Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.     

石英标准生长曲线在青藏高原东缘黄土光释光测年中的应用

石英标准生长曲线（SGC）法在黄土高分辨率光释光定年中有大幅节省测试时间的潜力,然而目前对该方法在不同时空尺度上的适用性尚存在争议。本文选取青藏高原东缘的典型黄土样品,通过系统地分析石英光释光生长曲线特征,检验SGC法和最小二乘法标准化生长曲线（LS-SGC）法在高原东缘黄土光释光定年中的适用性,结果显示：1）高原东缘黄土石英光释光信号以快组分为主,且同一地点不同样品、不同测片的生长曲线形态相似;2）当等效剂量（D_e）小于100 Gy时,不同地点样品石英生长曲线形态一致;大于100 Gy时生长曲线则具有不同的剂量响应特征;3）通过SGC法获得的D_e仅在0~100 Gy与实测值一致;LS-SGC法拟合的生长曲线结果更佳,并且LS-SGC法计算的D_e在100~200 Gy与实测D_e仍具有较好的一致性。研究结果有助于深入研究高原黄土光释光测年和风成沉积过程,同时也可为其他区域建立石英光释光标准生长曲线提供借鉴。

     

The Existence and Significance of Two Kinds of Effective Reservoirs in Deep Water Area of the Western Qaidam Basin

<正>Objective Deep-water deposit has become one of the greatest potential and economic areas for petroleum exploration. In the western Qaidam Basin, the deep-water sedimentary area account for nearly 2/3 of the basin area, but the related reports is less. Scholars generally believed that the salt water medium can inhibit the extension of the sand(Qian et al., 1984).     

甘青地区马家窑文化彩陶贸易及其动力探讨

马家窑文化是甘青地区仰韶文化晚期阶段一个重要的文化遗存类型,其以精美的彩陶闻名于世,对于马家窑文化彩陶的交流传播研究受到了学术界的广泛关注。本文运用X-荧光光谱分析法对甘青地区5个典型马家窑文化早期阶段遗址出土的92件彩陶陶片样品和各遗址附近采集的42份红粘土及黄土样品进行化学元素组成分析,追溯彩陶的产源地及贸易。同时基于陶器的起源传播理论,收集整理马家窑文化区遗址出土的粟（Setaria italica）、黍（Panicum miliaceum）农作物以及动物骨骼遗存鉴定资料,探讨马家窑彩陶贸易的动力。Ti/Al-Na/K和主成分分析结果显示宗日文化区宗日遗址的马家窑文化彩陶元素组成特征和马家窑文化主体区域的彩陶一致,表明宗日遗址马家窑类型彩陶是从马家窑文化主体区域贸易而来;马家窑文化彩陶的传播和该地区粟黍农作物的交流传播具有时空一致性,粟黍农业人群沿黄河上游谷地向共和盆地的扩散带来了新的文化元素;宗日文化区和马家窑文化核心区生业模式的差异带来了跨区域间文化交流的动力,这也可能是马家窑文化彩陶贸易的动力。

     

末次冰盛期-全新世东海陆坡滑塌沉积的地质年代与沉积学证据

基于东海陆坡区OT12-01孔长度为5.35 m沉积物AMS 14C测年、高分辨率粒度分析和XRF岩芯元素扫描数据,识别出了末次冰盛期(LGM)至全新世期间发生的多次滑塌事件。研究发现,OT12-01孔全新世晚期沉积层缺失,LGM至全新世期间呈现AMS 14C年龄模式频繁倒转、沉积物粒度、元素比值垂向上多处突变或"错动"等特征,保存了LGM至全新世非连续的沉积记录。OT12-01孔沉积物主要来源于低海平面时期的长江/黄河物质,OT12-01孔是由中国大陆陆源物质在东海陆架经水动力分选,细颗粒被搬运至东海陆坡后,发生多次滑塌形成。LGM时期物源供给是OT12-01孔形成滑塌沉积的重要因素,末次冰消期海平面快速上升可能是高频滑塌沉积的触发原因,而低海平面时期甲烷水合物溢出、频繁的地震和火山喷发可能是海底滑坡作用发生的诱因。     

敏感频率地震属性在薄层砂体预测中的应用——以松辽盆地肇源地区为例

以松辽盆地北部肇源地区Y5井区扶余油层河道砂体为研究对象,针对砂体厚度薄、横向变化快以及相同厚度砂体地震响应特征不一致的现象,在正演模拟分析目标砂体的地震响应特征基础上,采用时频分析技术,通过广义S变换在频率域优选不同厚度及组合砂体对应的敏感频率,依托敏感频率重构数据,消除砂体理论响应地震波形与地震剖面特征不一致情形,进而优选地震属性预测砂体展布。研究结果表明:利用敏感频率重构数据体提取的地震属性,可以有效提高薄层河道砂体的预测精度,大大降低油田勘探开发风险。     

Identifying geospatial services across heterogeneous taxonomies

Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach.