Structural architecture of Neoproterozoic rifting depression groups in the Tarim Basin and their formation dynamics

The Tarim Basin is the largest, oil-bearing, superimposed basin in the northwest of China. The evolution and tectonic properties of the initial Tarim Basin have been hotly disputed and remain enigmatic. The Neoproterozoic basin is covered by a vast desert and a huge-thickness of sedimentary strata, has experienced multiple tectonic movements, had a low signal to noise ratios(SNRs) of deep seismic reflection data, all of which have posed critical obstacles to research. We analysed four field outcrops, 18 wells distributed throughout the basin, 27 reprocessed seismic reflection profiles with higher SNRs across the basin and many ancillary local 2D and 3D profiles and aeromagnetic data. We found about 20 normal fault-controlled rifting depressions of the Cryogenian and Ediacaran scattered throughout the basin, which developed on the Precambrian metamorphic and crystalline basement. The structural framework is clearly different from that of the overlying Phanerozoic. The rifting depressions consist of mainly half grabens, symmetrical troughs and horst-grabens. From the northeast to southwest of the basin,they are divided into three rifting depression groups with the WNW, ENE, and NW-trends that are mainly controlled by normal faults. The maximum thicknesses of the strata are up to 4100 m. From the Cryogenian to Ediacaran, most of the main inherited faults to active and eventually ceased at the end of the Ediacaran or Early Cambrian, while subsidence centres appeared and migrated eastward along the faults. They revealed that the different parts of the Tarim continental block were in NNE-SSWoriented and NNW-SSE-oriented extensional paleo-stress fields(relative to the present) during the Neoproterozoic, and were accompanied by clockwise shearing. According to the analysis of the activities of syn-sedimentary faults, filling sediments,magmatic events, and coordination with aeromagnetic anomalies, the tectonic properties of the fault depressions are different and are primarily continental rifts or intra-continental fault-controlled basins. The rifting phases mainly occurred from 0.8–0.61 Ga.The formation of the rifting depression was associated with the initial opening of the South Altun-West Kunlun Ocean and the South Tianshan Ocean, which were located at the northern and southern margins of the Tarim Block, respectively, in response to the break-up of the Supercontinent Rodinia and the initial opening of the Proto-Tethys Ocean.     

Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques

A framework for the generation of bridge-specific fragility curves utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and expensive resimulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of each uncertain variable in their seismic demand model. The methodology is demonstrated through the case study of a multispan concrete bridge class in California. Geometric, material, and structural uncertainties are accounted for in the generation of bridge numerical models and their fragility curves. It is also noted that the traditional lognormality assumption on the demand model leads to unrealistic fragility estimates. Fragility results obtained by the proposed methodology can be deployed in a risk assessment platform such as HAZUS for regional loss estimation.     

基于极限学习机的GF-2影像分类

遥感图像分类是提取图像有效信息过程中重要的一部分,为了探寻最优的分类方法,许多机器学习算法逐步应用于遥感分类中。极限学习机（extreme learning machine,ELM）以其高效、快速和良好的泛化性能在模式识别领域得到广泛应用。本文采用训练速度快、运算量小的极限学习机算法与支持向量机（support vector machines,SVM）算法和最大似然法进行分类对比,对高分辨率遥感图像进行分类,分析极限学习机算法对于遥感图像分类的准确度等性能。选取吉林省长春市部分区域的GF-2遥感数据,将融合后的影像设置为原始数据,利用3种方法进行分类。研究结果表明,极限学习机算法分类图像总体分类精度达到85%以上,kappa系数达到0.718,与其他分类方法相比分类准确度较高,且极限学习机运行时间比支持向量机运行时间约短2 480 s,约为支持向量机运行时间的1/8,因此具有良好的性能和实用价值。     

Geological evidence for neotectonic activity during deglaciation of the southern Sperrin Mountains,Northern Ireland

In the southern Sperrin Mountains, Northern Ireland, stacked glacigenic sequences that accumulated during deglaciation (ca. 17000–13000 yr BP) overlie a basement of jointed and mascerated bedrock. The glacigenic sequences comprise interbedded glaciotectonic shears, diamictic breccias and rock rafts. At one site a normal fault with a metre‐scale vertical displacement cuts through part of the sequence and is overlain by a glacial diamict. Sediments at an adjacent site show that faulting and associated hydrothermal activity was related to neotectonic reactivation of pre‐existing Caledonian lineaments caused by ice unloading. From stratigraphical and directional evidence, fault reactivation occurred early in the deglaciation after north central Ireland ice had retreated southwards into lowland areas, but before Sperrin Mountain ice readvanced from the north. This relationship provides evidence for the relative timing of neotectonic activity in Northern Ireland, and demonstrates the effects of glacio‐isostatic unloading near ice‐sheet centres. Copyright © 1999 John Wiley & Sons, Ltd.     

