基于FOCMEC方法反演震源机制解的交互式程序研制与使用

基于P、SV、SH波的初动和振幅比联合反演震源机制解程序包(FOCMEC),结合我国地震波资料的保存格式,利用Delphi面向对象语言,开发交互式FOCMEC方法反演震源机制解程序,并详细介绍了计算原理、使用方法和注意事项。通过严格的测试和对比,认为该程序反演结果准确可靠。     

用微震台网对2006年兰州M_S2.5地震定位和震源机制研究

利用兰州临时微震台网的数字记录对2006年4月19日兰州Ms2．5地震进行了定位和震源机制反演，用P波初动和矩张量反演方法得出的震源机制具有较好的一致性。结合该地区的地震分布对可能的发震断层进行了讨论。     

利用P波初动及振幅比初步计算2005年11月26日九江―瑞昌地震余震震源机制

应用Jeanne L.Hardebeck和Peter M.Shearer编制的利用P波初动和振幅比计算地震震源机制解的程序,使用九江—瑞昌流动地震台观测的地震资料,初步计算了九江—瑞昌地震余震的震源机制;并对单独利用P波初动和利用初动及振幅比这两种计算方法进行了比较。综合最优计算结果,九江—瑞昌地震余震震源机制解为:走向174°,倾角62°,滑动角43°。     

利用P波初动及振幅比初步计算2005年11月26日九江-瑞昌地震余震震源机制

应用Jeanne L.Hardebeck和Peter M.Shearer编制的利用P波初动和振幅比计算地震震源机制解的程序,使用九江-瑞昌流动地震台观测的地震资料,初步计算了九江-瑞昌地震余震的震源机制;并对单独利用P波初动和利用初动及振幅比这两种计算方法进行了比较.综合最优计算结果,九江-瑞昌地震余震震源机制解为:走向174°,倾角62°,滑动角43°.     

中俄交界4.8级地震综合震源机制解及构造应力场

利用P波初动求解中俄交界4.8级地震的综合震源机制,通过震源机制反演震源区附近的应力场,结果表明:此次地震是略带走滑分量的正断层.主压应力场的方向是北北东方向,沿此断裂带展布.     

基于最大顺序统计量的P波初动极性自动读取方法的小震震源机制反演及在云南小江地区的应用研究

地震P波震相到时与初动极性的精确读取是地震资料分析处理的重要步骤之一.本研究采用了一种新的高精度自动确定地震波P波震相到时与初动极性的概率分布的方法(POI方法,后同),在此基础上研究开发了一套自动化小震震源机制解的反演流程,并将其应用在云南小江断裂带周边区域的小震震源机制和构造应力场反演中.得到的震源机制与应力场反演结果与前人的研究一致,且与区域GPS观测结果和构造背景吻合度较好,显示该方法具有良好的应用前景,为高效分析密集台阵地震观测资料,开展小震震源参数测量与区域应力场反演提供了新的研究思路和技术手段.     

基于最大顺序统计量的P波初动极性自动读取方法的小震震源机制反演及在云南小江地区的应用研究

地震P波震相到时与初动极性的精确读取是地震资料分析处理的重要步骤之一.本研究采用了一种新的高精度自动确定地震波P波震相到时与初动极性的概率分布的方法(POI方法,后同),在此基础上研究开发了一套自动化小震震源机制解的反演流程,并将其应用在云南小江断裂带周边区域的小震震源机制和构造应力场反演中.得到的震源机制与应力场反演结果与前人的研究一致,且与区域GPS观测结果和构造背景吻合度较好,显示该方法具有良好的应用前景,为高效分析密集台阵地震观测资料,开展小震震源参数测量与区域应力场反演提供了新的研究思路和技术手段.     

求震源机制P波初动解的格点尝试概率法

考虑到Ｐ波初动数据在震源球面上分布的不均匀性对节面解的可能影响，不用矛盾符号所占比例的大小作为选择可取节面解的标准，而是将震源球面分成许多小块，再确定有Ｐ波初动数字的小块为正号区或负号区的概率，最后以全部有数据小块的平均概率的高低作为选择解答的标准。用此改进方法重新测定了徐纪仁等（Ｘｕ ｅｔ．ａｌ，，１９８８）发表的青藏高原地区９９个地震的震源机制解，从中筛选出了２０个可信的解答。     

利用FOCMEC方法计算震源机制解的影响因素分析——以九江—瑞昌Ms5.7地震为例

应用Snoke最新发展的利用P波、SV波和SH波的初动和振幅比联合计算震源机制解的方法(FOCMEC),以2005年11月26日九江—瑞昌MS5.7地震为例,分析速度结构模型、震源定位误差、初动与振幅比资料和台站分布等对计算震源机制解的影响.结果表明,FOCMEC方法应采用研究区内的精细速度结构模型,各层速度误差在5％...     

2010年1月24日山西河津-万荣Ms4.8地震特征

分析2010年1月24日山西河津-万荣Ms4.8地震序列特征,利用P波初动及振幅比和波形反演CAP两种方法计算震源机制解,结合本次地震的地质构造和历史地震活动背景、震源机制、余震分布、极震区长轴方向等分析,结果表明,隐伏的西辛封断裂为发震构造的可能性较大.     

基于COI基因序列的太湖新银鱼遗传多样性

利用线粒体细胞色素C氧化酶I(COI)分子标记分析长江中下游太湖新银鱼(Neosalanx taihuensis)8个地理种群132个样本的遗传多样性.该基因630 bp片段的碱基序列共检出8个核苷酸变异位点(变异率1.27%),其中局域性单倍型居多(75%),群体单倍型多样性较高(h=0.576±0.036),而核苷酸多样性较低(π=0.00112±0.00204).不同地理种群遗传多样性差异显著:有人工移植历史种群遗传多样性较高、隔离度较高的种群遗传多样性较低,但大部分的遗传变异来自于种群内(54.83%),反映出地理隔离和人为干扰对太湖新银鱼遗传格局影响显著.研究表明COI基因适于银鱼科鱼类物种鉴别和系统发育研究,同时可为同种种群间遗传关系分析提供一定的信息.     

Multiscale island injection genetic algorithms for groundwater remediation

Genetic algorithms have been shown to be powerful tools for solving a wide variety of water resources optimization problems. Applying these approaches to complex, large-scale water resources applications can be difficult due to computational limitations, especially when a numerical model is needed to evaluate different solutions. This problem is particularly acute for solving field-scale groundwater remediation design problems, where fine spatial grids are often needed for accuracy. Finer grids usually improve the accuracy of the solutions, but they are also computationally expensive. In this paper we present multiscale island injection genetic algorithms (IIGAs), in which the optimization algorithms have different multiscale populations working on different islands (groups of processors) and periodically exchanging information. This new approach is tested using a field-scale pump-and-treat design problem at the Umatilla Army Depot in Oregon, USA. The performance of several variations of this approach is compared with the results of a simple genetic algorithm. The new approach found the same solution as much as 81% faster than the simple genetic algorithm and 9–53% faster than other previously formulated multiscale strategies. These findings indicate substantial promise for multiscale IIGA approaches to improve solution of complex water resources applications at the field scale.     

Study of Volcanic Sources at Long Valley Caldera,California, Using Gravity Data and a Genetic Algorithm Inversion Technique

We model the source inflation of the Long Valley Caldera, California, using a genetic algorithm technique and micro-gravity data. While there have been numerous attempts to model the magma injection at Long Valley Caldera from deformation data, this has proven difficult given the complicated spatial and temporal nature of the volcanic source. Recent work illustrates the effectiveness of considering micro-gravity measurements in volcanic areas. A genetic algorithm is a problem-solving technique which combines genetic and prescribed random information exchange. We perform two inversions, one for a single spherical point source and another for two-sources that might represent a more spatially distributed source. The forward model we use to interpret the results is the elastic-gravitational Earth model which takes into account the source mass and its interaction with the gravity field. The results demonstrate the need to incorporate more variations in the model, including another source geometry and the faulting mechanism. In order to provide better constraints on intrusion volumes, future work should include the joint inversion of gravity and deformation data during the same epoch.     

基于匹配追踪和遗传算法的大地电磁噪声压制

针对匹配追踪计算量大、大地电磁数据处理效率低的问题,提出基于匹配追踪和遗传算法的大地电磁噪声压制方法.首先,利用Gabor原子构建过完备原子库,并对过完备原子库集合进行划分.然后,借助遗传算法的自适应性,快速搜寻最优匹配原子及所在位置.最后,运用最优匹配原子对待处理信号进行稀疏分解,重构有用信号.通过对计算机模拟的典型强干扰和矿集区实测大地电磁数据进行分析处理,实验结果表明,相对于匹配追踪和正交匹配追踪,文中所提方法能从过完备原子库中快速、自适应地选取最优匹配原子与不同噪声干扰类型高精度的匹配,极大地提升了计算效率;大地电磁时间域序列中的大尺度强干扰被有效剔除,视电阻率曲线更为光滑、连续,低频段的数据质量得到明显改善.     

四川省城市地震灾害脆弱性综合评价研究

城市化进程的不断推进使得城市的地震灾害脆弱性日益加剧,而城市承灾体的脆弱性受复杂因素影响。本文针对评价指标受主观性影响较大的问题,基于人口、工程、经济及社会4个方面,构建了城市震害综合脆弱性评价指标体系;并构建基于实码加速遗传算法优化投影寻踪（RAGA-PP）的城市震害脆弱性评价模型;最后,对四川省21个市、州进行了震害脆弱性评价。结果表明:巴中、南充等地脆弱性较高,成都、攀枝花等地脆弱性较低;经济因素对城市震害脆弱性影响较大;该评价模型能够克服人为主观性,有效可行。     

利用余震震源分布确定主震断层面的方法研究

利用余震震源位置的空间分布,采用Newton-Raphson算法和遗传算法确定主震断层面参数的方法,对仿真数据求出了地震主断层面走向角和倾角,验证了方法的有效性。该方法可以与其他数据结合共同约束主震断层面的参数。     

1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach

Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step.     

用遗传算法实现地震信号反褶积

遗传算法作为寻优手段具有全局优化和很好的稳定性．本文将遗传算法用于地震信号反褶积处理，与已往方法相比它具有更好的分辨率和稳定性我们采用Bernoulli－Gaussian模型和ARMA模型分别描述地震反射系数序列和地震子波，用最大似然和最小预测误差准则分别构造用于估计反射系数序列和地震子波的目标函数，用遗传算法优化目标函数，以实现地震信号反褶积．     

Application of genetic BP network to discriminating earthquakes and explosions

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…