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湖南省主要内生成矿事件的构造格局控矿特征及动力机制
引用本文:俞颖,,黄风华,,刘永芬.湖南省主要内生成矿事件的构造格局控矿特征及动力机制[J].延边大学理工学报,2020,0(1):49-70.
作者姓名:俞颖    黄风华    刘永芬
作者单位:(1. 湖南省地质调查院,湖南 长沙 410116; 2. 东华理工大学 核资源与环境国家重点实验室,江西 南昌 330013)
摘    要:湖南省内生矿产资源丰富,内生成矿事件主要有加里东期(以志留纪为主)、印支晚期(晚三叠世)、燕山中晚期(晚侏罗世—早白垩世)等3期。以区域矿产资料为基础,结合大地构造、成岩成矿年龄、矿床成因机制等研究成果,对上述3期内生成矿事件的构造格局控矿特征和动力机制进行探讨。①受加里东运动自东南向西北扩展以及深部岩石圈结构差异控制,加里东期湖南省自东南往西北分为成矿特征有别的3个构造带。湘中—湘东南构造岩浆带(Ⅰ)发生后碰撞花岗质岩浆活动,于局部产生与岩浆活动相关的W、萤石等成矿作用; 雪峰构造带(Ⅱ)东部的雪峰冲断带(Ⅱ1)形成了以构造活化成因为主的金矿和锑金矿; 雪峰构造带(Ⅱ)西部的武陵低缓褶皱带(Ⅱ2)及湘西北构造抬升带(Ⅲ)内形成了与寒武纪同沉积断裂活动、加里东运动后的伸展活动以及相应的热液活动有关的汞铅锌矿。②印支晚期受深部岩石圈结构差异控制,湖南省自东南至西北分为3个构造带:湘中—湘东南构造岩浆带(Ⅰ)因后碰撞减压熔融而发生大规模花岗质岩浆活动,从而于其东南部形成钨锡铅锌多金属矿床,西北部形成锑金钨多金属矿床; 雪峰构造带(Ⅱ)可能无内生热液成矿作用; 湘西北褶皱带(Ⅲ)发育小型脉型铅锌矿。③燕山中晚期,湖南省自东南往西北分为3个构造带:湘中—湘东构造岩浆带(Ⅰ)受岩石圈拆沉、软流圈上隆、陆内碰撞后期增温减压、俯冲板块崩塌等深部构造作用控制而发生大规模花岗质岩浆活动,形成了大量的有色金属矿床和金矿床; 雪峰西部构造带(Ⅱ)成矿作用弱,局部存在Au、Hg成矿作用; 湘西北褶皱带(Ⅲ)发育少量低温热液充填型萤石矿和砷矿。

关 键 词:内生成矿  构造格局  动力机制  成矿年龄  加里东期  印支晚期  燕山中晚期  湖南

Speech emotion recognition based on feature dimension reduction and parameter optimization
YU Ying,' target="_blank" rel="external">,HUANG Fenghua,' target="_blank" rel="external">,LIU Yongfen.Speech emotion recognition based on feature dimension reduction and parameter optimization[J].Journal of Yanbian University (Natural Science),2020,0(1):49-70.
Authors:YU Ying  " target="_blank">' target="_blank" rel="external">  HUANG Fenghua  " target="_blank">' target="_blank" rel="external">  LIU Yongfen
Affiliation:(1. Hunan Institute of Geological Survey, Changsha 410116, Hunan, China; 2. State Key Laboratory of Nuclear Resourcesand Environment, East China University of Technology, Nanchang 330013, Jiangxi, China)
Abstract:The traditional BP neural network has been existing some burning questions in the process of speech emotion recognition, especially the high computational and local optimum trending. Against these shortcomings, we present a novel method of emotion recognition based on feature dimension reduction and parameter optimization. The recognition method is divided into three stages. In the first stage, it extracts the high-dimensional joint features of the speech emotion database. This is, in fact, aimingto reduce the complexity of the problem which is carried out by the fast principal component analysis(Fast_PAC)method. In the second stage, genetic algorithm is used to optimize the parameters of BP neural network to avoid the local optimum problem. Finally, we construct a speech emotion recognition classifier, and take the experiments on the CASIA Chinese corpus and Berlin German corpus for emotion recognition verification. The experiments show that the proposed method can effectively reduce the feature dimension of speech emotion comparing with other competitive methods, such as the traditional support vector machine(SVM)method and the traditional PCA combined with SVM model recognition method. Furthermore, it demonstrates the advantages of less computation and higher recognition accuracy.
Keywords:Fast_PAC  genetic algorithm  BP neural network  speech emotion recognition
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