1918年南澳7.3级地震与强余震之参数复核

本文利用早期国际地震观测报告,采用现代地震定位理论和技术,对1918年南澳7．3级地震与2次强余震之参数进行了复核确定。结果表明,新核定的南澳地震及其强余震震中位置在南澎岛附近,是现代小震密集带,具有强地震活动的构造背景。     

Effect of soil parameter uncertainty on seismic demand of low-rise steel buildings on dense silty sand

Focusing on low-rise steel buildings supported by shallow isolated foundations on dense silty sand, this study demonstrates the effect of uncertainty in soil parameters on seismic response of structures. Considering a set of 20 ground motions representing 10% in 50 years hazard level and concentrating on peak base moment, base shear and interstory drift as the demand variables of interest, it is found that uncertainty in soil parameters may result in significant response variability of the structures, especially when vertical factor of safety is low and the structure is relatively stiff. Uncertainty in friction angle results in significant variability of the peak base moment and base shear, while peak interstory drift ratio is found to be virtually unaffected by uncertainty in soil parameters. It is also found that a linear soil–structure-interaction (SSI) model will not be able to predict such response variability under these set of ground motions.     

人工拟合地震动时程参数对场地反应的影响分析

采用目标谱拟合法人工合成地震动时程,拟合过程中对包络线参数进行定量的变化,进行土层地震反应分析.根据其对场地反应的影响,讨论时程参数对人工拟合地震动时程的影响规律,为场地抗震设防参数的选取提供依据.研究结果表明:t1、t2和c不同时,所拟合的地震动时程对土层地震反应结果有较大影响,其影响程度与土层结构关系密切.     

关于改进我国抗震设计反应谱的探讨

本文回顾了地震反应分析理论发展的三个阶段,指出反应谱理论仍然是许多国家现阶段抗震设计的理论依据,我国规范也主要采用反应谱法进行抗震设计分析。进一步讨论了反应谱与抗震设计反应谱的联系和区别。把我国《建筑抗震设计规范(GB50011—2001)》中有关地震影响系数分别与ISO 3010:2001(E)和美国的建筑抗震设计规范中的地震影响系数做了对比,发现我国规范有些地方考虑得还不够全面,还需要不断地完善。提出在抗震设计中运用地震动参数代替地震烈度来进行计算能更好地反映工程实际情况。     

Calibration of complex subsurface reaction models using a surrogate-model approach

Automatic calibration of complex subsurface reaction models involves numerous difficulties, including the existence of multiple plausible models, parameter non-uniqueness, and excessive computational burden. To overcome these difficulties, this study investigated a novel procedure for performing simultaneous calibration of multiple models (SCMM). By combining a hybrid global-plus-polishing search heuristic with a biased-but-random adaptive model evaluation step, the new SCMM method calibrates multiple models via efficient exploration of the multi-model calibration space. Central algorithm components are an adaptive assignment of model preference weights, mapping functions relating the uncertain parameters of the alternative models, and a shuffling step that efficiently exploits pseudo-optimal configurations of the alternative models. The SCMM approach was applied to two nitrate contamination problems involving batch reactions and one-dimensional reactive transport. For the chosen problems, the new method produced improved model fits (i.e. up to 35% reduction in objective function) at significantly reduced computational expense (i.e. 40–90% reduction in model evaluations), relative to previously established benchmarks. Although the method was effective for the test cases, SCMM relies on a relatively ad-hoc approach to assigning intermediate preference weights and parameter mapping functions. Despite these limitations, the results of the numerical experiments are empirically promising and the reasoning and structure of the approach provide a strong foundation for further development.     

An integrated optimization algorithm for parameter structure identification in groundwater modeling

The inverse problem of parameter structure identification in a distributed parameter system remains challenging. Identifying a more complex parameter structure requires more data. There is also the problem of over-parameterization. In this study, we propose a modified Tabu search for parameter structure identification. We embed an adjoint state procedure in the search process to improve the efficiency of the Tabu search. We use Voronoi tessellation for automatic parameterization to reduce the dimension of the distributed parameter. Additionally, a coarse-fine grid technique is applied to further improve the effectiveness and efficiency of the proposed methodology. To avoid over-parameterization, at each level of parameter complexity we calculate the residual error for parameter fitting, the parameter uncertainty error and a modified Akaike Information Criterion. To demonstrate the proposed methodology, we conduct numerical experiments with synthetic data that simulate both discrete hydraulic conductivity zones and a continuous hydraulic conductivity distribution. Our results indicate that the Tabu search allied with the adjoint state method significantly improves computational efficiency and effectiveness in solving the inverse problem of parameter structure identification.     

