Synthetic tsunami mareograms for realistic oceanic models

We study how the tsunami mode is generated by a scaled double-couple seismic source, and how it propagates in realistic oceanic models. The method developed and used is the direct extension to tsunami waves propagating in multilayered oceanic media of the well-known Haskell method. The most intensive tsunamis may be expected from sources located within the crust in the deep-water parts of the ocean. The extension to laterally heterogeneous structures shows that, if the thickness of the ocean liquid layer diminishes, the maximum amplitude of the tsunami wave train increases.     

Glacial isostatic adjustment in Fennoscandia for a laterally heterogeneous earth

Glaciation and deglaciation in Fennoscandia during the last glacial cycles has significantly perturbed the Earth's equilibrium figure. Changes in the Earth's solid and geoidal surfaces due to external and internal mass redistributions are recorded in sequences of ancient coastlines, now either submerged or uplifted, and are still visible in observations of present‐day motions of the surface and glacially induced anomalies in the Earth's gravitational field. These observations become increasingly sophisticated with the availability of GPS measurements and new satellite gravity missions.
Observational evidence of the mass changes is widely used to constrain the radial viscosity structure of the Earth's mantle. However, lateral changes in earth model properties are usually not taken into account, as most global models of glacial isostatic adjustment assume radial symmetry for the earth model. This simplifying assumption contrasts with seismological evidence of significant lateral variations in the Earth's crust and upper mantle throughout the Fennoscandian region.
We compare predictions of glacial isostatic adjustment based on an ice model over the Fennoscandian region for the last glacial cycle for both radially symmetric and fully 3‐D earth models. Our results clearly reveal the importance of lateral variations in lithospheric thickness and asthenospheric viscosity for glacially induced model predictions. Relative sea‐level predictions can differ by up to 10–20 m, uplift rate predictions by 1–3 mm yr⁻¹ and free‐air gravity anomaly predictions by 2–4 mGal when a realistic 3‐D earth structure as proposed by seismic modelling is taken into account.     

空间异质性定量研究方法

空间异质性是景观生态学定量化研究的核心,其定量研究方法包括景观综合图、数学模型和地学信息图谱.本文概括总结了景观综合图的内容和定量化指标,分析了这种定量化方法的优势和局限;对数学模型的复杂性、优点和与景观综合图的互补性作了简要分析,指出了数学模型研究需要进一步探讨和重视的问题;通过对地学信息图谱三个阶段(景观制图实验,图谱概念的提出和地学信息图谱理论的形成)的回顾和对其特点的分析,我们认为地学信息图谱综合了景观综合图和抽象的数学模型可以提供的互补信息,吸收了两者的优点,是空间异质性的综合定量研究方法.     

地铁对服务业集聚的影响 ——以广州市为例

以广州市为例,基于POI数据,运用SAR模型识别城市地铁开通对服务业集聚的影响.研究发现:服务业集聚具有显著的空间依赖性特征,本地服务业集聚会受到周边地区服务业集聚的影响;开通地铁对广州市服务业空间集聚产生显著的正向影响,可提高城区的服务业集聚水平,但这种集聚效应因行业不同而有所差异;地铁开通对批发与零售业、住宿与餐饮...     

热带山区高时空分辨率NDVI融合精度及其影响因素分析

高时空分辨率NDVI时序数据作为遥感应用中的重要数据源,对土地覆被动态变化监测具有重要意义,特别是在地表高程变化显著、气候条件复杂、景观异质性强烈的热带山区。虽然当前学者们提出了诸多时空数据融合模型,但针对这些模型在热带山区的NDVI数据融合精度及其影响因素分析尚不多见。对此,本文选取3类时空数据融合方法(权重函数法、概率统计法和多种混合法)中具有代表性的4个模型:STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)、RASTFM(Spatial and Temporal Adaptive Reflectance Fusion Model)、ESTARFM(Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model)、BSFM(Bayesian Spatiotemporal Fusion Model)(STARFM、ESTARFM为权重函数法;BSFM为概率统计法;RASTFM为多种混合法),选择位于我国热带山区的纳板河流域作为研究区。对融合模型的...     

居民出行与轨迹行为交互模式挖掘与关联技术

出租车是居民出行的重要交通工具,其轨迹数据蕴含着丰富的居民出行信息.原始出租车轨迹数据因缺少语义信息无法直观反映居民出行规律.通过轨迹数据挖掘技术处理之后的出租车轨迹数据能够反映居民活动规律和行为模式,从而为城市规划决策提供参考依据.本文重点研究了基于语义的交互模式度量,通过出租车停留点推断其语义信息;然后根据语义信息...     

顾及地理-语义动态的城市热点预测框架

城市热点时空预测是城市管理和智慧城市建设的一项长期而富有挑战性的任务。准确地进行城市热点时空预测可以提高城市规划、调度和安全保障能力并降低资源消耗。现有的区域级深度时空预测方法主要利用基于地理网格的图像、给定的网络结构或额外的数据来获取时空动态。通过从原始数据中挖掘出潜在的自语义信息,并将其与基于地理空间的网格图像融合,也可以提高时空预测的性能,基于此,本文提出了一种新的深度学习方法地理语义集成神经网络(GSEN),将地理预测神经网络和语义预测神经网络相叠加。GSEN模型综合了预测递归神经网络(PredRNN)、图卷积预测递归神经网络(GCPredRNN)和集成层的结构,从不同的角度捕捉时空动态。并且该模型还可以与现实世界中一些潜在的高层动态进行关联,而不需要任何额外的数据。最终在3个不同领域的实际数据集上对本文提出的模型进行了评估,均取得了很好的预测效果,实验结果表明GSEN模型在不同城市热点时空预测任务中的推广性和有效性,利用该模型可以更好地进行城市热点时空预测,解决一系列如犯罪、火灾、网约车预订等等现代城市发展中亟需解决的相关问题。