关键词： complex carbonate rock   decision tree method   lithology identification of well logging   machine learning method   Sulige Gas Field   决策树方法   复杂碳酸盐岩   机器学习方法   测井岩性识别   苏里格气田   Language of Keywords: English   Chinese  

摘要： The reservoir of Block Sudong41-33 in Sulige Gas Field has the characteristics of low porosity，low permeability and high heterogeneity. The carbonate reservoir in Lower Ordovician Majiagou Formation were subjected to multi-stage multi-type construction，sedimentary and other effects，which makes the lithology complex and diverse，and thus the accurate identification of lithology has become a difficult problem of development in this area. In recent years，more and more attention has been focused on the use of decision tree method in machine learning in the field of geoscience，especially in lithology prediction. Based on the data of well logging and the analysis of lithological parameters，six kinds of well logging parameters that are sensitive to lithology were selected，which includes acoustic time difference（AC），natural gamma ray （GR），photoelectric absorption cross section index（PE），density（DEN），deep lateral resistivity（RLLD）and compensated neutron（CNL）. Through the analysis of the six well logging parameters，a multi classifier was constructed based on decision tree method，and the information of lithology and rock characteristics were fused. Compared with the lithologic data of well logging，the recognition accuracy is over 80%. When compared with the Naive Bayesian，the accuracy of lithology recognition is improved by 13%. [ABSTRACT FROM AUTHOR]

关键词： Machine learning   Scattering pattern   Hexagonal prisms   Ice crystals   Size   Aspect ratio   Ray tracing with diffraction on facets   Zernike moments  

摘要： Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 mu m), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 mu m in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles' orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle's size and size PADs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： 岩屑成分预测   砂砾岩   机器学习   百口泉组  

摘要： 选择凝灰岩岩屑作为预测对象,对测井数据进行标准化处理,对砂砾岩储层薄片鉴定结果和测井数据进行相关性分析,优选对岩屑敏感的CNL、GR、RT、RI、SP测井参数作为训练学习的对象;分别利用SVM、BP神经网络、CART、BP神经网络-Bagging、CART-Bagging、随机森林等机器学习方法建立岩屑预测模型,对西北缘X723井百口泉组岩屑成分进行预测、对比和分析。结果表明:单个机器学习方法预测效果不佳,而经集成学习方法优化的BP神经网络-Bagging、随机森林取得较好的实验结果,尤其是随机森林的预测效果最好,平均相对误差绝对值为17.17%,证实机器学习方法在本工区预测岩屑成分是有效的,可以进行推广。

关键词： GENETICS of Alzheimer's disease   SINGLE nucleotide polymorphisms   MOLECULAR structure   BIOMEDICAL engineering   MACHINE learning  

关键词： COX抑制剂   机器学习方法   自组织特征神经网络   随机森林   支持向量机  

摘要： 针对目前COX(环氧合酶)抑制剂较少且抑制效果差的问题,以及传统的化学实验筛选COX抑制剂分子的方法中成本高且效率低的问题,基于机器学习算法,提出并建立了一种COX抑制剂的预测模型。该模型可高效且准确地找到COX抑制剂,通过大量搜集文献中的数据建立数据集,使用Mold2软件计算化合物分子描述符,利用自组织特征映射神经网络(SOM)划分训练集和测试集,应用随机森林(RF)和支持向量机(SVM)等机器学习算法分别建立了COX抑制剂预测模型。实验对比发现,SOM结合RF算法较传统化学实验方法具有更好的预测精度,且预测效率也有大幅提升。实验研究表明,基于自组织神经网络和随机森立的机器学习方法建立的COX抑制剂预测模型,具有很好的分类预测效果,可以为COX抑制剂的分析与预测提供有力的研究工具。

关键词： 架空输电线   舞动   预警   机器学习方法   BP神经网络  

摘要： 为了保证在覆冰舞动环境下输电线路的正常运维,将输电线舞动预警问题归结为有监督机器学习方法下的分类预测问题,提出了一种基于BP神经网络的舞动预警方法。通过分析影响舞动的外界气象因素,构建了以风速、风向与线路的夹角、相对湿度以及温度为输入特征量的BP神经网络学习算法,判断是否达到易舞气象条件预测输电线的舞动情况,并采用评价指标评估其预警性能,以便进行模型改进。采用河南电网舞动相关历史数据进行算例分析,验证了所提方法的有效性和实用性。输出的预警结果可为电网运维人员合理制定调度决策提供支持,保证电网安全迎峰度冬。

