Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979, the multi-level mapping model of neural network BP type was applied to calculate the system’s fractual dimension D0 = 2.8, leading to a three-level model of this type with i × j = 3 × 2, k = 1, and the 1980 monthly mean temperture prediction on a long-term basis were pre-pared by steadily modifying the weighting coefficient, making for the correlation coefficient of 97% with the measurements. Furthermore, the weighting parameter was modified for each month of 1980 by means of observations, therefore constructing monthly mean temperature forecasts from January to December of the year, reaching the correlation of 99.9% with the measurements. Likewise, the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlation of 98% and the month-to month forecasts of 99.4%.     

用径向基函数神经网络方法预报太阳黑子数平滑月均值

简单介绍了径向基函数神经网络方法的原理和应用,发展了用径向基函数（RBF）对平滑月平均黑子数进行预报的方法. 用不同的数据序列对网络进行训练,对未来8个月的平滑月平均黑子数进行预报. 用该方法对第23周开始后的平滑月平均黑子数进行逐月预报,并与实测值进行比较,结果表明随着预报实效的延长预报误差被逐渐放大,该方法可以较准确地做出未来４个月的预报,绝对误差可以控制在20以内,标准差为4.8,相对误差控制在38％以内,大部分相对误差不超过15%（占总预报数的89％）,具有较好的应用价值. 用于网络训练的样本数量对预报结果会产生一定的影响.     

Prediction of Elastic Modulus of Jointed Rock Mass Using Artificial Neural Networks

Two artificial neural network models for the prediction of elastic modulus of jointed rock mass from the elastic modulus of corresponding intact rock and joint parameters have been demonstrated in this paper. The data collected from uniaxial and triaxial compression tests on different rocks with different joint configurations and different confining pressure conditions, reported in the literature are used as input for training the networks. Important joint properties like joint frequency, joint inclination and roughness of joints are considered separately for making the network more versatile. Two different techniques of artificial neural networks namely feed forward back propagation (FFBP) and radial basis function (RBF) are used to predict the elastic modulus ratio.     

深度学习在地质学上的应用

自Hinton等使用基于卷积神经网络的深度学习模型赢得Image Net分类比赛以来,深度学习的研究席卷了各个行业。通过介绍深度学习的历史,探索国内地质行业中深度学习模型的使用情况,并介绍深度学习的基础概念(如神经元、神经网络、监督学习和无监督学习等)以及深度学习基础模型中的2个重要网络:深度信念网络(DBN)和卷积神经网络(CNN)。在此基础上,类比深度学习在医学等相关领域的应用,提出了深度学习在地质上的几点应用:利用深度学习在计算机视觉上表现出的强大能力,可以对遥感图像进行聚类、对岩石样品图像进行分类、对岩石薄片数据进行描述;利用深度学习对原始数据表现出的强大识别能力,处理地质异常数据,从而确定成矿靶区的可能位置;利用深度学习的特点,对地震前的声信号数据进行处理,从而判断出地震发生前的剩余时间。     

城市土地利用演变信息的数据挖掘——以上海市为例

城市土地利用变化 ,具有非线性特征 ,一般的数据挖掘方法基本上失效。本文研究了利用马尔可夫链和神经网络两种方法 ,基于地理信息系统、遥感图像、电子地图 ,预测了上海市中心城区、 2 0 0 2年和 2 0 0 5年的土地利用总量和土地利用类型结构的变化 ,从而研究了城市土地利用状况演变预测的地学数据挖掘技术。     

