Distribution Prediction of Middle-deep Lacustrine Source Rocks in Eocene Wenchang Formation in the Kaiping Sag,Pearl Mouth Basin     

XIONG Lianqiao  XIE Xiaojun  ZHAO Zhao  ZHANG Chunyu  WANG Long  LIAO Jihu  CAI Lulu  XU Wei 《《地质学报》英文版》2021,95(1):248-258

Oil and gas shows are rich in drilling wells in Kaiping sag,however,large oilfield was still not found in this area.For a long time,it is thought that source rocks were developed in the middle-deep lacustrine facies in the Eocene Wenchang Formation,while there is no source rocks that in middle-deep lacustrine facies have been found in well.Thickness of Wenchang Formation is big and reservoirs with good properties could be found in this formation.Distribution and scale of source rock are significant for further direction of petroleum exploration.Distribution characterization of middle-deep lacustrine facies is the base for source rock research.Based on the sedimentary background,fault activity rate,seismic response features,and seismic attributes were analyzed.No limited classification method and multi-attributes neural network deep learning method were used for predicting of source rock distribution in Wenchang Formation.It is found that during the deposition of lower Wenchang Formation,activity rate of main fault controlling the sub sag sedimentation was bigger than 100 m/Ma,which formed development background for middle-deep lacustrine facies.Compared with the seismic response of middle-deep lacustrine source rocks developed in Zhu I depression,those in Kaiping sag are characterized in low frequency and good continuity.Through RGB frequency decomposition,areas with low frequency are main distribution parts for middle-deep lacustrine facies.Dominant frequency,instantaneous frequency,and coherency attributes of seismic could be used in no limited classification method for further identification of middle-deep lacustrine facies.Based on the limitation of geology knowledge,multi-attributes of seismic were analyzed through neural network deep learning method.Distribution of middle-deep lacustrine facies in the fourth member of Wenchang Formation is oriented from west to east and is the largest.Square of the middle-deep lacustrine facies in that member is 154 km²and the volume is 50 km³.Achievements could be bases for hydrocarbon accumulation study and for exploration target optimization in Kaiping sag.  相似文献   

Late Quaternary chironomid community structure shaped by rate and magnitude of climate change     

Roseanna J. Mayfield  Peter G. Langdon  C. Patrick Doncaster  John A. Dearing  Rong Wang  Gaute Velle  Kimberley L. Davies  Stephen J. Brooks 《第四纪科学杂志》2021,36(3):360-376

Much is known about how climate change impacts ecosystem richness and turnover, but we have less understanding of its influence on ecosystem structures. Here, we use ecological metrics (beta diversity, compositional disorder and network skewness) to quantify the community structural responses of temperature-sensitive chironomids (Diptera: Chironomidae) during the Late Glacial (14 700–11 700 cal a bp ) and Holocene (11 700 cal a bp to present). Analyses demonstrate high turnover (beta diversity) of chironomid composition across both epochs; however, structural metrics stayed relatively intact. Compositional disorder and skewness show greatest structural change in the Younger Dryas, following the rapid, high-magnitude climate change at the Bølling–Allerød to Younger Dryas transition. There were fewer climate-related structural changes across the early to mid–late Holocene, where climate change was more gradual and lower in magnitude. The reduced impact on structural metrics could be due to greater functional resilience provided by the wider chironomid community, or to the replacement of same functional-type taxa in the network structure. These results provide insight into how future rapid climate change may alter chironomid communities and could suggest that while turnover may remain high under a rapidly warming climate, community structural dynamics retain some resilience.  相似文献   

水文监测网络运行状态综合评估系统设计与实现     

王菊  胡超  金惠英  梁妙元  李胜 《水文》2021,41(1):55-60

对水文监测网络的实时状态进行综合评估,从而掌握网络的整体性能是对水文监测网络进行管理的重要方面。针对当前水文监测网络综合评估缺少合理有效评价机制的现状,设计并开发水文监测网络实时状态综合评估系统,实现对水文网络中测站传感器、RTU、信道状态的采集、评估和综合呈现,提高对水文监测网络的分析水平。  相似文献   

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks     

Husam A.H.Al-Najjar  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(2):625-637

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献   

基于随机补片和DeepLabV3+的建筑物遥感图像变化检测     

王民水  孔祥明  陈学业  杨国东  王明常  张海明 《吉林大学学报(地球科学版)》2021,51(6):1932-1938

为有效解决传统遥感图像变化检测预处理复杂的问题,提出一种基于随机补片和DeepLabV3+的建筑物遥感图像变化检测方法。以ResNet50特征提取网络为基础,创建DeepLabV3+语义分割网络,并在图像和标签中创建大小为224像素×224像素的随机补片作为网络输入,训练建筑物提取网络;修改建筑物提取网络输入层为6通道,通过矩阵运算将两期遥感图像转换为一幅6通道非RGB图像,利用转换后的非RGB图像进行网络训练并验证变化检测精度。实验1利用ENVI5.3软件,采用马氏距离法进行变化检测;实验2采用改进的U-Net网络和随机补片,完成网络训练和精度验证;实验3使用实验2的训练数据和验证数据,采用随机补片和DeepLabV3+网络进行变化检测网络训练及精度验证。实验结果表明,该方法实验1、实验2、实验3建筑物变化检测平均交并比分别为24.43%、83.14%、89.90%,边界轮廓匹配分数分别为61.47%,80.24%、96.51%。  相似文献   

