Fault kinematics and surface deformation across a releasing bend during the 2010 MW 7.1 Darfield, New Zealand, earthquake revealed by differential LiDAR and cadastral surveying

Duffy, Brendan; Quigley, Mark; Barrell, David; Dissen, R. J. Van; Stahl, Timothy; Leprince, S.; McInnes, Craig; Bilderback, Eric L.

doi:10.1130/b30753.1

Cited by 55 publications

(63 citation statements)

References 47 publications

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“…The data were collected a few days after the earthquake and were used along with a variety of other data to measure the surface displacement resulting from the earthquake and to interpret the kinematics of the Greendale Fault (Duffy et al, 2012).…”

Section: Case Studiesmentioning

confidence: 99%

Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data

et al. 2017

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Abstract. The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost functions to rapidly interpolate structural features between manually defined control points in point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3-D) and two-dimensional (2-D) datasets, including high-resolution aerial imagery, digital outcrop models, digital elevation models (DEMs) and geophysical grids.We demonstrate the algorithm with four diverse applications in which we extract (1) joint and contact patterns in high-resolution orthophotographs, (2) fracture patterns in a dense 3-D point cloud, (3) earthquake surface ruptures of the Greendale Fault associated with the M w 7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (lidar) data, and (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the consistency of the interpretation process while retaining expert guidance and achieves significant improvements (35-65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.

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Section: Case Studiesmentioning

confidence: 99%

Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data

et al. 2017

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“…Multi-source, multi-period DTM data are not only a critical tool for research on the mechanism of landslides, but also considered as greatly useful information regarding active faults, earthquake disasters [1][2][3][4], and flooding/river bank erosion [5]. When it comes to the comparison of this type of terrain information, error estimation of data obtained in various periods using various techniques becomes crucial.…”

Section: Introductionmentioning

confidence: 99%

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

Hsieh

Chan

2016

Remote Sensing

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In this study, six different periods of digital terrain model (DTM) data obtained from various flight vehicles by using the techniques of aerial photogrammetry, airborne LiDAR (ALS), and unmanned aerial vehicles (UAV) were adopted to discuss the errors and applications of these techniques. Error estimation provides critical information for DTM data users. This study conducted error estimation from the perspective of general users for mountain/forest areas with poor traffic accessibility using limited information, including error reports obtained from the data generation process and comparison errors of terrain elevations. Our results suggested that the precision of the DTM data generated in this work using different aircrafts and generation techniques is suitable for landslide analysis. Especially in mountainous and densely vegetated areas, data generated by ALS can be used as a benchmark to solve the problem of insufficient control points. Based on DEM differencing of multiple periods, this study suggests that sediment delivery rate decreased each year and was affected by heavy rainfall during each period for the Meiyuan Shan landslide area. Multi-period aerial photogrammetry and ALS can be effectively applied after the landslide disaster for monitoring the terrain changes of the downstream river channel and their potential impacts.

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“…To work with LiDAR point clouds directly, Nissen et al [46] introduced a new method for calculating 3-D coseismic surface displacements from preand post-earthquake LiDAR data based on the Iterative Closest Point (ICP) algorithm [52,53]. The method was also used to extract threedimensional displacements and rotations from pre-and postearthquake LiDAR data for the 2008 Iwate-Miyagi earthquake and the 2011 Fukushima-Hamadori earthquake in Japan [49]. One limitation of the method by Nissen et al [46,49] is that it is based on the assumption that all LiDAR points in the compared point clouds have uniform accuracy, whereas the error for each LiDAR point depends on variable factors such as the range from system to target and the incidence angle of laser beam.…”

Section: Surface Deformation Revealed By Differential Lidarmentioning

confidence: 99%

“…The method was also used to extract threedimensional displacements and rotations from pre-and postearthquake LiDAR data for the 2008 Iwate-Miyagi earthquake and the 2011 Fukushima-Hamadori earthquake in Japan [49]. One limitation of the method by Nissen et al [46,49] is that it is based on the assumption that all LiDAR points in the compared point clouds have uniform accuracy, whereas the error for each LiDAR point depends on variable factors such as the range from system to target and the incidence angle of laser beam. To take into account the random errors in differential LiDAR point clouds, Zhang et al [47] developed an error propagation method to generate an estimated random error for each LiDAR point, and obtain 3D displacements between two LiDAR point clouds using an anisotropic weighted ICP algorithm.…”

Section: Surface Deformation Revealed By Differential Lidarmentioning

confidence: 99%

“…In tectonically active areas with multi-temporal LiDAR data coverage, it is possible to use differential LiDAR data for detecting changes in geomorphic markers caused by earthquakes. So far, pre-and postearthquake LiDAR data are available only in deformation zones caused by the following four earthquakes: the 2010 M w 7.2 El Mayor-Cucapah earthquake in Mexico [44][45][46][47], the 2010 M w 7.1 Darfield earthquake in New Zealand [48], the 2008 M w 6.9 Iwate-Miyagi earthquake in Japan [49], and the 2011 M w 7.1 Fukushima-Hamadori earthquake in Japan [49].…”

Section: Surface Deformation Revealed By Differential Lidarmentioning

confidence: 99%

See 1 more Smart Citation

LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic Geomorphology

Dong¹

2015

J Geophys Remote Sens

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Fault kinematics and surface deformation across a releasing bend during the 2010 MW 7.1 Darfield, New Zealand, earthquake revealed by differential LiDAR and cadastral surveying

Cited by 55 publications

References 47 publications

Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data

Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

LiDAR Data for Characterizing Linear and Planar Geomorphic Markers in Tectonic Geomorphology

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