层状云系人工增雨潜力评估研究

层状云系是中国人工增雨催化作业的主要对象.云系人工增雨潜力的大小是判断是否作业的最为重要的科学依据.但是,目前中国还没有一套比较完整的综合判断云系人工增雨潜力的评估方法.自然降水形成过程是个复杂的科学问题,因此判断人工增雨潜力也是一个非常复杂的科学问题.作者利用中尺度MM5数值模式模拟的层状云系研究与人工增雨潜力有关的要素,从理论上研究了不同要素与降水的关系.在分析云体的垂直结构、降水机制、水汽厚度、冰面过饱和水汽量、云水厚度、过冷水含量、冰晶浓度、降水效率(凝结水降水效率和凝华水降水效率)等潜力要素基础上,获得了新的潜力要素,例如 "催化-供给" 云结构、降水机制、冰面过饱和水汽量,并提出了定性综合判断云系人工增雨潜力思路.     

地震资料非高斯信息分析提取方法及油气预测

从讨论实际地震记录高阶累积量分析入手,研究利用高阶累积量计算高阶谱的方法.在分析高阶累积量及高阶谱提取分离具有非高斯分布信号时,详细讨论了产生非高斯分布的地震记录的特征规律.阐明了用地震资料的低频高阶谱预测砂体含油气性的机理.从理论模型和实际资料论证了研究方法的正确性.最后利用该方法计算的高阶谱结果和实际产油气井资料进行符合率验证分析.在此基础上,利用该方法进行有利含油气层预测.     

Imaging and image processing in porous media research

Three-dimensional imaging and image processing has become an important part for investigations of fluid distribution and flow in porous media. We describe two methods of computed tomography with different characteristics, namely X-ray- and neutron-based. We give an overview of image processing sequences and their methods. We investigated image enhancement with a focus on filters using partial differential equations, classification and structure identification that we used to prepare our images for quantitative evaluations. These methods are demonstrated on a partially saturated sand sample. Finally, we show an application with soil aggregates where investigations using synchrotron X-rays and thermal neutrons have led to new insights and refined fluid distribution and flow models.     

基于脊波域的低信噪比地震资料处理技术

在小波分析的基础上发展起来的脊波变换方法,能更好地处理含线状变化特征的信号。针对地震资料中的同相轴信息,尝试利用脊波变换方法对其进行处理,提高剖面信噪比,突出同相轴信息。在对某工区实际地震资料的处理中发现,处理后的地震剖面同相轴品质及连续性有了明显改善,信噪比增强,分辨率相应提高,体现了该方法相对常规小波分析方法的优越性。      

甘肃省冬, 春季人工增雨雪作业条件分析

从冬、春季人工增雨雪的工作需要出发,分析了与冬、春季降水关系密切的部分气象要素多年变化特征,统计了有利于甘肃冬、春季人工增雨雪的4种环流型、云系与降水的关系,并根据它们与冬、春季降水的关系,分析了有利于人工增雨雪作业的天气气候背景。     

一种基于小波变换的SAR海洋图像数据增强系统

提出了一种基于小波变换的SAR海洋图像数据增强系统,并进行了试验.本系统包括小波变换、Speckle及Pepper-Salt噪声抑制、船等目标提取、内波提取、目标数统计和图像图形显示放大等功能.该图像增强系统可以对含有常见的高斯噪声及Speckle和Pepper-Salt非高斯噪声的污染图像数据进行增强处理,可对SAR等微波成像传感器数据、光学成像传感器数据进行增强处理,具有广泛的应用前景     

侧扫声纳图像增强技术

随着海洋探测技术的飞速发展,侧扫声纳的图像质量和定位精度迅速提高,其应用日益广泛和深入。在声纳图像判读分析时,常常需要进行辐射改正和均衡增强。根据海底地貌声纳图像的特点,归纳了几种有效、适用的图像增强方法,并建立了声纳图像分析软件。     

山东省海洋渔业资源增殖放流及其渔业效益

以统计调查数据及研究文献为依据,对山东省海洋渔业资源增殖放流现状及其渔业效益进行了分析,指出山东省海洋渔业资源增殖放流成效显著,但亟需加强基础研究,特别是探寻放流量和产量在外界条件下波动的耦合关系,以及加强放流前期的基础调查、放流过程的规范化研究和放流效果的定量评估等,以进一步提高增殖放流效益,增强放流管理科学性。     

海洋微塑料作为生物载体的生态效应

随着海洋塑料垃圾的不断增多, 海洋微塑料作为一种新型的海洋污染物逐渐受到重视。目前关于微塑料来源分布及分析方法的认识较为普遍, 大部分研究注重于海洋生物误食微塑料、微塑料吸附及释放有毒物质的环境及生态效应, 而微塑料作为微生物、附生动植物的载体作用仍有待研究。文章综述了微塑料作为生物载体的三个作用: 1) 聚集作用。微塑料表面易覆盖生物膜形成微型生物群落, 为基因水平移动提供场所, 可能引发致病基因、抗生素抗性基因(antibiotic resistance genes, 简称ARGs)的转化、转导。2) 扩散作用。来源于污水的微塑料上可能附着生长着有害藻种、致病菌耐药菌或其他生物, 随水流移动到适宜区域, 可能引发赤潮或导致致病菌及耐药菌的传播扩散, 形成生物入侵。3) 首次提出微塑料的“捕食增强作用”概念, 即有机物、营养盐、生物群落聚集在微塑料颗粒上可提高捕食者的捕食效率, 从而促进海洋动物摄食微塑料, 进而加剧微塑料颗粒对海洋生物的毒理病理作用。文章着重阐述了“微塑料+生物”的生态效应。     

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.