中国中西部褶皱冲断带构造变形机制与结构模型     

贾承造  陈竹新  雷永良  王丽宁  任荣  苏楠  杨庚 《地学前缘》2022,29(6):156-174

基于复杂构造解析和实验模拟研究,揭示了中西部前陆褶皱冲断构造带主要表现为受侧向挤压形成的滑脱冲断构造变形过程和结构样式;明确了单层滑脱挤压冲断构造变形存在临界增生和非临界增生两种变形机制,发育脆性拆离型、塑性滑移型和黏性流动型3种作用类型,并受滑脱层强度、地层厚度、底部边界和外动力过程等4种主要因素影响。复杂冲断构造带基本上表现为受多层单滑脱作用控制形成的垂向叠置组合结构,本文提出了复杂滑脱冲断变形结构的可分解性以及受不同性质的滑脱层组合控制形成特征结构模式,并揭示了前陆冲断带前缘多滑脱构造变形结构中由浅层向深层逐渐发育的变形时序;建立了中西部再生前陆冲断带结构模型、构造单元以及基本构造类型;并基于前陆盆地多阶段构造演化过程以及晚期的隆升剥蚀-沉降沉积过程,提出了中西部两种类型冲断带的控油气作用及其勘探领域。  相似文献   

Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks     

Xin Wei  Lulu Zhang  Hao-Qing Yang  Limin Zhang  Yang-Ping Yao 《地学前缘(英文版)》2021,12(1):453-467

Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.  相似文献   

SAR image despeckling with a multilayer perceptron neural network     

Xiao Tang  Lei Zhang  Xiaoli Ding 《International Journal of Digital Earth》2019,12(3):354-374

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.  相似文献   

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District   总被引：2，自引：0，他引：2  

Jun Yang  Andong Guo  Yonghua Li  Yuqing Zhang  Xueming Li 《地理信息系统科学与遥感》2019,56(3):388-405

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.  相似文献   

制备TJ-1模拟月壤空心圆柱试样的新方法   总被引：1，自引：0，他引：1  

李立青  蒋明镜  苏佳兴  吴晓峰 《冰川冻土》2011,33(4):927-932

针对TJ-1模拟月壤颗粒级配跨度大、粒径〉2mm．的颗粒含量多,采用常规方法制备空心圆柱试样容易出现粗细颗粒分离、试样整体不均匀的特点,提出改良型制备方法．首先采用分层欠压法与冰冻法相结合制备出孔隙比e=1．0的实心圆柱试样,并对实心样的均匀性进行评价,得到分层制备相同孔隙比的TJ-1模拟月壤均匀试样时每层试样的高度;...  相似文献   

A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor     

Syam Sundar De  Goutami Chattopadhyay  Bijoy Bandyopadhyay  Suman Paul 《Comptes Rendus Geoscience》2011,343(10):664-676

The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.  相似文献   

多层次格网模型的近邻指数聚类生态区划算法与实验——以新疆北部地区区划为例     

袁烨城  周成虎  覃彪  欧阳 《地球信息科学学报》2011,13(1):1-11

在综合考虑气候、植被、地貌等凶素的基础上,提出一种基于多层次格网模型的最近邻指数-模糊聚类生态区域划分算法(Nearest Neighbor Index Fuzzy clustering,NNI-FIC).该算法采用"自下而上"的方式,首先,利用离散格网单元之间的严格相似性形成区划的核心分区;然后,通过最近邻指数统计分...  相似文献   

用多层感知器模型由吸收光谱反演浮游植物色素   总被引：1，自引：0，他引：1       下载免费PDF全文

周雯  曹文熙  王桂芬  梁少君  赵俊  孙兆华 《热带海洋学报》2010,29(2):46-51

浮游植物吸收光谱已逐渐成为高光谱水色遥感的可获取参量。文章采用了多层感知器模型, 由珠江口担杆群岛附近水体的浮游植物吸收光谱进行了色素浓度的反演, 感知器的输入量是浮游植物吸收光谱, 输出量分别对应叶绿素a、叶绿素b、叶绿素c、光保护类胡萝卜素和非光保护类胡萝卜素五大类主要色素的浓度。分析结果表明, 叶绿素a和叶绿素c估算结果的平均相对偏差比较低, 在测试数据集中两者的偏差分别为19.06%和15.90%; 光保护类胡萝卜素和非光保护类胡萝卜素的估算浓度的相对偏差比较高, 对于测试数据而言, 分别为37.62%和36.96%; 叶绿素b浓度在测试数据集中的估算相对偏差约为27.47%。五大类色素在测试数据集和训练数据集的估算偏差比较接近, 已训练好的多层感知器可用于担杆岛水体中色素信息的反演。同时, 此色素反演方法也为遥感监测水体浮游植物种群动态提供了重要的手段。  相似文献   

从受训神经-模糊模型中提取知识     

冯天瑾  于树松  程军娜 《中国海洋大学学报(自然科学版)》2003,33(1):107-114

本文对用于模式分类、函数逼近、参数估计的多层感知器 (MLPs)给出 1个清晰的关于内部行为的解释。作者以单隐层的 MLP为例 ,论述了关于 MLP的内部行为的半线性分析理论。对受训的MLP,将隐层单元的输出分别定义为网络输出的正、负“内部分量”;定义内部分量的连接权重集为给定问题的“内部判别模式”;建立了 MLP和模糊集相结合的新模型 ;分析了 MLP的结构为 N- 2 - 1和N- H- 1 ,给出权重初始化的方法 ;提出了 1种从受训神经 -模糊模型 (NFMs)中提取知识的全新的具有实用价值的方法。  相似文献   

倾角导向体控制的气烟囱识别技术及其在海拉尔盆地贝尔凹陷中的应用     

杨瑞召  李洋  庞海玲  赵争光  文小龙  邵建欣  孙玉凤  李聪聪  王少卿 《现代地质》2013,(1):223-230

在调研了国内外气烟囱研究成果与思路的基础上,通过全新的地震解决方案——OpendTect平台提供的基于属性多层感知器(MLP)的人工神经网络(ANN)的方法预测气烟囱的发生概率体,并利用倾角导向体对算法进行改进,提出倾角控制下的地震气烟囱识别技术,很好地补充并发展了模式识别技术在地震勘探领域的应用。通常该技术一方面可以解释运移通道和浅层气成藏的原因;另一方面,形成的气烟囱地震数据体还可以预测源岩的发育情况;此外,对于判别断层封闭性也非常适用。最后应用这一技术对海拉尔盆地贝尔凹陷一含油区块的运移通道和成藏规律进行研究,分析该研究区的断裂发育特征和运移通道类型,总结了油源、通道、储层和盖层的空间配置关系,并建立相应的成藏模式。  相似文献