Calibration of the specific barrier model to Iranian plateau earthquakes and development of physically based attenuation relationships for Iran

Earthquake ground-motion relationships for soil and rock sites in Iran have been developed based on the specific barrier model (SBM) used within the context of the stochastic modeling and calibrated against up-to-date Iranian strong-motion data. A total of 171 strong-motion accelerograms recorded at distances of up to 200 km from 24 earthquakes with moment magnitudes ranging from Mw 5.2 to 7.4 are used to determine the region-specific source parameters of this model. Regression analysis was conducted using the “random effects” methodology that considers both earthquake-to-earthquake (inter-event) variability and within-earthquake (intra-event) variability to effectively handle the problem of weighting observations from different earthquakes. The minimization of the error function in each iteration of the “random effects” procedure was performed using the genetic algorithm method. The residuals are examined against available Iranian strong-motion data to confirm that the model predictions are unbiased and that there are no significant residual trends with distance and magnitude. No evidence of self-similarity breakdown is observed between the source radius and its seismic moment. To verify the robustness of the results, tests were performed to confirm that the results are unchanged if the number of observations is changed by removing different randomly selected datasets from the original database. Stochastic simulations, using the derived SBM, are then performed to predict peak ground-motion and response spectra parameters for a wide range of magnitudes and distances. The stochastic SBM predictions agree well with the new empirical regression equations proposed for Iran, Europe and Middle East in the magnitude–distance ranges well represented by the data. It has been shown that the SBM of this study provides unbiased ground-motion estimates over the entire frequency range of most engineering interests (1–10 Hz) for the Iranian earthquakes. Our results are also important for the assessment of hazards in other seismically active environments in the Middle East and Mediterranean regions.     

A risk assessment model for produced water discharge from offshore petroleum platforms-development and validation

Produced water discharge accounts for the greater portion of wastes arising from offshore oil and gas production operations. Development and expansion of Canada’s offshore oil and gas reserves has led to concerns over the potential long-term impacts of produced water discharges to the ocean. To examine this emerging environmental issue at a regional scale, an integrated risk assessment approach was developed in this study based on the princeton ocean model (POM), a random walk (RW) and Monte Carlo simulation. The use of water quality standards arrayed in a Monte Carlo design in the developed approach has served to reflect uncertainties and quantify environmental risks associated with produced water discharge. The model was validated against field data from a platform operating off Canada’s east coast, demonstrating its usefulness in supporting effective management of future produced water discharge.     

A cascade approach to continuous rainfall data generation at point locations

High-resolution temporal rainfall data sequences serve as inputs for a range of applications in planning, design and management of small (especially urban) water resources systems, including continuous flow simulation and evaluation of alternate policies for environmental impact assessment. However, such data are often not available, since their measurements are costly and time-consuming. One alternative to obtain high-resolution data is to try to derive them from available low-resolution information through a disaggregation procedure. This study evaluates a random cascade approach for generation of high-resolution rainfall data at a point location. The approach is based on the concept of scaling in rainfall, or, relating the properties associated with the rainfall process at one temporal scale to a finer-resolution scale. The procedure involves two steps: (1) identification of the presence of scaling behavior in the rainfall process; and (2) generation of synthetic data possessing same/similar scaling properties of the observed rainfall data. The scaling identification is made using a statistical moment scaling function, and the log–Poisson distribution is assumed to generate the synthetic rainfall data. The effectiveness of the approach is tested on the rainfall data observed at the Sydney Observatory Hill, Sydney, Australia. Rainfall data corresponding to four different successively doubled resolutions (daily, 12, 6, and 3 h) are studied, and disaggregation of data is attempted only between these successively doubled resolutions. The results indicate the presence of multi-scaling behavior in the rainfall data. The synthetic data generated using the log–Poisson distribution are found to exhibit scaling behaviors that match very well with that for the observed data. However, the results also indicate that fitting the scaling function alone does not necessarily mean reproducing the broader attributes that characterize the data. This observation clearly points out the extreme caution needed in the application of the existing methods for identification of scaling in rainfall, especially since such methods are also prevalent in studies of the emerging satellite observations and thus in the broader spectrum of hydrologic modeling.     

Random vibration analysis of long-span structures subjected to spatially varying ground motions

On the basis of the pseudo-excitation method (PEM), a random vibration methodology is formulated for the seismic analysis of multi-supported structures subjected to spatially varying ground motions. The ground motion spatial variability consists of the wave passage, incoherence and site–response effects. Advantages of this method are that less computation effort is required and that the cross-correlations both between normal modes and between excitations are automatically included. Random seismic responses of a realistic long-span bridge due to the wave passage, incoherence and site–response effects are extensively investigated. It is shown that all these effects have significant influence on the seismic response of the structure.     

