利用时空立方体挖掘小微地震时空演变模式

doi:10.13474/j.cnki.11-2246.2020.0293

测绘通报 ›› 2020, Vol. 0 ›› Issue (9): 106-109.doi: 10.13474/j.cnki.11-2246.2020.0293

利用时空立方体挖掘小微地震时空演变模式

黄泽纯, 刘子璞, 范心仪, 洪安东

西南交通大学地球科学与环境工程学院, 四川成都 611756

收稿日期:2020-05-26 修回日期:2020-07-11 出版日期:2020-09-25 发布日期:2020-09-28
作者简介:黄泽纯(1974-),男,博士生,副教授,主要研究方向为GIS与时空数据挖掘分析。E-mail:zchuang@yeah.net
基金资助:
中央高校基本科研业务费专项资金（2682014CX017）

Mining the spatiotemporal evolution pattern of small earthquakes using space time cube

HUANG Zechun, LIU Zipu, FAN Xinyi, HONG Andong

Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Received:2020-05-26 Revised:2020-07-11 Online:2020-09-25 Published:2020-09-28

摘要/Abstract

摘要： 挖掘分析小微地震的时空演变模式，可为地震灾害的分析与预报提供辅助决策参考。本文以四川地区的地震监测数据为基础，利用时空立方体融合地震点的空间、时间与属性数据，基于时空热点统计分析方法挖掘小微地震的时空冷热点分布模式。试验结果表明：在试验数据的时域内，四川地区小微地震的热点模式主要表现为连续热点、逐渐减少热点和振荡热点。冷点模式主要是连续冷点，且冷点覆盖范围比热点覆盖范围广。基于时空立方体的时空热点分析方法能够发挥时空统计学的优势，可有效挖掘分析小微地震的时空演变趋势。

关键词: 小微地震, 时空立方体, 空间统计, 时空分布模式, 热点分析

Abstract: Mining and analyzing the spatiotemporal evolution patterns of the small earthquakes can provide auxiliary decision-making support for the analysis and prediction of earthquake disasters. In this paper, based on the seismic monitoring data in Sichuan area, the space time cube was used to fuse the characteristic data of space, time and attributes of earthquake points, and the spatial temporal hot spot statistical analysis methods were utilized to mine the distribution patterns of the spatiotemporal cold spots and hot spots of small earthquakes. The experimental results show that, in the time domain of the experimental data, the hot spots patterns of small earthquakes in Sichuan area are mainly consecutive hot spots, diminishing hot spots and oscillating hot spots, and the cold spots patterns are mainly consecutive cold spots, and the coverage of cold spots is wider than that of hot spots. The spatiotemporal evolution pattern mining method based on space time cube can play the advantage of spatiotemporal statistics, and it is an effective way to mine and analyze the temporal and spatial evolution trend of small earthquakes.

Key words: earthquakes, space time cube, spatial statistics, spatiotemporal pattern, hot spot analysis

中图分类号:

P227

黄泽纯, 刘子璞, 范心仪, 洪安东. 利用时空立方体挖掘小微地震时空演变模式[J]. 测绘通报, 2020, 0(9): 106-109.

HUANG Zechun, LIU Zipu, FAN Xinyi, HONG Andong. Mining the spatiotemporal evolution pattern of small earthquakes using space time cube[J]. Bulletin of Surveying and Mapping, 2020, 0(9): 106-109.

参考文献

[1] 张瑞芳,黄泽纯,洪安东,等. 利用样方分析小微地震空间分布模式[J]. 测绘与空间地理信息, 2018, 41(6):21-24.
[2] 邹进贵,李琴,朱勇超,等. 时序地基SAR子影像集监测地震滑坡灾害研究[J]. 测绘通报, 2015(S1):152-156.
[3] 张瑞芳. 基于时空统计的大地震前后小微地震时空格局演变研究[D]. 成都:西南交通大学, 2018.
[4] 杨涛. 基于时空视角的全球地震灾害分布模式分析[J]. 现代测绘, 2017, 40(1):29-31, 54.
[5] 杨格格,杨艳昭,游珍,等. 中国陆域地震灾害的时空分布格局[J]. 地球科学进展, 2011, 26(5):548-555.
[6] 黄腾,姜岩. 1990年以来四川地区地震时空分布特征[J]. 四川地质学报, 2018, 38(3):474-478.
[7] 王竹华,王大鹏,孙祝友,等. 中国陆域近10年地震时空分布统计特征[J]. 烟台师范学院学报(自然科学版), 2005, 21(4):293-296.
[8] 汤国安. 地理信息系统教程[M]. 2版. 北京:高等教育出版社, 2019.
[9] 胡庆武,王明,李清泉. 利用位置签到数据探索城市热点与商圈[J]. 测绘学报, 2014, 43(3):314-321.
[10] 洪安东. 基于时空立方体的交通拥堵点时空模式挖掘与分析[D]. 成都:西南交通大学, 2017.
[11] Esri. Create space time cube by aggregating points[EB/OL]. (2019-5-22)[2020-5-6]. https://desktop.arcgis.com/en/arcmap/10.7/tools/space-time-pattern-mining-toolbox/create-space-time-cube.htm.
[12] MILLER H J. Tobler's first law and spatial analysis[J]. Annals of the Association of American Geographers, 2004, 94(2):284-289.
[13] ORD J K,GETIS A. Local spatial autocorrelation statistics:distributional issues and an application[J]. Geographical Analysis, 2010, 27(4):286-306.
[14] Esri. How emerging hot spot analysis works[EB/OL]. (2019-5-22)[2020-5-25]. https://desktop.arcgis.com/en/arcmap/10.7/tools/space-time-pattern-mining-toolbox/learnmoreemerging.htm.
[15] SHIMAZAKI H,SHINOMOTO S. A method for selecting the bin size of a time histogram.[J]. Neural Computation, 2007, 19(6):1503-1527.

利用时空立方体挖掘小微地震时空演变模式

Mining the spatiotemporal evolution pattern of small earthquakes using space time cube

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