The Effect of Bayesian Updating in the Hazard Assessment of Submarine Landslides

Roneet Das, P. Varela, Z. Medina-Cetina
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引用次数: 2

Abstract

This paper introduces a Bayesian methodology to conduct landslide hazard assessment. The proposed approach demonstrates how a probabilistic method can incorporate evolving information about a site for progressively more certain geotechnical characterization. The probabilistic method presented herein is called the Bayesian framework, which integrates a physics-based model defining certain characteristic or phenomenon related to the site, state of evidence on the model parameters, and experimental observations to produce an updated state of evidence on the model parameters and more confident model predictions. This study focuses on landslide geohazard of a site using the physics-based infinite block slope model to estimate the probability of submarine slope failure. The probability of failure against sliding is estimated using the predictions of the infinite slope model under static loading condition for different states of evidence on the model parameters. A state of evidence reflects the level of knowledge about a parameter which describes an attribute of the site such as bathymetry or geotechnical properties of the in-situ soil. This research studies the influence of increasing states of evidence on the confidence gain in model predictions and subsequent updates in the estimates of probability of failure. Predictions based on the infinite slope model are made using the Monte-Carlo algorithm through random sampling of the model parameters. The state of evidence on the model parameters is incorporated in the algorithm by considering the model parameters as random variables following a probability distribution function. These probability distributions, also known as the prior probability distributions, represent the initial state of evidence on the model parameters. The Bayesian framework is used to conduct sequential calibration of the infinite slope model using synthetically generated data on the shear strength of the in-situ soil. These experimental observations represent the state of evidence on the soil conditions. In this paper two sets of data containing 5 and 20 data ‘sample’ points, respectively are used to calibrate the infinite slope model. Calibration of the model results in an updated state of evidence on the model parameters and generates a new set of probability distributions known as the posterior probability distributions. The posterior distributions more accurately describe the potential range of value that the parameters can attain. Comparison between the model predictions based on the initial state of evidence and the updated states of evidence shows a gain in the certainty of the model predictions.
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贝叶斯更新在海底滑坡危险性评价中的作用
本文介绍了一种用于滑坡危险性评价的贝叶斯方法。所提出的方法演示了概率方法如何能够将有关站点的不断发展的信息纳入逐步更确定的岩土特性。本文提出的概率方法被称为贝叶斯框架,它将一个基于物理的模型集成在一起,该模型定义了与现场相关的某些特征或现象、模型参数的证据状态和实验观测,从而产生了一个关于模型参数的最新证据状态和更可靠的模型预测。本文以某场地的滑坡地质灾害为研究对象,采用基于物理的无限块体边坡模型估计海底边坡破坏的概率。利用静力加载条件下无限斜率模型的预测结果,对模型参数的不同状态进行了抗滑破坏概率的估计。证据状态反映了对描述遗址属性的参数的知识水平,例如原位土壤的测深或岩土力学特性。本研究研究了证据状态的增加对模型预测的置信度增益的影响,以及随后对失效概率估计的更新。通过对模型参数的随机抽样,利用蒙特卡罗算法对无限斜率模型进行预测。通过将模型参数视为服从概率分布函数的随机变量,将模型参数的证据状态纳入算法。这些概率分布,也称为先验概率分布,表示模型参数上证据的初始状态。利用综合生成的土抗剪强度数据,采用贝叶斯框架对无限边坡模型进行序贯定标。这些实验观察结果代表了有关土壤条件的证据状况。本文使用两组数据分别包含5和20个数据“样本”点来校准无限斜率模型。模型的校准会导致模型参数上证据的更新状态,并生成一组新的概率分布,称为后验概率分布。后验分布更准确地描述了参数可能达到的值的潜在范围。将基于证据初始状态的模型预测与基于证据更新状态的模型预测进行比较,表明模型预测的确定性有所提高。
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