基于年际增量法的卷积神经网络模型对中国夏季降水的预测研究

将前冬的500 hPa位势高度、向外长波辐射和海表温度的年际增量作为预测因子,建立基于卷积神经网络(Convolutional Neural Network, CNN)的非线性预测模型,对中国160个测站夏季降水展开预测研究,并与基于线性奇异值分解(Singular Value Decomposition, SVD)的预测模型进行效果对比。结果表明：CNN在1981—2020年的交叉检验中所回报的降水平均PS评分和距平相关系数(ACC)分别为74.33和0.12,比SVD高2.15和0.06,说明CNN比SVD在整体上对夏季降水具有更好的预测能力。其中,CNN对SVD预测较好年份的预测效果提升较为明显,对SVD预测较差的年份则改进不大。CNN对中国降水预测存在一定的系统性偏差,订正后CNN对拉尼娜年的降水预测改进较大。结果表明,基于年际增量法的CNN预测模型展示出较好的潜在应用价值。     

基于遗传支持向量机的地下工程裂隙岩体注浆量预测

提出了地下工程裂隙岩体注浆量预测的遗传支持向量机方法,通过支持向量机对实际注浆数据样本进行学习,建立注浆量及其影响因素之间的非线性映射关系,基于这种关系实现注浆量的预测。模型建立过程中,考虑到支持向量机惩罚因子和核参数对预测精度的影响,以预测误差为适应度,采用遗传算法对最佳参数进行搜索。结果表明,本文方法计算快速,预测精度高,是一种注浆量预测的好方法。     

Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques

This paper proposes a new set of probabilistic joint shear strength models using the conventional multiple linear regression method, and advanced machine‐learning methods of multivariate adaptive regression splines (MARS) and symbolic regression (SR). In order to achieve high‐fidelity regression models with reduced model errors and bias, this study constructs extensive experimental databases for reinforced and unreinforced concrete joints by collecting existing beam‐column joint subassemblage tests from multiple sources. Various influential parameters that affect joint shear strength such as material properties, design parameters, and joint configuration are investigated through tests of statistical significance. After performing a set of regression analyses, the comparison of simulation results indicates that MARS approach is the best estimation method. Moreover, the accuracy of analytical predictions of the derived MARS model is compared with that of existing joint shear strength relationships. The comparison results show that the proposed model is more accurate compared to existing relationships. This joint shear strength prediction model can be readily implemented into joint response models for evaluation of earthquake performance and inelastic responses of building frames. Copyright © 2014 John Wiley & Sons, Ltd.     

非连续变形岩石断裂分析中的一种全长剪切锚杆

锚杆从锚固方式上可划分两种：端部锚固方式和全长锚固方式。全长锚固锚杆作为工程支护中的一种重要锚杆类型,具有广泛的应用前景。非连续变形分析（DDA）以及在此基础上发展的非连续变形的岩石断裂分析（DDARF）方法中锚杆为端锚形式,且只考虑了锚杆的轴向作用,而忽略了锚杆作用力对锚固岩体的侧向限制。为了更好地理解锚杆的支护机制,考虑锚杆的剪切作用,提出了DDARF中一种新的锚固形式--全长剪切锚杆支护形式。用改进的全长剪切锚杆支护方法对一公路隧道稳定性进行了分析,并与端锚支护形式进行了对比,结果表明,提出的全长剪切锚杆支护方法是有效的,锚固效果比端锚支护更好。     

煤矿长斜井TBM施工安全风险分析与趋势预测

对煤矿长斜井盾构机（tunnel boring machine,TBM）施工的风险因素进行了识别,建立了二层次的风险评估指标体系,并确定了风险等级分类标准,利用熵权法确定风险指标的权向量,进而建立了基于集对分析法的煤矿长斜井TBM施工同异反评估模型。在此基础上利用偏联系数的理论确定了文中五元偏联系数的计算方法,根据改进的集对势理论给出风险趋势的预测方法。利用该模型对台格庙矿区煤矿长斜井（1#、2#实验井）TBM施工风险进行了评估与趋势预测。研究表明该模型与方法在煤矿长斜井TBM施工风险分析中是有效的、实用的,可为煤矿长斜井TBM施工风险分析与预测提供一种新的途径。     

深埋长隧道TBM施工关键问题探讨

针对深埋长隧道开挖所面临的高水压、高地压、高地温、大变形、难支护等问题,分析总结传统钻爆法开挖与支护技术、全断面隧道掘进机（TBM）施工技术、TBM导洞扩挖技术应用中的优劣,TBM导洞扩挖法为深埋长隧道开挖提供了新的设计思路。由于深埋长隧道的建设环境与浅埋隧道建设环境存在显著差异,TBM施工将面临3个关键问题--岩爆问题、卡盾（大变形）问题和未准确探测前方地质而发生的施工事故（涌水、突泥等）。为揭示TBM施工过程中卡盾的存在性,分别针对某一特定地质条件下深埋软、硬岩TBM施工进行理论分析和数值模拟研究。结果表明,软岩地层TBM施工发生卡盾,而硬岩完整地层TBM施工未发生卡盾。