基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验

doi:10.11947/j.AGCS.2016.F005

测绘学报 ›› 2016, Vol. 45 ›› Issue (S1): 40-47.doi: 10.11947/j.AGCS.2016.F005

基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验

余接情, 石珍, 吴立新, 邢松伟, 贾永基

中国矿业大学环境与测绘学院, 江苏徐州 221116

收稿日期:2016-08-20 修回日期:2016-10-20 出版日期:2016-12-31 发布日期:2017-03-29
作者简介:余接情(1984-),男,博士,副教授,主要从事全球格网有关研究。E-mail:yujieqing@cumt.edu.cn
基金资助:
国家自然科学基金青年项目（41301432）；中央高校基本科研业务费专项资金（2013QNB10）；江苏高校优势学科建设工程

The Framework of Earth System Cellular Automata Based on SDOG-ESSG and Its Preliminary Experiment

YU Jieqing, SHI Zhen, WU Lixin, XING Songwei, JIA Yongji

School of Environment Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

Received:2016-08-20 Revised:2016-10-20 Online:2016-12-31 Published:2017-03-29
Supported by:
National Natural Science Foundation of China(No.41301432);Fundamental Research Funds for the Central Universities(No.2013QNB10);Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要： 元胞自动机结构简单且具备模拟复杂系统的能力，已被广泛应用于大气、流体力学、地球物理等领域。然而，现有元胞自动机以欧氏空间为约束进行地球系统过程模拟，忽略了地球重力等天然约束，导致计算过程中元胞状态的传递方向与真实运动的趋势方向不相符，一定程度上扭曲了最终的模拟结果。本文提出了地球系统元胞自动机这一概念，并从元胞表达及构建、邻居模型等方面设计了基于SDOG-ESSG格网的地球系统元胞自动机框架。由于演化规则取决于不同的应用，因此本文进一步以地壳的热传递为例，通过对热力学方程离散化设计了相应的演化规则。最后，借助公开的数据源开展了地壳热传递元胞自动机模拟的初步试验，并与一定区域下的数值模拟结果展开了比对。试验表明，与数值模拟方法相比，本文方法的模拟结果相对误差控制在27%以内，具备一定程度的可行性，可作为地球系统过程模拟的一种新思路。

关键词: 元胞自动机, 热传导, 模拟, SDOG-ESSG, 全球网格

Abstract: Cellular Automata (CA) has been widely adopted in atmosphere, hydrodynamics, geophysics and geoscience for its simple structure and capability of simulating complex system. Existing CAs in geoscience ignore the restraints of gravity, and do earth system processes simulation under the constraints of Euclidean, which is not in accord with the tendency of state delivery from one cell to cell, and will produce some distortions on the results. This paper is to propose an Earth System Cellular Automata (ESCA) based on the restraint of gravity. The framework, including cell representation and construction, and neighbor model based on SDOG-ESSG, was proposed in the paper. As evolution rules are highly depended on actual problems, the problem of heat conduction in the Crust was taken as an example to design an evolution rule for the ESCA by imposing a discretization on thermodynamics law. Finally, a preliminary experiment and a comparison with numerical simulation in local scale were carried out. Results showed that, ESCA is reliable to some extent with its relative error limited to 27% comparing with numerical simulation. ESCA can be a new way of simulation for earth system process.

Key words: cellular automata, heat conduction, simulation, SDOG-ESSG, global grid

中图分类号:

P208

余接情, 石珍, 吴立新, 邢松伟, 贾永基. 基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验[J]. 测绘学报, 2016, 45(S1): 40-47.

YU Jieqing, SHI Zhen, WU Lixin, XING Songwei, JIA Yongji. The Framework of Earth System Cellular Automata Based on SDOG-ESSG and Its Preliminary Experiment[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(S1): 40-47.

