Estimated Distributed Near-Field Surface Displacements Using Nascent Mobile Laser Scanning

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Modeling fault dynamics requires dense observations of surface displacements over time. However, it remains challenging to observe distributed fault displacements due to non-linear deformation. Observation of near-field displacements challenges the level of detection limits of modern geosensing measurements because the rate of the displacements can be as small as several mm per year with variations only within several hundreds of meters from a fault trace, i.e. in the near field. To fill this void, we introduce a mobile laser scanning (MLS)-based change detection framework that is capable of detecting distributed fault displacements in the near field with high resolution and accuracy. The approach leverages MLS’s redundant point cloud representation of an object’s location and models the corresponding point clouds as geometric primitives for change detection. Corresponding point clouds are extracted using PointNet, a deep neural network, and a customized random sample consensus estimator. A combined least squares adjustment is developed for primitive modeling and change detection for both bi- and multi-temporal lidar time series, and the multi-temporal analysis introduces additional temporal constraints for further accuracy improvement. Using data collected after the Mw 6.0 2014 South Napa earthquake, our results reveal centimeter-level horizontal ground deformation, the post-seismic displacement field is detected by tracking displacements of vineyard posts modeled as cylindrical primitives from which patterns of off-fault deformation are identified and show agreement at cm level with collocated alinement array observations. Using MLS data collected in 2015, 2017 and 2018 on a segment of the Hayward fault, bi- and multi-temporal fault creep displacements are detected by leveraging abundant planar primitives in the built environment. The change detection results give time series of distributed fault creep displacement and the detected off-fault displacement profile matches in situ theodolite and creepmeter observations at the subcentimeter level. The proposed framework is shown to be accurate and practical for fault displacement detection in the near field and provides geodetic observations of non-linear displacement patterns at an unprecedented scale, and the results can be used to elucidate more sophisticated models of fault dynamics.

Mobile laser scanning, Fault displacement detection, Geometric primitive, RANSAC, Least squares adjustment, Lidar time series
Portions of this document appear in: Zhu, Xinxiang, Craig L. Glennie, and Benjamin A. Brooks. "Automated near-field deformation detection from mobile laser scanning for the 2014 Mw 6.0 South Napa earthquake." Journal of Applied Geodesy 16, no. 1 (2022): 65-79; and in: Zhu, Xinxiang, Craig L. Glennie, Benjamin A. Brooks, and Todd L. Ericksen. "Monitoring aseismic fault creep using persistent urban geodetic markers generated from mobile laser scanning." ISPRS Open Journal of Photogrammetry and Remote Sensing 2 (2021): 100009.