东濮凹陷三叠系砂岩储层构造裂缝特征及形成期次

根据露头、 岩心、 薄片、 古地磁、 成像测井、 岩石力学实验等资料;对东濮凹陷北部三叠系砂岩储层构造裂缝特征及形成期次进行分析。东濮凹陷三叠系砂岩储层主要发育北北东、 北东和近东西向3组高角度构造裂缝;裂缝走向近于平行或垂直主断层;倾向为北西和南东。二马营组裂缝较为发育;和尚沟组和刘家沟组裂缝相对不发育。储层裂缝为3期;分别为燕山早期的北西向扩张裂缝和北北西、 北西西向剪切裂缝;燕山晚期形成北东向拉张裂缝;喜马拉雅期形成北北东、 北东向拉张裂缝、 北北东向剪切裂缝和东西向扩张裂缝。古、 今构造应力场和岩石力学性质的各向异性分析表明;现今构造应力场最大水平主应力和岩石抗压强度的各向异性均影响燕山早期北西向裂缝的张开度及有效性。储层裂缝表现为两期;分别为燕山晚期的北东向拉张裂缝、 喜马拉雅期的北北东、 北东向拉张裂缝;北北东向剪切裂缝和东西向扩张裂缝。研究结果阐明了东濮凹陷北部三叠系砂岩储层构造裂缝特征及发育规律。     

金乡县节约集约用地调查分析及对策

近年来,随着经济社会的迅速发展和工业化、城镇化的顺利推进,金乡县土地供应"瓶颈"制约矛盾日益突出。该文对全县节约集约利用土地的现状、存在的问题及制约因素进行调查分析,提出推进节约集约用地的对策与措施,必将更好地推动全县经济与社会的科学发展。     

金乡县新型农村社区建设初探

建设新型农村社区是统筹城乡发展,破除城乡二元结构,落实中央政策的重大举措。该文从分类规划、分步实施、政策引领、强化质监等方面,介绍了金乡县在新型农村社区建设中的几点做法,对各地新型农村社区建设工作具有借鉴意义。     

贵州主汛期极端降水事件及其环流特征分析

利用贵州地区52个测站1961-2006年历年主汛期(6-8月)逐日降水资料,定义了不同台站的极端降水阈值,统计了贵州近46年主汛期极端降水事件发生的频次,并分析了极端降水的时空分布特征、周期振荡特征以及极端降水事件典型多年、少年的环流特征.结果表明:贵州主汛期极端降水自北向南逐渐增大,极端降水事件存在着明显的年际、年代际变化特征,并存在2.9 a、20 a的周期振荡特征,在贵州主汛期极端降水事件偏多和偏少时期,其环流特征存在着显著的差异.     

沙漠与城市气溶胶散射系数变化比较

利用塔克拉玛干沙漠大气环境观测试验站和中国气象局乌鲁木齐沙漠气象研究所承担的乌鲁木齐大气成分观测站2010年1月1日至12月31日的单波段积分浊度计的观测资料,分析了沙漠（塔中）和城市（乌鲁木齐）两种不同下垫面地区的气溶胶散射系数的变化特征。（1）散射系数小时平均值：塔中地区变化范围在39.6～8 442.8 Mm^-1,平均值318.4 Mm^-1;乌鲁木齐变化范围在28.9～6 590.0 Mm^-1,平均值451.5 Mm^-1;乌鲁木齐小时平均值在1 000 Mm^-1以上的高值区间大于塔中地区。（2）散射系数日变化：塔中呈现单峰变化,其中最大值387.0 Mm^-1出现于凌晨02：00,最小值276.4 Mm^-1出现于午后的17：00;乌鲁木齐呈现三锋变化,3个峰值分别出现于09：00、14：00、22：00,与塔中地区存在明显差异。（3）散射系数月均值变化：塔中的月平均值最大是4月,为500.2 Mm^-1,最小是2月,为145.9 Mm^-1;乌鲁木齐月均值最大在2月,为1 086.3 Mm^-1,最小值在7月,为101.1 Mm^-1,两地区月均值变化整体呈现相反的现象。（4）散射系数季节变化：塔中地区春季>夏季>秋季>冬季;乌鲁木齐冬季>秋季>春季>夏季。     

中国老挝跨境生物多样性保护区的林地变化(英文)

