用钾-氩法求地质事件真实年龄的方法探讨

钾-氩法所测出的地质事件的表面年龄一般小于地质事件发生的真实年龄,其中很多没有地质意义。本文利用随机过程理论,在对地质体内⁴⁰Ar的逸散作用进行分析的基础上,推导出如下年龄计算公式: 在一定条件下,利用这些公式,可以据钾-氩法测试资料求出地质体形成时代t+t⁰、热扰动结束时代t⁰及热事件所持续的时期t’。据所导出的公式,对冀东迁西群地层的年龄进行讨论,求得2500Ma左右结束的那期热事件所持续的时期t’为4.76亿年,迁西群形成时代为3000—3300Ma。检验表明,公式有效。     

On the identification of ship coupled heave–pitch motions using neural networks

This paper outlines a procedure for the derivation of the differential equations describing the free response of a heaving and pitching ship from its stationary response to random waves. The coupled heave–pitch motion of a ship in random seas is modelled as a multi-dimensional Markov process. The partial differential equation describing the transition probability density function, known as the Fokker-Planck equation, for this process is derived. The Fokker-Planck equation is used to derive the random decrement equations for the coupled heave–pitch motion. The parameters in these equations are then identified using a neural network approach. The method is validated using numerical simulations and experimental results. The experimental data was obtained using an icebreaker ship model heaving and pitching in random waves. It is shown that the method produces good results when the system is lightly damped. An extension for using this method to identify couple heave–pitch motion in realistic seas is suggested.     

关于-遗传算法收敛性的注记

遗传算法是一种受到广泛注意的全局优化算法,已经在包括地震工程的很多领域中获得应用.本文将结合这一算法的实际操作步骤,对简单遗传算法的不收敛性和包含最优个体保护策略的遗传算法的收敛性给出一个简要的证明.     

基于局部采样MCMC方法的时移探地雷达反演

马尔科夫链蒙特卡洛方法(MCMC)是一种启发式的全局寻优算法,可以用来解决概率反演的问题.基于MCMC方法的反演不依赖于准确的初始模型,可以引入任意复杂的先验信息,通过对先验概率密度函数的采样来获得大量的后验概率分布样本,在寻找最优解的过程中可以跳出局部最优得到全局最优解.MCMC方法由于计算量巨大,应用难度较高,在地...     

一种基于高斯马尔可夫随机场的异常目标探测方法

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。     

马尔科夫链分析在东海陆架盆地花港组沉积微相分析中的应用

通过对东海陆架盆地某凹陷取心井岩心仔细观察和描述,采用双属性划分标准,在研究区花港组岩心中识别出了28种岩石相类型.其中砾岩相5种,砂岩相15种,细粒岩相8种.针对22口取心井岩心详细划分沉积微相和岩石相,共取得2227个岩石相数据.针对研究区发育的湖泊、三角洲、河流3种沉积体系,运用马尔科夫链分析不同沉积微相类型中岩石相沉积序列模式,建立了不同沉积微相类型可能的岩石相组合规律及岩石相定量组合概率,为后期研究相同或相似类型的沉积相提供地质知识库,并为沉积相的识别提供定量的基础.     

Application of Markovian models for non-ergodic and non-stationary earthquake times series for the identification of seismic patterns and future projections*

The current earthquake forecast algorithms are not free of shortcomings due to inherent limitations. Especially, the requirement of stationarity in the evaluation of earthquake time series as a prerequisite, significantly limits the use of forecast algorithms to areas where stationary data is not available. Another shortcoming of forecast algorithms is the ergodicity assumption, which states that certain characteristics of seismicity are spatially invariant. In this study, a new earthquake forecast approach is introduced for the locations where stationary data are not available. For this purpose, the spatial activity rate density for each spatial unit is evaluated as a parameter of a Markov chain. The temporal pattern is identified by setting the states at certain spatial activity rate densities. By using the transition patterns between the states, 1- and 5-year forecasts were computed. The method is suggested as an alternative and complementary to the existing methods by proposing a solution to the issues of ergodicity and stationarity assumptions at the same time.     

基于Markov链的金川铜镍矿床超基性岩体侵位过程模拟及找矿启示

金川铜镍矿床是世界第三大铜镍硫化物岩浆矿床,现有研究表明其成矿模式为岩浆通道成矿,但是对于金川超基性岩体的侵位过程存在较大争议。为探索岩体的侵位过程,将岩浆侵位描述为马尔可夫(Markov)过程,提出一种基于Markov链的岩体侵位模拟算法,实现对金川超基性岩体侵位过程的模拟。以金川Ⅱ矿区为例,探讨了侵位过程与矿化的关联及岩浆通道骨架,为在矿床深部寻找第二成矿空间提供方向和线索。     

基于无偏灰色马尔可夫链的吉林省降水量预测

为了更准确地对吉林省降水量进行预测,分析其时空变化特征,应用无偏灰色马尔可夫链模型对8个具有代表性的雨量站进行降水量预测,并根据预报结果讨论历史数据波动性与预报精度的关系。其中：83%以上预测结果合格,白城、乾安、长春、蛟河、四平、通化6个地区降水量多年呈递减趋势,减幅分别为0.23%、0.09%、0.24%、1.01%、0.51%、0.54%;延吉、靖宇2个地区降水量多年呈递增趋势,增幅分别为2.60%、0.54%。结果表明：无偏灰色马尔可夫链模型预测精度较高,说明该方法适用于吉林省的降水量预测;吉林省中西部地区降水量呈递减趋势,东部地区呈递增趋势,但变幅不大;在波动性与预报精度的关系方面,时间序列的波动性越大预测所产生的误差越大。     

基于模糊分类的UBGM(1,1)-Markov模型在露天矿边坡沉降预测中的应用

针对UBGM(1,1)-Markov模型中存在2个邻近值可能被归属到不同状态,导致预测值产生偏差的问题,结合模糊分类理论,构建基于模糊分类的无偏灰色-马尔科夫模型(unbiased gray-Markov model based on fuzzy classification, FC-UBGM(1,1)-Markov)。首先对UBGM(1,1)模型进行残差修正,然后将修正后拟合值的相对残差序列作为Markov链进行区间划分,再结合模糊分类的隶属度函数,计算相对残差的模糊向量,根据隶属度确定其所属的状态。实际算例表明,该模型比传统UBGM(1,1)-Markov模型的预测效果更好。