BP人工神经网络法在大同市日极大风速预报中的应用     

刘洁莉  刘冬辉  韩登云  徐鑫 《内蒙古气象》2019,(2):34-38

文章利用2010年1月1日—2013年12月31日逐日NCEP再分析资料(1°×1°)和大同地区地面常规观测资料,采用BP人工神经网络法建立大同市分站点、分季节日极大风速人工神经网络预报模型并且在对T639数值预报产品和EC细网格数值预报产品释用基础上建立了台站日极大风速的客观预报系统,对2015年9月1日—2016年7月31日进行了24h预报,试用结果显示,各季模式平均绝对误差在3.2~5.7m·s^-1之间,因此,该系统可以为预报员快速做出日极大风速的预报提供客观参考依据。  相似文献   

基于循环神经网络的单站能见度短临预报试验          下载免费PDF全文

李昕蓓  张苏平  衣立  韩美  潘宁 《山东气象》2019,39(2):76-83

由于能见度具有局地性和复杂的非线性变化特征,一直是精细化预报的难点。人工神经网络对复杂变化过程的模拟能力较高,为解决这一难题提供了可能性。本文采用循环神经网络,利用福州气象观测站地面观测数据,建立了福州单站能见度短临预报模型,并就预报能力进行了评估。随机检验结果表明,在1 h、3 h、6 h时效上,循环神经网络的预报与观测的变化趋势一致性较好;均方根误差比基于实况的预报分别减小15.75%、31.66%、41.26%,说明具备较好的预报能力;平均绝对值误差比传统BP神经网络分别减小12.90%、24.45%、 38.99%,表明循环神经网络对能见度预报具有优势,为能见度的精细化短临预报提供了新途径。  相似文献   

Measuring,mapping and quantifying the effects of trust and informal communication on transboundary collaboration in the Great Lakes fisheries policy network     

《Global Environmental Change》2019

Ecosystem-based management of fisheries and other transboundary natural resources require a number of organizations across jurisdictions to exchange knowledge, coordinate policy goals and engage in collaborative activities. Trust, as part of social capital, is considered a key mechanism facilitating the coordination of such inter-organizational policy networks. However, our understanding of multi-dimensional trust as a theoretical construct and an operational variable in environmental and natural resource management has remained largely untested. This paper presents an empirical assessment of trust and communication measures applied to the North American Great Lakes fisheries policy network. Using a scale-based method developed for this purpose, we quantify the prevalence of different dimensions of trust and in/formal communication in the network and their differentiated impacts on decision-making and goal consensus. Our analysis reveals that calculation-based ‘rational trust’ is important for aligning mutual goals, but relationship-based ‘affinitive trust’ is most significant for influencing decision-making. Informal communication was also found to be a strong predictor of how effectively formal communication will influence decision-making, confirming the “priming” role of informal interactions in formal inter-agency dealings. The results also show the buffering and interactive functions of these components in strengthening institutional resilience, with procedural trust undergirding the system to compensate for a lack of well-developed relationships. Overall, this study provides evidence to suggest that informal communication and multi-dimensional trust constitute a crucial element for improving collaboration and reducing conflict in the networked governance of transboundary natural resource systems.  相似文献   

基于小波长短期记忆网络的风电功率超短期概率预测     

王朋  孙永辉  翟苏巍  候栋宸  王森 《南京气象学院学报》2019,11(4):460-466

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆（LSTM）网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.  相似文献   

循环神经网络在雷达临近预报中的应用          下载免费PDF全文

韩丰  龙明盛  李月安  薛峰  王建民 《应用气象学报》2019,30(1):61-69

该文将循环神经网络（recurrent neural network,RNN）应用于雷达临近预报。使用预测循环神经网络（predictive RNN）架构,利用雷达历史组合反射率因子建模,给出雷达组合反射率因子未来1 h的预报结果。预测循环神经网络的核心是在长短时记忆单元（long short-term memory,LSTM）中增加时空记忆模块,能够提取雷达回波不同尺度的空间特征,配合循环神经网络架构,可以有效解决反射率因子预测问题。北京大兴雷达和广州雷达长时间序列的独立检验结果和2个强对流天气个例检验结果表明:该方法和传统的基于交叉相关法的1 h雷达外推临近预报相比,在20 dBZ和30 dBZ检验项目内,临界成功指数（CSI）可以提升0.15~0.30,命中率（POD）提高0.15~0.25,虚警率（FAR）降低0.15~0.20,该方法对反射率因子强度变化有一定预报能力。  相似文献