAP评价模型对比,本文建立的模型主要采用基于降雨入渗规律,而SINMAP评价模型主要基于降雨汇流过程,因此在利用过程中应根据区域特征选择利用。     

Analysis of the 2003 Varunawat Landslide,Uttarkashi, India using Earth Observation data

Mass movements such as landslides in mountainous terrains are natural degradation processes and one of the most important landscape-building factors. Varunawat Parbat overlooking Uttarkashi town witnessed a series of landslides on 23 September 2003 and the debris slides and rock falls continued for 2 weeks. This landslide complex was triggered due to the incessant rainfall prior to the event, and its occurrence led to the blockage of the pilgrim route to Gangotri (source of the Ganges river) and evacuation of thousands of people to safer places. Though there was no loss of lives due to timely evacuation, heavy losses to the property were reported. High-resolution stereoscopic earth observation data were acquired after the incidence to study the landslide in detail with emphasis on the cause of the landslide and mode of failure. Areas along the road and below the Varunawat foothill region are mapped for landslide risk. It was found that the foothill region of the Varunawat Parbat was highly disturbed by man-made activities and houses are dangerously located below steep slopes. The potential zones for landslides along with the existing active and old landslides are mapped. These areas are critical and their treatment with priority is required in order to minimise further landslide occurrences.     

Frequency–size relation of shallow debris slides on cut slopes along a railroad corridor: A case study from Nilgiri hills, Southern India

The probability of landslide volume, V _L , is a key parameter in the quantitative hazard analysis. Several studies have demonstrated that the non-cumulative probability density, p(V _L ), of landslide volumes obeys almost invariably a negative power law scaling of p(V _L ) for landslides exceeding a threshold volume and a roll-over of small landslides. Some researchers attributed the observed roll-over to under-sampling of data, while others relate it to a geo-morphological (physical) property of landslides. We analyzed 15 sets of a complete landslide inventory containing shallow debris slides (2 ≤ V _L  ≤ 3.6 × 10³ m³) with sources located on cut slopes along a 17-km-long railroad corridor. The 15 datasets belong to individual years from 1992 to 2007. We obtained the non-cumulative probability densities of landslide volumes for each dataset and analyzed the distribution pattern. The results indicate that for some datasets the probability density exhibits a negative power law distribution for all ranges of volume, while for others, the negative power scaling exists only for a volume greater than 10 m³, with scaling exponent β varying between 0.96 and 2.4. When the spatial distribution of landslides were analyzed in relation to the terrain condition and triggering rainfall, we observed that the number of landslides and the range and the frequency of volumes vary according to the changes in local terrain condition and the amount of rainfall that trigger landslides. We conclude that the probability density distribution of landslide volumes has a dependency on the local morphology and rainfall intensity and the deviation of small landslides from power law, i.e., the roll-over is a “real effect” and not an artifact due to sampling discrepancies.     

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.     

History of landslide susceptibility and a chorology of landslide-prone areas in the Western Ghats of Kerala,India

Kerala is the third most densely populated state in India. It is a narrow strip of land, of which 47% is occupied by the most prominent orographic feature of peninsular India, The Western Ghats mountain chain. The highlands of Kerala experience several types of landslides, of which debris flows are the most common. They are called “Urul Pottal” in the local vernacular. The west-facing Western Ghats scarps that runs the entire extent of the mountain system is the most prone physiographic unit for landslides. The highlands of the region experience an annual average rainfall as high as 500 cm through the South-West, North-East and Pre-Monsoon showers. A survey of ancient documents and early news papers indicates a reduced rate of slope instability in the past. The processes leading to landslides were accelerated by anthropogenic disturbances such as deforestation since the early 18th century, terracing and obstruction of ephemeral streams and cultivation of crops lacking capability to add root cohesion in steep slopes. The events have become more destructive given the increasing vulnerability of population and property. Majority of mass movements have occurred in hill slopes >20° along the Western Ghats scarps, the only exception being the coastal cliffs. Studies conducted in the state indicates that prolonged and intense rainfall or more particularly a combination of the two and the resultant pore pressure variations are the most important trigger of landslides. The initiation zone of most of the landslides was typical hollows generally having degraded natural vegetation. A survey of post-landslide investigation and news paper reports enabled the identification of 29 major landslide events in the state. All except one of the 14 districts in the state are prone to landslides. Wayanad and Kozhikode districts are prone to deep seated landslides, while Idukki and Kottayam are prone to shallow landslides.     

滑坡频度-降雨量的分形关系

降雨-滑坡关系是对降雨引发的滑坡进行预报的一个重要基础。本文根据中国浙江省1990～2003年期间有明确日期和坐标记录的1414个滑坡数据和基本覆盖浙江全部陆地区域的1257个雨量站记录的日降雨量数据,研究了滑坡频度-降雨关系。研究结果表明,降雨引发滑坡的频度与降雨量之间遵循分形的幂指数关系,并且在两个尺度的降雨量范围内具有不同的标度指数。按照幂指数关系拟合的两条滑坡累计频度-降雨关系线交点(拐点)处的降雨量(Rin)指出了引发75%左右滑坡的累计降雨阀值的上边界(以THCR表示)。对1d、4d、6d和11d这4个累计降雨时段的研究表明,引发75%左右滑坡的累计降雨阀值分别为205mm(1d)、273mm(4d)、294mm(6d)、315mm(11d)。