地震地质 ›› 2019, Vol. 41 ›› Issue (2): 363-376.DOI: 10.3969/j.issn.0253-4967.2019.02.007

• 研究论文 • 上一篇    下一篇

利用R语言半自动化提取河流阶地——以米家山黄河阶地为例

姚文倩1, 刘静1, Michael Oskin2, 韩龙飞1, 李雪3, 1, 徐心悦1, 李占飞1, 张金玉1   

  1. 1. 中国地震局地质研究所, 地震动力学国家重点实验室, 北京 100029;
    2. 加州大学戴维斯分校, 地球与行星科学学院, 美国, 戴维斯 95616;
    3. 中国地震局地震研究所, 中国地震局地震大地测量重点实验室, 武汉 430071
  • 收稿日期:2019-01-02 修回日期:2019-02-20 出版日期:2019-04-20 发布日期:2019-05-21
  • 作者简介:姚文倩,女,1985年生,2012年于中国石油大学(北京)地球科学学院获理学硕士学位,现为中国地震局地质研究所构造地质学专业在读博士研究生,主要从事活动构造和构造地貌方面的研究,电话:15001228611,E-mail:wenqian_08@163.com。
  • 基金资助:
    地震动力学国家重点实验室课题(LED2017A01)、国家自然科学基金(41761144065)和中国地震局川滇国家地震监测预报实验场项目(2017CESE0102)共同资助

APPLICATION OF SEMIAUTOMATIC EXTRACTION OF FLUVIAL TERRACES BASED ON R LANGUAGE-AN EXAMPLE FROM THE YELLOW RIVER TERRACES AT MIJIA SHAN

YAO Wen-qian1, LIU-ZENG Jing1, Michael Oskin2, HAN Long-fei1, LI Xue3, WANG Heng1, XU Xin-yue1, LI Zhan-fei1, ZHANG Jin-yu1   

  1. 1. State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China;
    2. University of California Davis, Department of Earth and Planetary Sciences, Davis, CA 95616, USA;
    3. Institute of Seismology, China Earthquake Administration, Key Laboratory of Seismic Geodesy, China Earthquake Administration, Wuhan 430071, China
  • Received:2019-01-02 Revised:2019-02-20 Online:2019-04-20 Published:2019-05-21

摘要: 构造活跃地区阶地的发育对于分析不同时间域下的构造变形或气候作用具有重要意义。因此,如何利用有效的定量方法提取和精细刻画这类地貌特征显得极为重要。R语言是一种集统计分析和图形显示于一体的优秀编程语言,目前已被广泛应用于医学、生物学等领域,但尚未应用于地质与地貌学领域。文中以海原断裂带景泰-哈思山段的米家山东侧保存较好的多级黄河河流阶地为研究目标,初步尝试基于R语言对SfM技术获取的高精度地形数据进行分析和可视化,完成了对米家山黄河阶地的半自动提取,共划分出20级河流阶地,同时揭示出较年轻的阶地具有较好的连续性和延伸性,而较老阶地的连续性和延伸性则相对较差,且老阶地变形逐渐趋于明显,阶地年龄越老,其似半抛物线形态的翘曲越明显,反映了米家山东侧多级阶地形成后的不同演化历史。此次试验结果表明R语言有望成为高精度地形数据分析和可视化的有效工具。

关键词: R语言, SfM, 高精度地形数据, 米家山, 黄河阶地

Abstract: The generation, abandonment and preservation of terraces formed in active tectonic areas are important to the analysis of the role of the tectonics and climate along the temporal variations, so it appears significant as how to use the effective quantitative methods to extract and accurately depict these terraces. The increasingly convenient acquisition of high-precision topographic data has greatly promoted the advancement of quantitative research in geoscience, making it possible to analyze mid-micro-geomorphic features on a large scale, especially by studying the temporal and spatial evolution of tectonic deformation through accurate capture of micro-geomorphic features. Over the past decade, the rapid development of LiDAR(Light Detection and Ranging)technology has provided unprecedented opportunity to access high-precision topographic data(up to centimeter in vertical and horizontal directions). However, its relatively high cost and relatively complex data processing techniques limit its widespread application in the field of earth sciences. In recent years, with the continuous innovation and advancement of topographic measurement technology, the three-dimensional structure of motion reconstruction technology(Structure from Motion, SfM)has gradually been introduced into the field of digital topographic photogrammetry due to its rapid advantage in providing quick, convenient and cost-effective methods for obtaining high-density geospatial point data. This method thus shows great potential for providing high resolution topographic data with comparable resolution and precision. Therefore, with the acquisition of more and more high-resolution terrain data in recent years, it is an important development trend to explore automated or semi-automated quantitative geomorphological analysis methods. R language, as an excellent programming language, has not been used in the geology and geomorphology, although is widely applied in medicine and meteorology based on its powerful capability of statistician and graphic visualization. In this paper, we focus on the Yellow River multi-terraces formed to the east of the Mijia Shan, which belongs to the Jingtai-Hasi Shan segment of the Haiyuan Fault. With the analysis and visualization of the high-resolution topographic data collected from the SfM in the environment of the R language, we implement the semiautomatic classification and mapping of the Yellow River multi-terraces. The method identifies 20 terraces with different elevation. Our results also imply that the younger terraces have better continuity and elongation, and the older terraces have more deformation, which can be demonstrated from their gradually notable semi-parabolic shape. Besides this, it also suggests the diverse evolution stages of the Yellow River terraces. Our study indicates that R language is expected to become an efficient tool of statistics and visualization of the high-resolution topographic data.

Key words: R language, SfM, high-resolution topographic data, Mijia Shan, Yellow River multi-terraces

中图分类号: