Soil water-salt dynamics state and associated sensitivity factors in an irrigation district of the loess area: a case study in the Luohui Canal Irrigation District,China

Soil salinisation determines the distribution pattern of crop processes in irrigation districts. The research presented here was conducted in the Luohui Canal Irrigation District, which is located in the loess area of Shaanxi Province, China. A back-propagation artificial neural network (BPANN) for the soil water-salt state was established to predict soil salinity and alkalinity. The degree of influence of numerous factors on the dynamics was quantitatively determined using the default factor testing method and verified with grey relational analysis. The results show that the BPANN prediction accuracy is very high, and it can efficiently depict the comprehensive relationships between the influential factors and dynamic states. The influence of soil moisture, evaporation, groundwater salt and groundwater depth on the dynamics is significant in the region. The current irrigation method, used for many years, cannot meet the water necessities for the vegetation, causing the groundwater levels to decline and a lowering of the soil moisture zone, leading to the occurrence of serious soil salinisation. Under the action of evaporation, more salt accumulates in the upper part of the soil, resulting in extensive soil salinisation. The higher the groundwater salt content, the more salt is carried by rising capillary water, and the more the soil is salinised. If groundwater depth was to exceed the critical water table, then groundwater and salt would move to the soil surface by moisture evaporation, and salt would build up on the soil surface in this irrigation district. The interactions between each factor forms a complex coupling relationship state.     

北极黑碳气溶胶研究现状和展望

在过去的100年中,北极温度升高的速率几乎是全球平均速率的两倍,尽管人为长生命期的温室气体(GHGs)在气候系统辐射强迫中占主导地位,黑碳气溶胶和其它短生命期的污染物却能很好地解释北极气候增暖较快的原因。因此,减少人为黑碳气溶胶排放将成为缓解北极迅速变化的重要策略。介绍北极黑碳气溶胶研究现状,讨论它在北极变暖中扮演的角色,分析当前北极黑碳气溶胶研究中存在的问题,展望深入开展黑碳气溶胶观测研究的必要性和紧迫性。     

对筒柱藻属33株总脂含量及脂肪酸组成的比较研究

在温度为２５±１℃,盐度为２８的条件下,用Ｆ／２培养基对青岛海洋大学微藻种质库保存的３３株筒柱藻进行培养。在指数生长期末期进行收获。测定了３３株筒柱藻的脂肪酸组成,总脂含量,收获时的细胞密度,生物量。其主要脂肪酸为１４∶０,１６∶０,１６∶１（ｎ－７）和２０∶５（ｎ－３）,其中Ｂ２００的２０∶５（ｎ－３）含量最高,占总脂肪酸的１５．９％。     

陆相沉积盆地不可忽视的工作方法--成因相与作用相分析

成因相与作用相分析是相分析不可分割部分。这种分析不但能够确立盆地沉积相序列,而且把盆内与盆外、构造与沉积、沉积动态与沉积过程,时间与空间紧密地结合起来,达到深刻认识沉积盆地演化发展的目的,是陆相盆地不可缺少的工作方法。     

Study on fish community structure in the Zhujiang Estuary and its adjacent waters

I~IOXMoreattentionhasbenpaidtothefishcornlnwhtiesintheestuaryandcoastalwaters,andanemphasishashogivenontheuseofvariousdiversityindicesandmultivariateanalysismethanethat~onthaisindicesinthestudyofthecharacteristicsOffishcornmwhties(Zhuetal.,1994;Zhuet...     

Envelope Functions of Time Histories in Seismic Safety Evaluation and Scenario Earthquakes

It has been a period of time since the concept of scenario earthquake was proposed, but this concept has rarely been used in seismic safety evaluation in China since then. Meanwhile, because of the uncertainties of magnitudes-distances pairs, there is large arbitrariness while determining the envelope function of time histories in seismic hazard analysis. In this paper, we describe a method to control the envelope functions of the time histories by introducing the most-likely combinations of magnitude and distance of the scenario earthquakes based on a probabilistic method, revise the software of the ellipse model for seismic hazard analysis, and give a computation example.     

Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes

Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.     

吉林松原M≥5.0震群前地磁谐波振幅比异常特征

以东北地区(40°—50°N,120°—130°E)为研究区域,利用黑龙江、吉林和辽宁地区地磁观测数据,进行谐波振幅比计算,对2013年10月31日至11月23日吉林松原接连发生的5个M≥5.0震群前该研究区地下电性结构进行分析,发现:(1)在该区域内显著地震发生前,望奎、通河、三岗、通化和铁岭地震台均出现下降—转折—恢复的异常变化特征,持续时间1—4.5年,变化幅度为0.04—0.07,而通河台Y_(ZH_y)项的不同周期间存在不同步现象,且异常持续2年以上;(2)通河台分钟值谐波振幅比具有由深至浅的周期迁移特征,三岗台秒值数据结果 Y_(ZH_y)向具有由浅入深的迁移特征;(3)2014年3月至2016年4月通河台水平方向的EW向数据不同深度介质电性出现差异性异常,可能预示着该台周边具有发生中强地震的危险,应重点关注该异常变化。     

江苏省数字强震动台网运行与维护

介绍江苏省数字强震动台网布局、强震动仪器观测环境及技术系统组成,并阐述强震动台网运行以及运行设备存在的常见故障和解决措施。江苏省数字强震动台网布局合理、性能稳定、动态范围大,地震记录信息完整可靠,丰富并充实了江苏省强震动数据库,为工程结构的抗震设计及地震工程学研究提供了宝贵的基础数据。     

基于单片机和GPS信号的校频系统

介绍一种基于INTEL 80C196KC单片机的校频系统,包括系统的设计原理、硬件组成和软件实现。该系统通过利用GPS接收机得到的1PPS秒脉冲信号,实现对晶体振荡器频率的校准,从而获得一个短期及长期稳定度都比较优良的时间频率标准。该系统采用了量化时延原理进行短时间间隔比对,对测量数据进行卡尔曼滤波处理。