关键词： QTAIM   Quantum chemical topology (QCT)   IQA   Kriging   Machine learning   Force field development   S22   Complexes   Dispersion  

摘要： Here, we extend the system energy prediction approach used in the force field FFLUX (Maxwell et al. Theor Chem Acc 135:195, 2016) to complexes bound by weak intermolecular interactions. The investigation features the first application of the approach to bound complex systems, additionally challenged by investigating complexes held together only weakly, through either a predominant dispersion contribution, or through mixed dispersion and hydrogen-bonding. Our approach uses the interacting quantum atoms (IQA) energy partitioning scheme to obtain the intra-atomic, , and interatomic, , energies, which when summed, compose the molecular energy, . The and energies are mapped to the positions of the nuclear coordinates through the machine learning method kriging to build atomic energy models. A model's quality is established through its ability to accurately predict the atomic and molecular energies of atoms in an external test set. Mean absolute error percentages (MAE%) of 1.5, 1.5, 1.6, 1.0, 2.6 and 1.7% are obtained in recovering the molecular energy for ammoniaaEuro broken vertical bar benzene, wateraEuro broken vertical bar benzene, HCNaEuro broken vertical bar benzene, methaneaEuro broken vertical bar benzene, stacked-benzene (C (2h)) dimer and T-benzene (C (2v)) dimer complexes, respectively.

关键词： Hemileia vastatrix   Disease discrimination   Red edge   Random forest   Variable optimization  

摘要： Coffee leaf rust (CLR) caused by the fungus Hemileia vastarix is a devastating disease in almost all coffee producing countries and remote sensing approaches have the potential to monitor the disease. This study evaluated the potential of Sentinel-2 band settings for discriminating CLR infection levels at leaf levels. Field spectra were resampled to the band settings of the Sentinel-2, and evaluated using the random forest (RF) and partial least squares discriminant analysis (PLS-DA) algorithms with and without variable optimization. Using all variables, Sentinel-2 Multispectral Imager (MSI)-derived vegetation indices achieved higher overall accuracy of 76.2% when compared to 69.8% obtained using raw spectral bands. Using the RF out-of-bag (OOB) scores, 4 spectral bands and 7 vegetation indices were identified as important variables in CLR discrimination. Using the PLS-DA Variable Importance in Projection (VIP) score, 3 Sentinel-2 spectral bands (B4, B6 and B5) and 5 vegetation indices were found to be important variables. Use of the identified variables improved the CLR discrimination accuracies to 79.4 and 82.5% for spectral bands and indices respectively when discriminated with the RF. Discrimination accuracy slightly increased through variable optimization for PLS-DA using spectral bands (63.5%) and vegetation indices (71.4%). Overall, this study showed the potential of the Sentinel 2 MSI band settings for CLR discrimination as part of crop condition assessment. Nevertheless further studies are required under field conditions.

关键词： Machine learning   Tree model   Bagging   Random Forest   Wastewater hydraulics   Drop manhole   Side weir   Experimental research  

摘要： Wastewater hydraulics problems are frequently addressed by investigation on physical models. Dimensional analysis is a powerful tool that allows discovering essential information about the investigated phenomenon, but in some cases it is affected by significant limitations. In such cases, many issues can be addressed by means of machine learning algorithms, resulting from the theories on pattern recognition and computational learning. In order to show the potential of such an approach, in this study Regression Tree M5P model, Bagging algorithm and Random Forest algorithm were applied to the solution of some complex problems of wastewater engineering: the prediction of energy loss, the pool depth, the air entrainment in a drop manhole, and the forecasting of the lateral outflow in a low crested side weir. The algorithms were trained and tested on data obtained from experimental tests that were carried out at the Water Engineering Laboratory of the University of Cassino and Southern Lazio. In most of the considered cases, regression trees and ensemble methods were able to provide very accurate predictions.