This paper analyzed forest land use change in Trans-Boundary Laos-China Biodiversity Conservation Area which is called Namha National Biodiversity Conservation Areas (Namha NBCAs), with 1992 and 2002 TM (ETM). Based on GIS and statistical methods, the intensity, state index of land use change and transfer matrix were used to study spatiotemporal land use change in the region. The results showed that the area of forest land decreased greatly, also the area of bamboo and grassland decreased. The other way round, the area of unstocked forest, slash and burn and rice paddy land increased. As a whole, the intensity of forest land change was higher, but others were lower. From the transfer matrix, most of the forest land changed into unstocked forest and urban area, some to slash and burn. The grassland and bamboo were mainly transferred to forest and unstocked forest. The unstocked forest land mostly came from forest land and grassland. It was showed there were three main causes of the land use change namely: opium poppy cultivation, rubber plantation and also ecotourism activities and finally some policies were developed in order to address the land use change in the study areas.     

利用Sentinel-1数据和SBAS-InSAR技术监测西安地表沉降

哨兵一号(Sentinel-1)数据是目前现势性较好的免费SAR数据,且因其6天的重访周期,非常适合In SAR地表形变监测。本文以西安市城区及周边为研究区,开展基于多期Sentinel-1数据和短基线集干涉(SBAS-In SAR)技术的时序地表沉降监测方法的探索,研究形成了详细的数据处理流程,利用已有研究资料佐证了方法的有效性。监测表明:2015—2016年,绝大部分区域地表形变速率位于[-33~30]mm/a区间内,228 d监测期内累积沉降量最大约75 mm,发生在目前西安最大沉降中心鱼化寨;相比20世纪末,沉降强度大幅减弱,沉降严重区域由西安市东郊向南郊转移,且沉降范围减小。     

Multi-granularity periodic activity discovery for moving objects

With the development of location-based services, more moving objects can be traced and a great deal of trajectory data can be collected. Periodicity is very commonly used to analyse the habits of moving objects, so finding objects’ periodic patterns can aid in understanding their behaviour. However, objects’ periodic patterns are always unknown previously, and describing their periods with different granularities will create some surprised findings. This article proposes a multi-granularity periodic activity discovery (MPAD) approach for moving objects. First, a multi-granularity model is introduced to describe the spatial and temporal information of an object’s activities. Then, two algorithms, namely, spatial first and temporal first multi-granularity activity discovery algorithms, are provided to transfer objects’ activities into different granularities. Finally, a novel periodic discovery algorithm is described to find the periodicities of objects’ activities. Experiments on both synthetic and real datasets demonstrate both the efficiency and effectiveness of the proposed work and its notably improved running performance compared to the same algorithms. Additionally, the discovered periodic patterns are more practically significant.     

南海北部孔隙水碘与天然气水合物成藏关系研究

天然气水合物是一种重要的新型能源。然而,深海水合物具有埋藏深、作业难度大等特点,水合物难以被高效准确地识别,这已成为制约南海水合物地质调查的瓶颈之一。针对这一问题,本文结合近年来国际上新兴的碘及碘同位素方法,对南海北部台西南盆地两个站位的柱状沉积物(HD-86V和HD-109)开展了水合物成藏识别的研究。该沉积物孔隙水中碘离子含量分别介于8.3~132.0μmol/L和7.4~118.1μmol/L之间,远高于正常海水值(0.44μmol/L)。结合区域水合物勘查结果及甲烷含量特征,发现孔隙水碘离子含量与甲烷含量表现为显著的正相关关系。对比琼东南盆地及神狐工区等区域数据,均表明沉积物孔隙水碘离子含量在南海水合物成藏示踪方面具有良好的应用前景和推广价值。     

Multivariate Adaptive Regression Splines Application for Multivariate Geotechnical Problems with Big Data

Despite the rapid increases in processing speed and memory of low-cost computers, the enormous computational costs of running complicated numerical analyses such as finite element simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization since many geotechnical problems are highly nonlinear and multivariate. To reduce the cost, surrogate models, also known as meta-models, are constructed and then used in place of the actual numerical simulation models. To ensure the surrogate model is more reliable, the ranges of the design variables should be as wide as possible. Thus meta-modeling techniques capable of analyzing multivariate problems are desirable. This paper explores the use of a fairly simple nonparametric regression procedure known as multivariate adaptive regression splines (MARS) in approximating the relationship between the inputs and outputs with a big data. First the basis of the MARS methodology and its associated procedures are explained in detail. Then two complicated geotechnical problems are presented to demonstrate the function approximating capabilities of MARS and its efficiency in dealing with multivariate problems involving large amounts of data. This paper demonstrates that the MARS algorithm is capable of producing simple, accurate and easy-to-interpret models and estimating the contributions of the input variables.