声波对气溶胶和云雾粒子聚并影响研究进展

声波对气溶胶和云雾粒子聚并的作用和影响是当今云雾物理和人工影响天气领域研究的前沿科学问题。已有研究表明：声聚并机制主要包括同向团聚机制、流体力学机制（包括共辐射压效应、共散射效应和声波尾流效应）和声致湍流机制等;气溶胶粒子在声场中的聚并现象是在多种声聚并机制共同作用下出现的;低频强声波在声聚并机制作用下会增加云雾滴粒子之间的相对运动,促进粒子间的碰并过程,对云雾滴生长和降水过程有显著的影响。但由于声波聚并过程的复杂性、实验条件的多样性和理论的局限性,使声聚并效率最高的最佳实验条件和参数配置依然具有很大的不确定性,需要进行大量实验与数值模拟的综合研究。今后应加强声聚并对云雾滴作用的云室和数值模拟研究,并开展声波对云雾过程和降水影响的野外综合观测试验评估,这对发展人工影响天气（如人工消雾、增雨等）新技术有重要的科学意义。     

层状云人工增雨机制、条件和方法的探讨

该文提出了新的层状云人工增雨机制，指出人工冰晶除通过贝吉隆过程使过冷云水转化为降水外，还使一部分冰面过饱和水汽转化为降水，凝华潜热的释放导致空气增温和局部升速加大，促进云降水的发展。与此相应提出了适合人工增雨的云层条件、监测识别方法以及最佳催化部位、药剂、剂量等。     

火箭人工增雨作业的个例分析

2005年6月27日甘肃永昌实施了火箭人工增雨作业,XDR数字化天气雷达对这次增雨过程进行了跟踪探测。通过分析这次人工增雨前后天气学背景和雷达回波演变情况,证明在有利的天气形势下,通过火箭人工增雨作业后,雷达回波强度增强、回波范围和地面降水频率增大,作业后24 h地面雨量的分布情况也佐证了这一结果。     

基于信号整体与局部特征的地震数据自动处理方法研究

提出一种基于信号整体与局部特征的地震数据自动处理新方法, 该方法不同于以往仅采用包络线互相关来直接检测事件. 新方法依然按照检测、 识别、 关联和定位等4个步骤进行处理, 但在进行单个震相信号检测的同时, 也检测信号波列并利用其包络线特征来识别和关联震相. 文中详细阐述了数据处理过程中如何定义一个波列及抽取和应用其特征. 相关的数据处理技术目前已成功应用于区域台网的日常数据处理分析中. 作为例子, 给出了对新疆区域台网连续16天数据进行测试处理的结果. 实际应用结果表明, 这种新方法可以大幅度降低自动处理结果的误检、 漏检率, 在实际应用中具有很好的前景.     

前期西太平洋暖池热含量异常影响长江中下游夏季降水的可能途经

利用日本气象厅历史海温资料、NCEP/NCAR再分析资料、海表温度和降水资料,研究了1951-2010年前期西太平洋暖池（简称暖池）热含量异常与长江中下游夏季降水的关系,及其可能影响途径.结果表明,前期暖池热含量与长江中下游夏季降水存在超前2个季节的显著负相关关系,前期11-1月（即上年11月-当年1月,下同）暖池关键区（166.5°E-173.5°W,7.5°S-3.5°N）0~200 m热含量的偏低（高）对长江中下游夏季降水偏多（少）的预测有重要指示意义.前期暖池热含量异常的持续存在,及其外强迫作用激发的具有一定斜压性结构的夏季东亚-太平洋型遥相关（EAP）,可能是影响长江中下游夏季降水的主要原因.暖池热含量在前期11-1月异常偏低导致其西北侧菲律宾异常反气旋形成并维持,夏季菲律宾异常反气旋向西北方向扩展加强,东亚沿岸EAP波列形成,使得长江中下游及其以东的西北太平洋副热带地区受异常气旋控制,且长江中下游地区为北方冷空气与南方暖湿气流的交汇区.同时,对流层高层东亚沿岸亦存在位置较中低层向西北偏移的EAP波列,长江中下游及其以南地区为异常偏强高压,高层辐散与中低层辐合相配合,有利于长江中下游地区对流发展和降水增多;反之亦然.     

伽玛能谱探管设计

N761四道γ能谱测井探管是用于测量总计数率及铀、钍、钾放射性元素含量的测井仪器,配接在核工业北京地质研究院开发的HD-4002测井系统中,本探管采用双CPU结构,提高了系统的集成度,主CPU进行测量和自动稳谱,辅CPU对来自主CPU的测量数据进行并/串转换和码形变换,经铠装电缆传输到地面系统中,通过采用改进的自动稳谱技术,探管能够在较宽的环境温度范围(0℃～50℃)内自稳,各参数的记数偏差在8%以下,测井探管工作稳定,本探管经野外试验考核达到设计标准.     

复小波包域的微震信号分析提取方法

提出了一种在复小波包域分析提取微震信号的新方法。此方法先采用复小波包分解同时获得信号的幅值信息和相位信息,再采用一种称为“比值加权”的复小波包重构法,重构出信噪比大大增强的微震信号。实际应用表明这种新方法在提取微震信号,提高信噪比方面效果显著。     

On the predictive skill of morphodynamic models for onshore sandbar migration

A simplified process-based approach for evaluating onshore sandbar migration is presented in this study. This approach takes wave-averaged parameters as input and computes sediment transport processes using a reconstructed intra-wave orbital velocity signal. The proposed method guarantees good predictive skills for multiple onshore sandbar migration cases without an ad hoc calibration for each scenario. The study differs from previous onshore sandbar migration models, where calibration and testing are performed on just one or few events, and model assessment for uncalibrated realizations is rarely carried out. The present version of the model only addresses onshore sandbar migrations. For the onshore migration cases analyzed, the model has better predictive skills than state-of-the-art morphodynamic models (XBeach and Unibest-TC).     

核电站用机电设备抗震性能试验鉴定若干问题

本文了核电站用机电设备抗震性能试验鉴定若干问题,其中包括“激励输入地选取;利用给定场地响应谱确定激励信号过程;     

Common-offset domain reflection–diffraction separation with deep learning

Separation of diffracted from reflected events in seismic data is still challenging due to the relatively low amplitude of the diffracted wavefield compared to the reflected wavefield as well as the overlap in the kinematics of reflection and diffraction events. A workflow based on deep learning can be a simple and fast alternative, but using training data made by physics-based modelling is expensive and lacks diversity in terms of noise, amplitude, frequency content and wavelet. This results in poor generalization beyond the training data without retraining and transfer learning. In this paper, we demonstrate successful applications of reflection–diffraction separation using a conventional U-net architecture. The novelty of our approach is that we do not use synthetic data created from physics-based modelling, but instead use only synthetic data built from basic geometric shapes. Our domain of application is the pre-migration common-offset domain where reflected events resemble local geology and the diffracted wavefield consists of downward convex hyperbolic diffraction patterns. Both patterns were randomly perturbed in many ways while maintaining their intrinsic features. This approach is inspired by the common practice of data augmentation in deep learning for machine vision applications. Since many of the standard data augmentation techniques lack a geophysical motivation, we have instead perturbed our synthetic training data in ways to make more sense from a signal processing perspective or given our ‘domain knowledge’ of the problem at hand. We did not have to retrain the network to show good results on the field data set. The large variety and diversity in examples enabled to trained neural networks to show encouraging results on synthetic and field data sets that were not used in training.