吉尔吉斯斯坦布丘克造山型金矿床：来自流体包裹体、H- O- S同位素证据

吉尔吉斯斯坦北天山构造带的矿床学数据缺乏,制约了天山造山带境内外成矿对比。布丘克金矿床位于吉尔吉斯斯坦北天山构造带中部。金矿体为石英复脉,呈带状发育于NWW向韧性剪切带中。矿体倾向SSW,倾角60°～70°,赋矿围岩主要为侵入于早古生代变质碎屑杂岩中的正长斑岩。布丘克金矿床成矿期石英流体包裹体观察、石英H-O同位素、硫化物S同位素测试结果显示,布丘克金矿床石英脉中包裹体大小集中在2～10μm之间,类型以H₂O-CO₂型、富CO₂型、水溶液型包裹体为主,成分以富CO₂、含CH₄为特征。成矿流体具有中温(200～320℃)、低盐度(3%～7%NaCl_eqv)特征;石英δD_V-SMOW值介于-108.1‰～-90.2‰之间,δ¹⁸O_流体值介于4.86‰～9.26‰之间;黄铁矿δ³⁴S分布在0‰左右(-0.9‰～1.6‰)。综合本文数据、矿床地质特征、区域地质资料,本文认为布丘...     

遗传算法求解最大预报误差的可行性研究

利用条件非线性最优扰动（conditional nonlinear optimal perturbation,CNOP）可以实现最大预报误差的上界估计。CNOP通常由基于梯度信息的约束优化算法进行求解,且其中的梯度信息由伴随模式提供。然而当非线性模式中含不连续＂开关＂时,传统伴随方法不能为优化过程提供正确的梯度方向,从而导致优化失败。为此,采用自适应变异和混合交叉的遗传算法,联赛选择机制和小生境技术的约束处理方法来求解最大预报误差上界。为检验新方法的有效性,以修改的Lorenz模型作为预报模式,对3个初始态分别用新方法和传统伴随方法进行比较,数值试验结果显示新方法求解出的最大预报误差的上界更加精确。     

Intelligent optimization algorithms to VDA of models with on/off parameterizations

Some variational data assimilation (VDA) problems of time- and space-discrete models with on/off parameterizations can be regarded as non-smooth optimization problems. Same as the sub-gradient type method, intelligent optimization algorithms, which are widely used in engineering optimization, can also be adopted in VDA in virtue of their no requirement of cost functions gradient (or sub-gradient) and their capability of global convergence. Two typical intelligent optimization algorithms, genetic algorithm (GA) and particle swarm optimization (PSO), are introduced to VDA of modified Lorenz equations with on-off parameterizations, then two VDA schemes are proposed, that is, GA based VDA (GA-VDA) and PSO based VDA (PSO-VDA). After revealing the advantage of GA and PSO over conventional adjoint methods in the ability of global searching at the existence of cost functions discontinuity induced by on-off switches, sensitivities of GA-VDA and PSO-VDA to population size, observational noise, model error and observational density are detailedly analyzed. Its shown that, in the context of modified Lorenz equations, with proper population size, GA-VDA and PSO-VDA can effectively estimate the global optimal solution, while PSO-VDA consumes much less computational time than GA-VDA with the same population size, and requires a much lower population size with nearly the same results, both methods are not very sensitive to observation noise and model error, while PSO-VDA shows a better performance with observational noise than GA-VDA. It is encouraging that both methods are not sensitive to observational density, especially PSO-VDA, using which almost the same perfect assimilation results can be obtained with comparatively sparse observations.     

On the Application of a Genetic Algorithm to the Predictability Problems Involving "On-Off" Switches

The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on-off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm (GA) to the CNOP method, named GA-CNOP, to solve the predictability problems involving on-off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on-off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.