青藏高原及邻区未来地震活动性趋势数值分析          下载免费PDF全文

董培育  石耀霖  程惠红  乔学军 《地球物理学报》2020,63(3):1155-1169

区域中长期地震危险性数值分析研究,需要对其初始构造应力场有所了解,但目前以及未来一段时期内仍无法直接观测到深部孕震层区域的应力场状况.本文首先基于岩石库仑-摩尔破裂准则,利用青藏高原及邻区百年历史范围内的强震信息,来反演估算该区域的初始应力场.然后,考虑区域构造应力加载及强震造成的应力扰动共同作用,重现了历史强震的发展过程.然而对于初始应力场的反演估算,本文仅能给出区域其上下限的极限值,并不能唯一确定.因此,采用Monte Carlo随机法,进行大量独立的随机试验计算,生成数千种有差异的区域初始应力场模型,且保证每种模型都能令历史强震有序发生,但未来应力场演化过程不尽相同.最后,将数千种模型在未来时间段内的危险性预测结果集成为数理统计结果,据此给出了区域未来的地震危险性概率分布图.初步结果显示未来强震危险性概率较高地区集中在巴颜喀拉块体边界及鲜水河断裂带地区.  相似文献   

Reliability of mooring lines and piles for a permanently manned vessel in a tropical cyclone environment     

《Applied Ocean Research》2019

The factors of safety for stationkeeping systems in current standards (ISO, API) are not derived or validated using reliability analyses. As the oil and gas exploration and production is breaking new boundaries, deploying new floating systems and moving into regions with harsher environments, it is of paramount importance to understand what level of reliability these new marine structures are achieving. This paper presents a reliability analysis of the mooring system of a Floating LNG (FLNG) vessel permanently moored and permanently manned offshore North West Australia in a tropical cyclone environment. The reliability analysis addresses both the mooring chain and the pile foundation. The analysis accounts for the long term characteristics of the environment, including the short term variability, in response to a given sea state and the variability and uncertainty in strength of the mooring chain and the pile. The stationkeeping system was analysed using detailed time domain simulations, capturing system non-linearities and low frequency oscillations as well as wave frequency responses and, thereby, reducing modelling uncertainties to a minimum.It is found that for the conditions modelled, neither the chain nor the pile meet a target reliability of 10⁻⁴/annum using the factors of safety commonly used in design following current ISO and API standards. New factors of safety are proposed to achieve this target reliability. For the pile design, one complicating factor is that current design standards do not explicitly define the exceedance probability that should be associated with the characteristic value of the undrained shear strength to be used in the design. It is demonstrated that the required factor of safety is crucially dependent on the definition of this characteristic value and on the level and the type of uncertainty in the soil strength profile. A recommendation is made regarding the definition of this characteristic value and the associated factor of safety. Furthermore, it is found that designing the mooring system to an environmental condition with a return period of 10,000 yr (as an Abnormal Limit State event), and setting the factor of safety to unity, meets the target reliability of 10⁻⁴/annum for the pile, if the characteristic undrained shear strength is a lower bound, defined in this paper by the 10th percentile value. For the chain however, this target reliability is not achieved.  相似文献   

联合多点地质统计学与序贯高斯模拟的随机反演方法   总被引：1，自引：0，他引：1       下载免费PDF全文

刘兴业  李景叶  陈小宏  李超  郭康康  周林 《地球物理学报》2018,61(7):2998-3007

岩相和储层物性参数是油藏表征的重要参数，地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论，能够对不同类型的信息源进行综合，建立具有较高分辨率的储层模型，因而得到广泛关注.其中，概率扰动方法是一种高效的迭代随机反演策略，它能综合考虑多种约束信息，且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中，本文有效的联合多点地质统计学与序贯高斯模拟，并结合统计岩石物理理论实现随机反演.首先，通过多点地质统计学随机模拟，获得一系列等可能的岩相模型，扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型；然后通过统计岩石物理理论，计算相应的弹性参数；最后，正演得到合成地震记录并与实际地震数据对比，通过概率扰动方法进行迭代，直到获得满足给定误差要求的反演结果.利用多点地质统计学，能够更好地表征储层空间特征.相控序贯高斯模拟的应用，能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果，模型测试和实际数据应用验证了方法的可行性和有效性.  相似文献   

Effect of 2D spatial variability on slope reliability: A simplified FORM analysis     

