基于走时的保幅偏移方法

振幅随偏移距变化是描述储层特征的重要方法之一,保幅偏移方法就是使偏移剖面能够反映出振幅随偏移距的变化.本论文中的保幅偏移是以走时为基础,主要的方法是采用走时的双曲线展开法,通过走时的二阶空间导数来确定波前曲率.该方法通过建立在大网格上的走时表来确定插值系数,将大网格插值成为较为精细的网格,这样就节省了数据的存储空间.对于相同的网格密度,通过插值来计算走时表比采用程函方程有限差分法直接计算走时要节省5至6倍的时间.走时的插值系数还可以用来计算几何扩散因子、权函数,不仅提高了成像质量,还大大节省了计算时间.     

Effect of farmland expansion on drought over the past century in Songnen Plain,Northeast China

The effects of human activities on climate change are a significant area of research in the field of global environmental change. Land use and land cover change(LUCC) has a greater effect on climate than greenhouse gases, and the effect of farmland expansion on regional drought is particularly important. From the 1910 s to the 2010 s, cultivated land in Songnen Plain increased by 2.67 times, the area of cultivated land increased from 4.92×10~4 km~2 to 13.14×10~4 km~2, and its percentage of all land increased from 25% to 70%. This provides an opportunity to study the effects of the conversion of natural grassland to farmland on climate. In this study, the drought indices in Songnen Plain were evaluated from the 1910 s to the 2010 s, and the effect of farmland expansion on drought was investigated using statistical methods and the Weather Research and Forecasting Model based on UK's Climatic Research Unit data. The resulting dryness index, Palmer drought severity index, and standardized precipitation index values indicated a significant drying trend in the study area from 1981 to 2010. This trend can be attributed to increases in maximum temperature and diurnal temperature range, which increased the degree of drought. Based on statistical analysis and simulation, the maximum temperature, diurnal temperature range, and sensible heat flux increased during the growing season in Songnen Plain over the past 100 years, while the minimum temperature and latent heat flux decreased. The findings indicate that farmland expansion caused a drying trend in Songnen Plain during the study period.     

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.     

成渝城市群城市建设用地扩张格局及规模体系分析

基于1990—2018年六期城市建设用地监测数据,本文利用城市用地年面积增长率指数、公共边测度、核密度估计、位序-规模法则等方法,刻画成渝城市群扩张演化特征及其发展规律,并对城市群规模体系进行分析。结果表明：成渝城市群城市建设用地扩张呈先快后慢的趋势,空间上呈“中部高速,南北低速”的特点,建设用地扩张密度高值主要集中在成都、重庆二市且城市群内部连片发展不明显。城市群新增城市用地呈现无序蔓延态势,并未趋于紧凑。2000—2010年,成渝城市群规模分布极化,呈现明显的双核首位特征,2010年后规模分布由非均衡趋于均衡。     

基于地理国情数据的城市扩张和生态格局分析——以武汉市为例

城市扩张是城镇化的主要特征,研究城市扩张规律对城市空间规划、高质量发展提供决策依据。本文以武汉市为研究区,以2015—2018年地理国情监测成果为主要数据源,通过数据整理与提取、计算城市扩张特征指数、提取城市格局变化、计算生态景观指数等处理,对研究区城市扩张和生态格局变化特征进行分析。研究结果表明：2015—2018年间,武汉市从快速扩张阶段逐渐减缓,紧凑度逐渐下降;中心城市扩张主要以种植土地破坏为代价;远城区快速扩张主要以林草面积减少为代价;在生态指数上,各生态景观类型破碎度均有下降,但水域面积保持良好。     

