Abstract:

Digital topographic analysis, an important means in the research of active tectonics and tectonic geomorphology, has increasingly become one of the principal tools in the identification of active tectonic features and understanding of the development of the earth’s surface process. Indoor interpretation of surface fault trace plays a key role in the digital topographic analysis as it can provide the foundation for setting priorities and defining strategies in the subsequent field investigation. At present, the extraction of fault traces is often realized by assisting the traditional visual interpretation through the image enhancement method. The relevant subjective assessments lead to the amount of work and usually cause different results due to the differences in the interpretation experience of actual operators. At the same time, the field of quantitative research on geomorphic parameters is evolving very rapidly with the advances in the popularity of high-resolution digital topographic data. Therefore, intelligent and automatic extraction of surface fault traces has gradually become a promising research direction. The methods based on machine learning often rely heavily on the good programming foundation of the operator, which is a visible technical barrier. We present a semi-automated method using an ArcGIS toolbox with a set of tools to extract surface fault traces based on geomorphic constraints. The Hutubi and Dushanzi faults are two typical thrust faults located on the northern piedmont of the Tianshan Mountains and are chosen as examples. Excellent exposure of the surface fault traces in these two regions permits detailed mapping of fault traces and deriving shape factors of faults with high-resolution DEMs(digital elevation models). Additionally, they are two of the most-studied thrust faults in this area. Large-scale geological and geomorphological mappings of them and numerous achievements have been published. This creates possibilities for us to conduct comparison analysis on different major methods. Based on typical morphology characteristics of fault scarps, appropriate geomorphic parameters are selected. In practice, reverse fault scarps are distinctly defined into forward and backward ones according to whether their dip is the same as that of the neighboring geomorphic surfaces. Based on two sets of geomorphic constraints,two approaches are then illustrated, including slope calculation, gully extraction, data density analysis and process modeling. Through a detailed comparison of the final extraction results and previous visual interpretations of remote sensing data and field geomorphic investigations, the validity of the method proposed in this study is proven. This method provides a set of tools with user-friendly interfaces to realize step-by-step interpretation and emphasizes the importance of field-based geomorphic constraints at the same time. Moreover, many subtle fault traces which have not been recognized before are simultaneously revealed in the Dushanzi research area. The high-resolution DEMs guarantee the realization of picking out finer bits of fault information. Compared to traditional ways of working, the method has the advantage of automatically delineating reverse fault traces on the earth’s surface. This advantage can significantly reduce the efforts to manually digitize geomorphic features and improve efficiency. But many basic manual adjustment options for recognizing target characteristics also need to be set in extraction, because the distinguishing criterion of fault scarp and surrounding geomorphic landforms vary among different areas. In different specific circumstances, users can manually adjust relevant parameters for the extraction during the modeling process. Generally speaking, the more detailed constraints, the more confidence in the final delineation of fault traces. Subjective judgments are therefore particularly critical for conducting extraction under complex backgrounds. But improving the degree of automation of the whole process is still an important study direction. Future work is thus recommended to employ machine learning and explore appropriate evaluation methods to search for the optimal solution of intermediate parameters.

Key words: thrust fault, automatic extracting, geomorphic parameter, active tectonics, high-resolution digital topographic data

摘要：

数字地形分析是活动构造和构造地貌研究中的一种重要手段, 目前已被广泛应用于地表过程分析中。随着高精度数字地形数据获取的日益便捷, 精细定量化研究地貌参数已成为一个重要趋势。呼图壁断裂和独山子断裂位于天山山脉北麓, 地表迹线十分典型而显著。在这2个区域内, 前人已经完成了区域大比例尺活动断裂地质地貌填图, 并发表了大量研究成果。因此, 它们是十分理想的探索断层迹线自动化提取方法的2个区域。在实际提取过程中, 根据逆断层陡坎的倾向是否与其所在地貌面坡向一致, 文中分别定义了正向和反向逆断层陡坎。基于对这2种不同断层陡坎形态的分析, 利用ArcGIS软件平台并选择恰当的地貌参数实现了对断层地表迹线的提取。通过坡度计算、冲沟提取、数据密度分析和流程建模等步骤, 建立了2套智能化提取流程。最终提取结果与以往的地质地貌填图和遥感数据目视解译结果基本一致。除此之外, 独山子研究区的提取结果还揭露了未曾被识别的反向断层陡坎迹线。这不仅说明文中提出的方法具有很好的适用性, 同时也能够提取十分细小的逆断层地表迹线。与传统方法相比, 这种人机交互式的半自动化方法大大提高了工作效率。但是, 如何真正实现任意地质构造背景中逆断层地表迹线的完全自动化提取, 仍然是未来一个重要的研究方向。

关键词: 逆断层, 智能化提取, 地貌参数, 活动构造, 高精度数字地形数据