Applications of large-scale and high-resolution topographic data in tectonic geomorphology
Abstract/Contents
- Abstract
- Many discoveries in geomorphology have been driven by new platforms for acquiring elevation data and new methods for measuring landscape characteristics and change in these datasets over the past two decades. This dissertation addresses three problems: detecting and measuring attributes of tectonic landforms in elevation data at scale, filtering point clouds to improve representations of the ground surface, and detecting change due to near-surface deformation in high-resolution datasets. It uses two sources of "big data" common in tectonic geomorphology: regional-scale, airborne light detection and ranging (lidar) data and small footprint, ultra-high density photogrammetric data. Geomorphic indicators of faulting, such as fault scarps and topographic lineaments, provide important evidence of fault zone extent, maturity, and activity beyond the observational timescales of instrumental or historical data. In Chapter 1, a template matching method to detect tectonic landforms including earthquake fault scarps is developed and applied at scale to a regional lidar dataset imaging over 2500 square kilometers of the northern San Andreas fault system. The method is evaluated against field-based morphologic dating and improvements to semi-automated fault mapping methods are discussed. Chapter 2 focuses on classification of ground and vegetation points, an important prerequisite to mapping landforms or measuring topographic change. Color-based classification and a multi-scale curvature method using relative height are combined to efficiently classify point clouds, exploiting the color attributes of these data. The new method captures rills, small channels, and tree fall in a photogrammetric dataset that are not visible in lidar data and produces consistent elevation models and topographic derivatives from each data source. Change detection applied to pre- and post-event elevation data has recently enabled inversions of shallow slip in large, surface-rupturing earthquakes. Chapter 3 systematically explores how subtle displacements due to near-surface deformation might be resolved by applying these techniques to photogrammetric data. A series of synthetic experiments is performed by deforming photogrammetric point clouds according to an elastic dislocation model and measuring the resulting displacements. The results indicate a minimum of 0.5 m of dip slip (1-2 m strike slip) is resolved at 100 m depth, consistent with the baseline variability of these data. They also document a survey-specific horizontal bias at large window sizes that provides important information for future applications, especially imaging of strike-slip earthquakes or propagating sills. Potential improvements to the level of detection using a higher frequency time series of displacements are discussed
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Sare, Robert Martin |
|---|---|
| Degree supervisor | Hilley, George E |
| Thesis advisor | Hilley, George E |
| Thesis advisor | DeLong, Stephen B |
| Thesis advisor | Lapôtre, Mathieu |
| Degree committee member | DeLong, Stephen B |
| Degree committee member | Lapôtre, Mathieu |
| Associated with | Stanford University, Department of Geological Sciences |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Robert M. Sare |
|---|---|
| Note | Submitted to the Department of Geological Sciences |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Robert Martin Sare
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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