关于发展我国地震科技信息工作的思考

地震科技信息的积累和应用是我国地震科学事业发展的基础，我国的地震信息工作在地震预报的探索和研究中以及地震工程应用中，均发挥了重要的作用，为进一步认识地震科技信息的重要性，发展这一事业，本文就地震科学研究与地震科技信息的内在联系，地震科技信息的运行机制及地震科技信息工作的发展提出了一些新的观点。     

新疆地表水资源对亚洲中部未来气候变化的响应

在未来的亚洲中中气候变冷过程中，从咸海干涸海底和卡拉博加戈尔湾等等内陆湖泊和地区吹起的盐粒，农田排出的农药残余物，尘埃微粒等－固态大陆型气溶胶具有主要的作用。在这一过程中，环流及该区域的生物系统对气候的变化可能起着相反的影响。     

流形上的空间密度聚类算法研究

研究流形上的聚类分析，针对基于密度的空间聚类引入了流形概念，提出1种基于流形的密度聚类算法，该方法将流形的概念与聚类相结合，可以适用于样本为复杂分布的聚类。文中通过实例证明此算法的有效性。     

不同空间权重定义下中国人口分布空间自相关特征分析

人口分布的研究对实现人口、环境、资源可持续管理具有深刻意义。相对于洛伦斯曲线、基尼系数等传统度量指标,运用空间自相关分析可以较好地表达人口分布的集聚现象,揭示人口格局的空间结构和空间相互作用。空间自相关测度的关键因素之一为空间权重矩阵。当前人口空间自相关特征测度多采用单一邻近关系定义空间权重矩阵,忽视了不同空间邻近关系对自相关特征分析结果的影响。本文根据距离阈值和邻接关系定义了八种空间权重,包括新定义的高铁两小时可达性空间权重。基于2010年《中国人口年鉴》统计资料,结合GeoDa软件测度了中国人口空间分布自相关特征并分析空间权重定义对自相关特征分析结果的影响。发现:(1)在不同空间邻近关系下,中国省域人口密度分布在全局自相关上均呈现空间正自相关,但其显著性水平有差异;(2)局部自相关分析结果Moran's I表现出明显的区域差异。     

大动态宽带干涉式雷电测向接收机新技术

提出了一种基于非线性盲辨识的自适应数字接收机技术。由于宽带雷电信号的时频特征未知或时变,在频域对宽带数字接收机输出信号中的谐波与互调分量进行识别和分选,并以其短时能量最小化作为其非线性行为模型参数的盲辨识准则,利用最速下降算法实现模型参数的自适应提取和更新,然后在线实时地对接收机输出信号进行非线性补偿。实验结果表明,该盲辨识数字接收技术可以将整机的无杂散失真动态范围（SFDR）提高近20dB,极有利于在强干扰存在时对微弱信号的接收与检测。     

晚型星HR5553,εEri和70 Oph的活动性,金属丰度和磁场的测定

对１９９５年１１月和１９９６年３、４月间观测获得的ＨＲ５５５３、７０Ｏｐｈ和εＥｒｉ给出了高分辨率,高信噪比的阶梯光栅光谱资料。确定了这３颗晚型活动星的大气参数,采用谱线细致分析方法求出了铁、锂、钙、钪、钛、钒、铬、锰、钴、镍、钇、锆和钡１３种金属元素的丰度。采用多重线统计方法测定了这３颗晚型活动星的磁场和磁场覆盖因子。     

寒武纪丝状蓝细菌生物膜主导和诱发的巨型鲕粒:以河南新安县石井剖面苗岭统徐庄组为例

多样化的产出环境和30×108年的分布历史表明,鲕粒成因一直是一个谜一样的沉积学难题,争论的关键问题是其究竟是有机(微生物)成因还是无机成因.来自于华北地台寒武系苗岭统徐庄组鲕粒滩相灰岩顶部的方解石巨鲕表现出同心状、放射—同心状和泥晶质的沉积组构,在鲕粒核心和鲕粒皮层中保存着精美的葛万菌(Girvanella)化石所主...     

Dynamic neuro-fuzzy local modeling system with a nonlinear feature extraction for the online adaptive warning system of river temperature affected by waste cooling water discharge

This paper proposes a dynamic modeling methodology based on a dynamic neuro-fuzzy local modeling system (DNFLMS) with a nonlinear feature extraction technique for an online dynamic modeling task. Prior to model building, a nonlinear feature extraction technique called the Gamma test (GT) is proposed to compute the lowest mean squared error (MSE) that can be achieved and the quantity of data required to obtain a reliable model. Two different DNFLMS modes are developed: (1) an online one-pass clustering and the extended Kalman filtering algorithm (mode 1); and (2) hybrid learning algorithm (mode 2) of extended Kalman filtering algorithm with a back-propagation algorithm trained to the estimated MSE and number of data points determined by a nonlinear feature extraction technique. The proposed modeling methodology is applied to develop an online dynamic prediction system of river temperature to waste cooling water discharge at 1?km downstream from a thermal power station from real-time to time ahead (2?h) sequentially at the new arrival of each item of river, hydrological, meteorological, power station operational data. It is demonstrated that the DNFLMS modes 1 and 2 shows a better prediction performance and less computation time required, compared to a well-known adaptive neural-fuzzy inference system (ANFIS) and a multi-layer perceptron (MLP) trained with the back propagation (BP) learning algorithm, due to local generalization approach and one-pass learning algorithm implemented in the DNFLMS. It is shown that the DNFLMS mode 1 is that it can be used for an online modeling task without a large amount of training set required by the off-line learning algorithm of MLP-BP and ANFIS. The integration of the DNFLMS mode 2 with a nonlinear feature extraction technique shows that it can improve model generalization capability and reduce model development time by eliminating iterative procedures of model construction using a stopping criterion in training and the quantity of required available data in training given by the GT.     

逆数据域表面多次波压制方法

基于波动方程压制表面多次波的反馈迭代方法,推导出压制表面多次波的逆数据域方法。该方法利用了一次波和表面多次波在逆数据域的可分离性质。含表面多次波的地震数据变换到逆数据域后,表面多次波的能量在逆数据域形成能量聚焦区域,可通过简单切除能量聚焦区域的信号实现表面多次波压制,避免了基于波动方程预测减去方法中的自适应相减过程,完全损伤不到一次波的能量。单道地震记录和合成炮数据模型试算表明：逆数据域多次被压制方法简单易行,可有效压制表面多次波,并保持一次波振幅不受损失。与常规的基于波动方程的表面多次被压制方法（SRME方法）对比表明,逆数据域方法能更好地应对有效波和多次波同相轴相交的情况。     

Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data

The accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factors and determining reasonable levels of residential subsidies. Previous studies focus mainly on housing price analysis at a macro scale, without fine‐scale study due to a lack of available data and effective models. By integrating a convolutional neural network for united mining (UMCNN) and random forest (RF), this study proposes an effective deep‐learning‐based framework for fusing multi‐source geospatial data, including high spatial resolution (HSR) remotely sensed imagery and several types of social media data, and maps urban housing prices at a very fine scale. With the collected housing price data from China's biggest online real estate market, we produced the spatial distribution of housing prices at a spatial resolution of 5 m in Shenzhen, China. By comparing with eight other multi‐source data mining techniques, the UMCNN obtained the highest housing price simulation accuracy (Pearson R = 0.922, OA = 85.82%). The results also demonstrated a complex spatial heterogeneity inside Shenzhen's housing price distribution. In future studies, we will work continuously on housing price policymaking and residential issues by including additional sources of spatial data.