初始误差和参数误差对混沌系统可预报性影响的比较

利用非线性误差增长理论,以Lorenz系统为例比较研究了初始误差和参数误差对混沌系统可预报性的影响．结果表明：在初始误差和参数误差单独存在时,系统的可预报期限随误差大小的变化规律基本上相同;对于相同的误差大小,初始误差和参数误差对系统可预报期限的影响几乎相同,这一结果基本上不随参数范围的变化而变化．当初始误差和参数误差同时存在时,两者对可预报期限影响所起的作用大小主要取决于初始误差和参数误差的相对大小．当初始误差远大于参数误差时,Lorenz系统的可预报期限主要由初始误差决定,可以不用考虑参数误差对预报模式可预报性的影响;反之,当参数误差远大于初始误差时,Lorenz系统的可预报期限主要由参数误差决定;当初始误差和参数误差大小相当时,两者都对系统的可预报期限起重要作用．在后两种情况下,在考虑初始误差对可预报性影响的同时还必须考虑参数误差的作用．这提醒我们在作实际数值天气预报的时候,不仅要重视初值的确定,也要重视数值模式控制参数的确定．     

基于谐波建模和自相关的磁共振信号消噪与提取方法研究

磁共振探测技术（Magnetic Resonance Sounding,MRS）以其无损、定量、直接等优势,被广泛应用于地下水调查、水文环境评价以及灾害水源预警等领域.在实际应用中,强工频谐波和随机噪声等严重影响MRS信号的质量,导致后续水文参数解释不准确.针对这一问题,提出谐波建模和自相关相结合的方法进行消噪以及信号特征参数提取.首先构建工频谐波模型,针对建模算法严重依赖工频基频精度的问题,采用自适应扫描方式搜索方案,大幅提高搜索准确度和速度;其次推导了MRS信号自相关表达式,提出了自相关参数提取的非线性拟合方法.仿真数据结果表明,建模消噪方法有效消除了工频谐波,信噪比平均提升了17.03 dB;自相关处理后,信噪比进一步提升了16.10 dB,初始振幅和弛豫时间参数提取结果的准确度比处理前分别提高了3.8倍和2.8倍.通过不同信噪比和弛豫时间的重复实验,得到当噪声水平小于200 nV和弛豫时间大于200 ms时,自相关参数提取具有较高的稳定性.最后,通过野外实测数据处理实验,进一步验证了联合消噪和参数提取方法的有效性.     

Correlation‐based reflection waveform inversion by one‐way wave equations

Reflection full waveform inversion can update subsurface velocity structure of the deeper part, but tends to get stuck in the local minima associated with the waveform misfit function. These local minima cause cycle skipping if the initial background velocity model is far from the true model. Since conventional reflection full waveform inversion using two‐way wave equation in time domain is computationally expensive and consumes a large amount of memory, we implement a correlation‐based reflection waveform inversion using one‐way wave equations to retrieve the background velocity. In this method, one‐way wave equations are used for the seismic wave forward modelling, migration/de‐migration and the gradient computation of objective function in frequency domain. Compared with the method using two‐way wave equation, the proposed method benefits from the lower computational cost of one‐way wave equations without significant accuracy reduction in the cases without steep dips. It also largely reduces the memory requirement by an order of magnitude than implementation using two‐way wave equation both for two‐ and three‐dimensional situations. Through numerical analysis, we also find that one‐way wave equations can better construct the low wavenumber reflection wavepath without producing high‐amplitude short‐wavelength components near the image points in the reflection full waveform inversion gradient. Synthetic test and real data application show that the proposed method efficiently updates the background velocity model.     

Bayesian inversion of joint SH seismic and seismoelectric data to infer glacier system properties

In glacial studies, properties such as glacier thickness and the basement permeability and porosity are key to understand the hydrological and mechanical behaviour of the system. The seismoelectric method could potentially be used to determine key properties of glacial environments. Here we analytically model the generation of seismic and seismoelectric signals by means of a shear horizontal seismic wave source on top of a glacier overlying a porous basement. Considering a one-dimensional setting, we compute the seismic waves and the electrokinetically induced electric field. We then analyse the sensitivity of the seismic and electromagnetic data to relevant model parameters, namely depth of the glacier bottom, porosity, permeability, shear modulus and saturating water salinity of the glacier basement. Moreover, we study the possibility of inferring these key parameters from a set of very low noise synthetic data, adopting a Bayesian framework to pay particular attention to the uncertainty of the model parameters mentioned above. We tackle the resolution of the probabilistic inverse problem with two strategies: (1) we compute the marginal posterior distributions of each model parameter solving multidimensional integrals numerically and (2) we use a Markov chain Monte Carlo algorithm to retrieve a collection of model parameters that follows the posterior probability density function of the model parameters, given the synthetic data set. Both methodologies are able to obtain the marginal distributions of the parameters and estimate their mean and standard deviation. The Markov chain Monte Carlo algorithm performs better in terms of numerical stability and number of iterations needed to characterize the distributions. The inversion of seismic data alone is not able to constrain the values of porosity and permeability further than the prior distribution. In turn, the inversion of the electric data alone, and the joint inversion of seismic and electric data are useful to constrain these parameters as well as other glacial system properties. Furthermore, the joint inversion reduces the uncertainty of the model parameters estimates and provides more accurate results.