基于循环神经网络的欧亚中高纬夏季极端高温年代际预测模型研究

近几十年来频繁发生的极端高温事件严重威胁着自然生态系统、社会经济发展和人类生命安全。针对生态环境脆弱的欧亚中高纬地区,首先评估了当前主流动力模式(CMIP6 DCPP)对于该地区夏季极端高温的年代际预测水平,并构建了基于循环神经网络(Recurrent Neural Networks,RNN)的年代际预测模型。多模式集合平均(Multi-Model Ensemble,MME)的评估结果显示,得益于大样本和初始化的贡献,当前动力模式对于60°N以南区域(South Eurasia,SEA)展现了预测技巧,准确预测出了其线性增长趋势和1968—2008年间主要的年代际变率,然而模式对于60°N以北区域(North Eurasia,NEA)极端高温的年代际变率几乎没有任何预测技巧,仅预测出比观测低的线性增长趋势。基于86个初始场的动力模式大样本预测结果,RNN将2008—2020年间NEA和SEA极端高温的年代际变率预测技巧显著提高,距平相关系数技巧从MME中的-0.61和-0.03,提升至0.86和0.83,均方差技巧评分从MME中的-1.10和-0.94,提升至0.37和0.52。RNN的实时预测结果表明,在2021—2026年,SEA区域的极端高温将持续增加,2026年很可能发生突破历史极值的极端高温事件,NEA区域在2022年异常偏低,而后将呈现波动上升。     

深度学习农作物分类的弱样本适用性

基于大数据驱动的深度学习挖掘图像数据的规律和层次已成为遥感影像解译的研究热点。海量标签样本是训练深度学习模型的前提条件,但成本昂贵的人工标记样本限制了深度学习技术在遥感领域的应用。本文提出了一种基于弱样本的深度学习模型农作物分类策略：以GF-1影像为数据源,将传统分类器SVM分类结果视为弱样本,训练深度卷积网络模型DCNN (Deep Convolutional Neural Networks),获取辽宁省水稻和玉米的空间分布,分析弱样本的适用性。结果显示：测试集总体精度达到0.90,水稻和玉米F1分数分别为0.81和0.90;在不同地形地貌、复杂种植结构的农业景观下均表现出良好的分类效果;与SVM结果的空间一致性为0.90;当弱样本最大面积误差比例小于0.36时,弱样本仍适用于DCNN作物分类,结果的总体精度保持在0.86以上。综上,该策略一定程度上消除了深度学习模型对大量人工标记样本高度依赖的局限性,为实现大尺度农作物遥感分类提供了一种新途径。     

Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches

In the recent decades, the application and research of unmanned surface vessels are experiencing considerable growth, which have caused the demands of intelligent autopilots to grow along with the ever-growing requirements. In this study, the design of an autopilot based on Unscented Kalman Filter (UKF) trained Radial Basis Function Neural Networks (RBFNN) was presented. In particular, in order to provide satisfactory control performance for surface vessels with random external disturbances, the modified UKF was utilised as the weights training mechanism for the RBFNN based controller. The configurations of the newly developed free running scaled model, as well as the online signal processing method, were introduced to enable the experimental studies. The experimental and numerical tests were carried out through using the physical scaled model and corresponding mathematical model to validate the capability of the designed control system under various sailing conditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of course keeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that the developed control scheme was effective to track the desired states and robust against unpredictable external disturbances. Moreover, in comparison with Back-Propagation (BP) RBFNN and Proportional-Derivative (PD) based autopilots, the UKF RBFNN based autopilot has the comparable capability in the aspects of providing smooth and effective control laws.     

地震多属性分析技术预测和评价盐下碳酸盐岩储层厚度分布

某海上盐下L区火成岩发育、碳酸盐岩储层厚度预测难度大,地震单属性分析已不能满足研究需求,本文尝试利用地震多属性分析技术进行了储层参数预测。通过属性与已钻井储层参数交会图分析进行地震属性集优选,通过主成分分析(PCA法)和K-L变换进行地震属性压缩,通过人工神经网络技术进行全区定量预测储层厚度,计算结果与实钻井误差范围不超过5.5%,大部分井多属性方法预测精度明显高于单属性方法,储层厚度平面分布特征与地质规律吻合。实践结果表明此方法在原理和实际应用上都是可行的,能够有效地提高储层定量预测精度,为探明油田储层分布特征和后续部署开发井网方案提供了参考。     

Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin,India

Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance (AAI) and relative acoustic impedance (RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone (TS) and Barail Arenaceous Sandstone (BAS) are the main reservoirs, delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network (MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.