佛山市干日、雨日持续日数概率的预报试验

本文利用静态马尔可夫过程和一定时段内随机变量极值的概率分布,给出了干日和雨日持续回数概率的预报模型,并对佛山市进行了预报试验。     

Significance of injection modes and heterogeneity on spatial and temporal dispersion of advecting particles in two-dimensional discrete fracture networks

We investigate the impact of injection mode (flux and resident injection) and heterogeneity in hydraulic properties on dispersion of advecting particles in two-dimensional discrete fracture network models, using a Monte Carlo method. We find that the injection mode has a significant effect on dispersion: The resident injection mode exhibits anomalous features of transport whereas the flux injection mode tends to Gaussian transport; this observation is easily understood by considering phase diagrams where a limited number of particles entering low velocity fractures greatly increase macrodispersion. In spite of a sizeable portion of negative longitudinal velocities, it is shown that multiple crossings are negligible when quantifying longitudinal macrodispersion. A simple probabilistic expression of particle mass balance is shown to predict well the spatial distribution of advecting particles.     

Seismic hazard estimation based on the distributed seismicity in northern China

In this paper, we have proposed an alternative seismic hazard modeling by using distributed seismicites. The distributed seismicity model does not need delineation of seismic source zones, and simplify the methodology of probabilistic seismic hazard analysis. Based on the devastating earthquake catalogue, we established three seismi- city model, derived the distribution of a-value in northern China by using Gaussian smoothing function, and cal-culated peak ground acceleration distributions for this area with 2%, 5% and 10% probability of exceedance in a 50-year period by using three attenuation models, respectively. In general, the peak ground motion distribution patterns are consistent with current seismic hazard map of China, but in some specific seismic zones which in-clude Shanxi Province and Shijiazhuang areas, our results indicated a little bit higher peak ground motions and zonation characters which are in agreement with seismicity distribution patterns in these areas. The hazard curves have been developed for Beijing, Tianjin, Taiyuan, Tangshan, and Ji’nan, the metropolitan cities in the northern China. The results showed that Tangshan, Taiyuan, Beijing has a higher seismic hazard than that of other cities mentioned above.     

基于改进的交叉极化比率算法的南极冻融探测

针对交叉极化比率（cross-polarized gradient ratio,XPGR）算法在冰盖冻融探测中存在的问题,提出了不依赖于实地观测数据的、自动阈值分割的、改进的XPGR微波辐射计南极冰盖冻融探测方法,即运用广义高斯模型对XPGR的长时间序列数据做直方图统计,计算出干湿雪划分的最优阈值,从而得到南极冰盖冻融分布图。试验结果表明：改进前后的结果虽然相差不多,但改进后的方法由于不依赖于实地观测数据,不仅提高了冰盖冻融探测的计算效率、实用性和可操作性,而且还在一定程度上提高了冰盖冻融探测的精度。     

基于深度学习的循环水养殖鳗鲡(Anguilla)行为状态识别的研究

对鱼类的行为进行智能监测,精准地量化与识别其健康状态,已成为研究热点。为实现养殖鳗鲡行为状态精准识别,提出一种基于DenseNet双流卷积神经网络的鳗鲡行为状态检测方法。利用混合高斯背景模型进行前景提取构建数据集,针对传统卷积神经网络对于时间动态信息提取能力有限的问题,搭建关联空间特征与时间特征的双流网络结构(Two-stream),并使用DenseNet-121网络替换原网络,对比VGGNet、ResNet等网络,通过密集连接实现特征重用,在搭建更深的网络结构基础上加强了运动特征传递并减少了参数量,更好地提取具有代表性的行为特征。传统双流网络在两端的softmax层仅作简单的决策层平均融合,无法更深程度关联时空高级特征,提出在网络卷积层提取空间特征与时间特征后,加上一层卷积层将时空特征进行卷积融合以提升模型识别精度。实验结果表明:文中提出的基于DenseNet双流卷积神经网络对6种鳗鲡行为状态检测方法准确率达到96.8%,相较于单通道的空间流与时间流网络,准确率分别提升了10.1%和9.5%;相较于以VGGNet、ResNet搭建的双流网络,准确率分别提升了12.4%和4.2%;与决...     

Fracture detection by Gaussian beam imaging of seismic data and image spectrum analysis

Localization of fractured areas is of primary interest in the study of oil and gas geology in carbonate environments. Hydrocarbon reservoirs in these environments are embedded within an impenetrable rock matrix but possess a rich system of various microheterogeneities, i.e., cavities, cracks, and fractures. Cavities accumulate oil, but its flow is governed by a system of fractures. A distinctive feature of wave propagation in such media is the excitation of the scattered/diffracted waves by the microheterogeneities. This scattering could be a reliable attribute for characterization of the fine structure of reservoirs, but it has extremely low energy and any standard data processing renders them practically invisible in comparison with images produced by specular reflections. Therefore, any attempts to use these waves for image congestion of microheterogeneities should first have a preliminary separation of the scattering and specular reflections. In this paper, the approach to performing this separation is based on the asymmetric summation. It is implemented by double focusing of Gaussian beams. To do this, the special weights are computed by propagating Gaussian beams from the target area towards the acquisition system separately for sources and receivers. The different mutual positioning of beams in each pair introduces a variety of selective images that are destined to represent some selected singular primitives of the target objects such as fractures, cavities, and edges. In this way, one can construct various wave images of a target reservoir, particularly in scattered/diffracted waves. Additional removal of remnants of specular reflections is done by means of spectral analysis of the scattered/diffracted waves' images to recognize and cancel extended lineaments. Numerical experiments with Sigsbee 2A synthetic seismic data and some typical structures of the Yurubcheno‐Tokhomskoye oil field in East Siberia are presented and discussed.     

多波多分量高斯束叠前深度偏移

本文对基于弹性波动理论的多波多分量高斯束偏移进行了完整且详细的分析和公式推导,实现了3D空间多分量(矢量)波场的直接成像.由于当前多数基于弹性波动方程的偏移方法只是假设应力边界条件为自由地表边界条件,这种假设不符合垂直地震剖面(VSP)和海底电缆(OBC)等地震数据.为此本文详细分析了实际应用中常见的三种弹性各向同性介质模型的应力边界条件:自由空间、海底和自由地表模型.在上行传播假设情况下,获得了应力边界条件与位移边界条件的关系式.在此基础上,准确推导了3D多波多分量高斯束波场延拓和偏移成像公式,并在偏移过程中实现了完整的多波型自动分离.由于常规的互相关成像条件不适用于矢量波场成像,本文引用了散度/旋度互相关成像条件.通过约定PS转换波的正向旋转方向解决了3D空间PS成像极性翻转问题.利用2D和3D模型数据偏移成像验证了我们所提出的多波多分量高斯束偏移方法的可行性.     

基于人工神经网络研究海水中叶绿素浓度的垂直分布

海水中叶绿素浓度的垂直分布可以用高斯函数近似拟合。目前卫星传感器只能测到透光层深度 2 2 %水体中的叶绿素浓度信息。为了寻找其高斯函数的 4个参数 (Bo,h,σ和 Z)与卫星测得的海表面温度、表面叶绿素浓度之间的映射关系 ,本文使用 6个输入的 3层 BP网络模拟这种复杂的映射关系。经过 30 0 0个周期的训练 ,网络的计算结果和测试数据的相关系数分别为 Bo(0 .4 76 1) ,h(0 .84 6 7) ,σ(0 .75 40 ) ,Z(0 .8988) ,表明了神经网络方法在确定叶绿素浓度垂直分布中的有效性。     

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