Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.     

Landslide identification using machine learning

Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,landslide-related data are compiled,including topographic data,geological data and rainfall-related data.Then,three integrated geodatabases are established;namely,Recent Landslide Database(Rec LD),Relict Landslide Database(Rel LD)and Joint Landslide Database(JLD).After that,five machine learning and deep learning algorithms,including logistic regression(LR),support vector machine(SVM),random forest(RF),boosting methods and convolutional neural network(CNN),are utilized and evaluated on each database.A case study in Lantau,Hong Kong,is conducted to demonstrate the application of the proposed method.From the results of the case study,CNN achieves an identification accuracy of 92.5%on Rec LD,and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing.Boosting methods come second in terms of accuracy,followed by RF,LR and SVM.By using machine learning and deep learning techniques,the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.     

Organization of Feature-, Time-, and Location-Based Mental Models

This study considers how mental models are encoded into memory by viewing visual displays like maps. A mental model is an internal representation of a situation that links objects or concepts to other objects or concepts. Previous studies indicated location-based mental models are encoded when a series of propositional statements such as the object is in the location are read from a text. Evidence that locations were being used as the basic container for an organization of mental models is provided by a significant fan effect. A fan effect shows an increase in reaction time with the number of models considered when making a decision. Features, times, and locations were considered as possible containers in the mental models. Subjects created location-based mental models, but also encoded feature-based mental models. A reverse fan effect for time, found for a map animation, suggested the order of the presentation of maps could greatly affect the structure of learned information.     

一种基于深度学习的热带气旋路径集成预报方法

本文提出了一种基于深度学习的热带气旋(tropical cyclone,TC)路径集成预报方法.该方法以长短期记忆深度网络为模型构架,利用前4个时刻(24 h,间隔6 h)及当前时刻的TC路径记录,以及由不同环境因素所计算的方向预报因子作为输入,分别直接预报和间接(通过预报移动速度)预报路径,集成两者预报结果实现时效为...     

大学英语“自主-合作学习”整合模式研究

针对现有自主学习和合作学习模式的不足,提出了大学英语"自主-合作学习"模式。该模式将学习目标、任务特质、学生认知风格、合作动机以及学习情境五个要素作为输入,教师教学、学生自主和合作学习几个环节的整合作为过程,以教学设计、教学实施和学习绩效作为输出,对大学英语教学模式的各要素、环节和结果及其关系予以整体、全面的探讨。模式结合了自主学习和合作学习两者的优势,克服了单一模式的不足,充分发挥教学模式的多重功能,为该模式的实施提供了一个概念框架。     

我国大陆强震预测的支持向量机方法

统计学习理论是研究小样本情况下机器学习规律的理论. 支持向量机是基于统计学习理论框架下的一种新的通用机器学习方法. 它不但较好地解决了以往困扰很多学习方法的小样本、过学习、高维数、局部最小等实际难题,而且具有很强的泛化（预测）能力,其预测效果通常优于人工神经网络. 我国大陆强震与全球主要板块边界的强震活动之间具有一定的关系,但是这种关系具有较强的非线性. 尽管这种关系还不清楚, 但是通过支持向量机可以很好地进行建模, 并对我国大陆强震进行预测.      

A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainland

Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth. In this study, we proposed a deep learning (DL) method based on convolutional neural network (CNN), named SfNet, to derive the v_S model from the Rayleigh wave phase and group velocity dispersion curves. Training a network model usually requires large amount of training datasets, which is labor-intensive and expensive to acquire. Here we relied on synthetics generated automatically from various spline-based v_S models instead of directly using the existing v_S models of an area to build the training dataset, which enhances the generalization of the DL method. In addition, we used a random sampling strategy of the dispersion periods in the training dataset, which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset. Tests using synthetic data demonstrate that the proposed method is much faster, and the results for the v_S model are more accurate and robust than those of conventional methods. We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent (ChinaVs-DL1.0), which has smaller dispersion misfits than those from the traditional method. The high accuracy and efficiency of our DL approach makes it an important method for v_S model inversions from large amounts of surface-wave dispersion data.     

图残差神经网络支持下的建筑物群组模式分类

建筑物作为城市中的重要地物,分析其群组模式对地图综合、导航定位、市政规划等具有重要作用。建筑物群组模式分析目前主要有基于规则的方法和基于机器学习的方法两种。基于规则的方法和基于传统机器学习分类器的方法均需要大量的人工处理过程。近年来兴起的深度学习特别是图卷积神经网络前期无需人工处理,因此提高了建筑物群组模式分析的自动化程度。传统的图卷积神经网络模型在训练深层网络时易出现退化问题,提取深层特征困难。为解决此问题,本文引入了图残差神经网络模型用于建筑物群组的模式分类。首先使用道路和河流等作为约束条件,利用K-means方法对建筑物进行聚类;然后根据Bertin视觉变量计算对应的建筑物特征指标,在每个建筑物群组中以建筑物质心为节点,连接节点的最小生成树作为边,构建建筑物群组图结构;最后将得到的图结构数据输入图残差神经网络进行训练,得到规则和不规则两种建筑物群组模式。试验结果表明,该模型较好地解决了传统图卷积神经网络模型的退化问题,并取得了更高的精度。     

Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method

Utilizing data from controlled seismic sources to image the subsurface structures and invert the physical properties of the subsurface media is a major effort in exploration geophysics. Dense seismic records with high signal-to-noise ratio (SNR) and high fidelity helps in producing high quality imaging results. Therefore, seismic data denoising and missing traces reconstruction are significant for seismic data processing. Traditional denoising and interpolation methods rarely occasioned rely on noise level estimations, thus requiring heavy manual work to deal with records and the selection of optimal parameters. We propose a simultaneous denoising and interpolation method based on deep learning. For noisy records with missing traces, we adopt an iterative alternating optimization strategy and separate the objective function of the data restoring problem into two sub-problems. The seismic records can be reconstructed by solving a least-square problem and applying a set of pre-trained denoising models alternatively and iteratively.We demonstrate this method with synthetic and field data.     

机器学习在地震检测与震相识别的应用综述

在地震学研究中地震检测与震相识别是最基础的环节,其拾取速度和精度直接影响其在地震精确定位以及地震层析成像中的应用效率和精度。近年来,机器学习在地震学领域中引起广泛关注。机器学习可以改进传统地震检测和震相识别方法,使它们能达到更加准确,识别率更高的效果。把机器学习方法按照监督学习和无监督学习分类介绍,并对机器学习方法流程进行总结,并对目前在地震检测与震相识别方面应用较为广泛的机器学习方法(卷积神经网络、指纹和相似性阈值、广义相位检测、PhaseNet、模糊聚类)进行综述。结果表明:机器学习在地震事件检测和震相识别将会是主要的手段。数据驱动的机器学习在地震学中的应用和物理模型的联合运用将是未来的发展趋势。