《地学前缘(英文版)》2018,9(6):1631-1638

To meet the high demand for reliability based design of slopes, we present in this paper a simplified HLRF(Hasofere Linde Rackwitze Fiessler) iterative algorithm for first-order reliability method(FORM). It is simply formulated in x-space and requires neither transformation of correlated random variables nor optimization tools. The solution can be easily improved by iteratively adjusting the step length. The algorithm is particularly useful to practicing engineers for geotechnical reliability analysis where standalone(deterministic) numerical packages are used. Based on the proposed algorithm and through direct perturbation analysis of random variables, we conducted a case study of earth slope reliability with complete consideration of soil uncertainty and spatial variability.  相似文献   

FLAC3D在边坡数值分析及位移监测中应用     

张颖  孙兆涛  裴仲文 《探矿工程》2018,45(3):57-61

以辽宁省岫岩县某山头边坡为例,通过槽探、钻探、大型剪切试验等多种手段,找出合理潜在滑动面。利用FLAC3D软件对该边坡进行三维数值模拟,分析应力应变关系,得出变形较大及应力集中区域,同时利用强度折减法得出边坡的安全系数,评价其稳定性。将从边坡后缘张拉裂隙上布设的位移监测点采集到的数据,与数值模拟结果进行对比分析,验证数值模拟的可靠性,为边坡抗滑设计提供依据。  相似文献   

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts     

赵滨  张博  李子良 《热带气象学报(英文版)》2021,27(1):24-33

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.  相似文献   

2010—2018年辽宁省雷电流幅值特征分析     

程攀  曾杨  程雷  庞文静  肖光梁  荣先远 《气象与环境学报》2021,37(4):107-113

基于辽宁省2010-2018年闪电定位（ADTD）资料,运用统计学方法分析了雷电流幅值时间变化特征;运用规程计算公式和IEEE推荐公式分别计算了雷电流幅值累积概率密度,并和实际地闪雷电流幅值累积概率密度曲线做了对比分析;运用最小二乘法拟合了IEEE推荐公式。结果表明：2010-2018年辽宁省地闪以负闪为主,占比高达89%,而负地闪雷电流幅值主要集中于-50~-20 kA;地闪频次在2011-2013年逐年升高,而后逐年减少,总地闪和负地闪的平均雷电流幅值自2010-2013年逐年降低,而后逐年升高;地闪主要发生在汛期的7-8月,平均雷电流幅值在冬季最高,且日变化平稳;雷电流幅值为20-50 kA的总地闪和负地闪累积概率密度曲线下降最快,而雷电流幅值在20 kA左右的累积概率密度曲线开始下降,总体下降速度较慢;通过对IEEE推荐公式进行拟合,拟合后的雷电流幅值累积概率密度分布曲线更加接近实际。  相似文献   

基于离心机模型试验的甘肃江顶崖古滑坡复活机理研究     

郭长宝  张永双  刘定涛  滑帅  任三绍  李雪  金继军 《工程地质学报》2022,30(1):164-176

位于白龙江断裂带的甘肃舟曲江顶崖古滑坡规模巨大,受断裂活动、降雨入渗与河流侵蚀和人类工程活动等因素影响,多次发生复活-堵塞白龙江灾害事件,造成极大危害。为研究江顶崖古滑坡的复活机理,本文在野外地质调查的基础上,重点开展了滑体在含水率为10%、15%和20%条件下的离心机模型试验。研究表明:在滑体含水率为10%情况下,试验结束后仅在坡体中后部产生少量裂缝,但滑坡体整体还处于稳定状态; 而在滑体含水率为15%和20%情况下,滑坡均发生了破坏,在滑体含水率分别为15%、20%情况下坡体失稳所需离心加速度分别为100g和50g。试验测试分析表明,江顶崖古滑坡为推移式滑坡,其变形先从坡体中后部开始,坡体中后部产生裂缝,随后裂缝逐渐向前缘扩展,最终裂缝贯通造成滑坡滑动破坏。滑坡体的变形过程主要分为3个阶段: ①变形启动阶段(裂缝开始形成阶段); ②变形加速阶段(裂缝加速发展阶段); ③失稳阶段。通过离心模拟试验,结合野外调查分析,认为江顶崖古滑坡复活的因素主要受降雨和孔隙水压力的影响,是受前缘河流侵蚀牵引、降雨入渗造成滑坡中后部推移的耦合滑动。  相似文献   

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer   总被引：1，自引：0，他引：1  

Wei Chen  Xi Chen  Jianbing Peng  Mahdi Panahi  Saro Lee 《地学前缘(英文版)》2021,12(1):93-107

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks     

Husam A.H.Al-Najjar  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(2):625-637

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献