基于FFT-MA的随机介质建模方法

随机介质是描述地球介质小尺度非均匀性的有效模型,在地震散射波场分析、储层描述等领域具有广泛应用,快速准确的随机介质建模方法是开展相关研究的基础与前提.本文首次将FFT-MA（Fast Fourier Transform Moving Average）算法引入到随机介质建模研究中,分析了该方法与传统随机介质建模方法相比具有的优势,并提出了基于FFT-MA的非平稳随机介质建模方法.建模实验表明,与传统的基于谱分解定理的随机介质建模方法相比,基于FFT-MA的方法在空间域产生随机数序列,使随机数序列与结构参数分离,因此,随机数序列与所建模型存在空间上的对应关系,可以分区域建模和局部修改模型.在非平稳随机介质模型建模时,滑动窗口的大小可以根据自相关长度变化而变化,避免了每个采样点都建立一次完整大小的模型,提高了建模效率.因此,FFT-MA随机介质建模方法能准确构建满足自相关函数要求的平稳及非平稳随机介质模型,具有建模效率高、灵活、实用的优点.

     

核设施一维土层地震反应分析中的参数不确定性

本文概述了一维土层地震反应分析等效线性化方法评价结果不确定性研究的进展,比较了中美两国核设施土层地震反应分析中参数不确定性的处理方法。基于实测数据,令参数随机变化,建立土层剖面模型,采用随机振动理论方法,分析了土层动力特性、剪切波速、基岩地震动输入界面对评价结果的影响。结果表明,土层剪切波速的不确定性对评价结果影响最大,主要表现为加速度反应谱平台段的延长。对比参数随机变化模型和最佳估计模型的计算结果可知,随机振动理论反映了土层对基岩地震动的影响,将随机模型分析结果的中值加减1倍标准差基本可以包络最佳估计模型的分析结果。     

基于经验模态分解的分数维地震随机噪声衰减方法

经验模态分解算法（EMD）是一种基于有效波和噪声尺度差异进行波场分离的随机噪声压制方法,但由于实际地震数据波场复杂,导致模态混叠较严重,仅凭该方法进行去噪很难达到理想效果.本文基于EMD算法对信号多尺度的分解特性,结合Hausdorff维数约束条件,提出一种用于地震随机噪声衰减的新方法.首先对地震数据进行EMD自适应分解,得到一系列具有不同尺度的、分形自相似性的固有模态分量（IMF）;在此基础上,基于有效信号和随机噪声的Hausdorff维数差异,识别混有随机噪声的IMF分量,对该分量进行相关的阈值滤波处理,从而实现有效信号和随机噪声的有效分离.文中从仿真信号试验出发,到模型地震数据和实际地震数据的测试处理,同时与传统的EMD处理结果相对比.结果表明,本文方法对地震随机噪声的衰减有更佳的压制效果.

     

复杂山地随机介质GMM-ULTI法射线追踪

对复杂山地介质的非均质性以及介质中地震波运动学特征进行深入研究,对于提高复杂山地区域地震勘探的效果有着重要的理论意义和实际价值.为了研究复杂山地非均质性和该介质中地震波的一些运动特性,提出了一种复杂山地随机介质的建模方法和一种新的射线追踪算法.与常规算法相比,复杂山地随机介质的生成方法采用更贴近实际介质特点的梯度介质作为背景介质,并在模型生成过程中加入地形修正步骤;新提出的GMM-ULTI射线追踪算法,充分融合群推进法、迎风思想、走时插值法的优势,采用先计算走时后追踪射线路径的两步策略完成射线追踪.算法分析与计算实例表明:复杂山地随机介质的生成方法能灵活、精细且更贴近实际地刻画复杂山地介质的非均质特点;新射线追踪算法兼顾精度和效率、能无条件稳定且灵活地适应复杂山地随机介质的特点;同时基于对几个模型试算结果的分析也得出了复杂山地随机介质中的地震波的一些传播规律.     

双稀疏字典和FISTA的地震数据去噪

地震数据的随机噪声去除是地震数据处理中的一项重要步骤,双稀疏字典提供了两层稀疏模型,比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示,然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm,FISTA）对初始字典系数进行更新,接着采用数据驱动紧标架（data-driven tight frame,DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数,最后通过DDTF字典和更新后的字典系数获得新的contourlet系数,并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比,该方法可以自适应地对地震数据进行稀疏表示,在地震数据较为复杂时得到更高的信噪比;与字典学习去噪方法相比,该方法不仅拥有较快的去噪速度,而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.