参考文献

[1] 陈述彭. 地球系统科学[M]. 合肥:中国科技大学出版社, 1998. CHEN Shupeng. Earth System Science[M]. Hefei:Press of University of Science and Technology of China, 1998.
[2] HOEKSTRA A G, KROC J, SLOOT P M A. Simulating Complex Systems by Cellular Automata[M]. Berlin Heidelberg:Springer-Verlag, 2010.
[3] BENGTSSON L, STEINHEIMER M, BECHTOLD P, et al. A Stochastic Parametrization for Deep Convection Using Cellular Automata[J]. Quarterly Journal of the Royal Meteorological Society, 2013, 139(675):1533-1543.
[4] TSUNEMATSU K, FALCONE J L, BONADONNA C, et al. Applying a Cellular Automata Method for the Study of Transport and Deposition of Volcanic Particles[M]//UMEO H, MORISHITA S, NISHINARI K, et al. Cellular Automata. Berlin Heidelberg:Springer, 2008:393-400.
[5] MARÍN M, RAUCH V, ROJAS-MOLINA A, et al. Cellular Automata Simulation of Dispersion of Pollutants[J]. Computational Materials Science, 2000, 18(2):132-140.
[6] PERIDIER V J. Estimating Transient Surface Heating Using A Cellular Automaton Energy-transport Model[J]. Complex Systems, 2005, 16(2):139-153.
[7] GEORGOUDAS I G, SIRAKOULIS G C, SCORDILIS E M, et al. A Cellular Automaton Simulation Tool for Modelling Seismicity in the Region of Xanthi[J]. Environmental Modelling & Software, 2007, 22(10):1455-1464.
[8] LEAMY M J. Application of Cellular Automata Modeling to Seismic Elastodynamics[J]. International Journal of Solids and Structures, 2008, 45(17):4835-4849.
[9] DEL NEGRO C, FORTUNA L, HERAULT A, et al. Simulations of the 2004 Lava Flow at Etna Volcano Using the Magflow Cellular Automata Model[J]. Bulletin of Volcanology, 2008, 70(7):805-812.
[10] LIU Xiaoping, LI Xia, LIU Lin, et al. A Bottom-Up Approach to Discover Transition Rules of Cellular Automata Using Ant Intelligence[J]. International Journal of Geographical Information Science, 2008, 22(11-12):1247-1269.
[11] ABURAS M M, HO Y M, RAMLI M F, et al. The Simulation and Prediction of Spatio-Temporal Urban Growth Trends Using Cellular Automata Models:A Review[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 52:380-389.
[12] LI Qilang, WONG S C, MI Jie. A Cellular Automata Traffic Flow Model Considering the Heterogeneity of Acceleration and Delay Probability[J]. Physica A:Statistical Mechanics and its Applications, 2016, 456:128-134.
[13] 余接情. 基于SDOG的地球系统空间格网及其三维建模应用[D]. 北京:北京师范大学, 2012. YU Jieqing. SDOG-Based Earth System Spatial Grid and Its Application on 3D Modeling[D]. Beijing:Beijing Normal University, 2012.
[14] 何金海, 郭品文, 银燕, 等. 大气科学概论[M]. 北京:气象出版社, 2012. HE Jinhai, GUO Pinwen, YIN Yan, et al. Introduction to Atmospheric Science[M]. Beijing:China Meteorological Press, 2012.
[15] 伍光和, 蔡运龙. 综合自然地理学[M]. 2版. 北京:高等教育出版社, 2004. WU Guanghe, CAI Yunlong. Comprehensive Physical Geography[M]. 2nd ed. Beijing:Higher Education Press, 2004.
[16] 余接情, 吴立新, 訾国杰, 等. 基于SDOG的岩石圈多尺度三维建模与可视化方法[J]. 中国科学:地球科学, 2012, 42(5):755-763. YU Jieqing, WU Lixin, ZI Guojie, et al. SDOG-Based Multi-Scale 3D Modeling and Visualization on Global Lithosphere[J]. Science China Earth Sciences, 2012, 55(6):1012-1020.
[17] YU Jieqing, WU Lixin, LI Zhifeng, et al. An SDOG-Based Intrinsic Method for Three-Dimensional Modelling of Large-Scale Spatial Objects[J]. Annals of GIS, 2012, 18(4):267-278.
[18] 李志锋, 吴立新, 薄海光, 等. 基于VisIt的全球科学数据并行可视化——以大气温度场为例[J]. 地理与地理信息科学, 2012, 28(1):24-28. LI Zhifeng, WU Lixin, BO Haiguang, et al. VisIt-Based Parallel Visualization of Global Scientific Data:Atmosphere Temperature Field Being A Case[J]. Geography and Geo-Information Science, 2012, 28(1):24-28.
[19] 余接情, 吴立新, 贾永基, 等. SDOG-ESSG的几何分布规律及面邻近计算[J]. 中国矿业大学学报, 2015, 44(2):359-366. YU Jieqing, WU Lixin, JIA Yongji, et al. The Face-Neighbor Calculation and Geometric Distribution Rules of SDOG-ESSG[J]. Journal of China University of Mining & Technology, 2015, 44(2):359-366.
[20] 滕吉文. 固体地球物理学概论[M]. 北京:地震出版社, 2003. TENG Jiwen. Introduction to Solid Geophysics[M]. Beijing:Seismological Press, 2003.
[21] 胡汉平, 程文龙. 热物理学概论[M]. 2版. 合肥:中国科学技术大学出版社, 2009. HU Hanping, CHENG Wenlong. Introduction to Thermophysics[M]. 2nd ed. Hefei:Press of University of Science and Technology of China, 2009.
[22] WANG Yang. Heat Flow Pattern and Lateral Variations of Lithosphere Strength in China Mainland:Constraints on Active Deformation[J]. Physics of the Earth and Planetary Interiors, 2001, 126(3-4):121-146.
[23] ARTEMIEVA I M, MOONEY W D. Thermal Thickness and Evolution of Precambrian Lithosphere:A Global Study[J]. Journal of Geophysical Research:Atmospheres, 2001, 106(B8):16387-16414.
[24] 史謌. 地球物理学基础[M]. 北京:北京大学出版社, 2002. SHI Ge. Basic of Geophysics[M]. Beijing:Peking University Press, 2002.

基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验

The Framework of Earth System Cellular Automata Based on SDOG-ESSG and Its Preliminary Experiment

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