城市扩展元胞自动机多结构卷积神经网络模型

传统的城市扩展元胞自动机(CA)模型是基于单个元胞的变量信息挖掘来构建转换规则的。针对这一问题,本文基于多结构卷积神经网络提出从区域特征出发且顾及区域多尺度特征挖掘转换规则的城市扩展元胞自动机模型(MSCNN-CA),并以武汉主城区和上海浦东新区为例,模拟了两个试验区2005—2015年期间城市扩展过程。模型验证表明:与逻辑回归和神经网络相比,本文构建的3个单一结构的卷积神经网络元胞自动机(CNN-CA)模型在4个指标(Kappa系数、FoM(figure of merit)值、命中率(h)和错误率(m))上都有不同程度的提高。特别是FoM指数,在武汉主城区提高了23.3%~29.4%,在上海浦东新区提高了20.3%~28.5%。此外,MSCNN-CA模型与3个单一结构的CNN-CA模型相比,在各个指标上也有所改善,FoM指数在武汉主城区提高了0.8%~4.8%,上海浦东新区提高了2.8%~7.8%。两个试验区的模拟结果表明:相比传统CA模型,基于多结构卷积神经网络的城市扩展元胞自动机模型(MSCNN-CA)能够有效提高城市扩展模拟的精度,更真实地反映城市扩展空间演变过程。相比单结构的卷积神经网络CA模型,多结构卷积神经网络CA模型的稳定性和模拟结果准确性有所提升。     

撤县(市)设区对城市空间扩展的影响机理——以杭州市为例

城镇化的持续推进对城市空间扩展产生了新的需求,撤县（市）设区是中国城市市辖区实现扩容提质的主要手段之一。以杭州为例,定量测度撤县（市）设区后杭州城区空间扩展的时空特征及其各方面的影响,并探讨了这一区划调整对城市空间扩展影响的作用机理。研究发现：通过撤县（市）设区,杭州城区的建设用地东扩趋势明显,萧山和余杭两区的建设用地新增面积和扩展速度均高于平均水平,且两地建设用地空间存在多中心集聚特征,成为新一轮的杭州建设用地扩展高密度区,城市功能不断凸显。总体来看,撤县（市）设区对城市空间的影响是间接的,具有一定的滞后性,通过引导城市空间扩展的主要方向,实现中心城市人口和产业向新设市辖区的转移和集聚,提供城市空间扩展的载体。撤县（市）设区后需要通过后续的城市规划修编、土地性质和权限调整、生产要素集聚与空间重组等路径,对城市空间扩张与空间演变产生直接影响。     

福建省城市扩张与经济增长的互动关系研究

基于2000~2015年福建省9个设区市的城市建成区面积与非农GDP的历史数据,根据生态位理论将9个设区市分为4个等级,结合库兹涅兹曲线分析、协整检验及Granger因果关系分析、脱钩分析3种方法对不同等级城市之间的城市扩张和经济发展的互动关系进行了研究。研究表明:福建省城市生态位呈现东高西低,南高北低的空间格局;9个设区市均符合广义的库兹涅兹曲线关系,但只有高生态位城市全部符合倒“U”型曲线关系;福建省多数城市用地扩张与经济增长之间的Granger因果关系受区位、政策和决策等因素影响并不明显;福建省9个设区市的脱钩关系主要呈现为增长负脱钩、弱脱钩、强脱钩和增长连接4类,绝大多数城市多数时期以弱脱钩状态为主。     

面向城区宽基线立体像对视角变化的结构自适应特征点匹配

提出一种基于结构自适应特征的城区宽基线影像特征点匹配方法。首先,对影像提取点特征和直线特征,挖掘点特征与其邻域内直线特征之间的几何关系,构建结构自适应的特征区域和特征描述符,并通过双向匹配策略获得初始匹配结果。然后,基于初匹配结果估计影像基础矩阵,构建核线约束的结构自适应特征匹配算法进行二次匹配。最后,将已匹配特征作为控制基础设计匹配扩展算法,进一步增加匹配点数量。本文方法以特征点邻域几何结构为出发点,构建自适应的特征区域,能够在显著的影像视角变化下,为同名特征点提取影像内容一致的特征区域,进而获得相似的特征描述符。试验结果证明,与传统算法相比,本文方法在城区宽基线影像上能够同时获得更多的正确匹配特征和更高的匹配正确率。     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.