Precise Registration of Laser Mapping Data by Planar Feature Extraction for Deformation Monitoring

Quantifying near-field displacements can enable an understanding of earthquake dynamics. To date, established remote sensing techniques have failed to recover sub-cm level near-field displacements at the scale and resolution required for shallow fault physical investigations. Mobile Laser Scanning (MLS) and Airborne Laser Scanning (ALS) can be used to provide rapid assessment of coseismic and aseismic near-field deformation. This dissertation develops methods to rapidly extract planar primitives using fast parallel approaches and employs an alternative registration approach between MLS and ALS datasets by automated planar matching. The algorithms are applied to laser mapping datasets along the Hayward and Napa faults, where rapid mapping of coseismic and aseismic deformation can provide valuable information to the scientific community.

The proposed methodology relies on partitioning the point cloud using an octree data structure and extracting planar regions from octree nodes. A region growing procedure generates the final planar features. The features extracted from two temporally spaced point clouds are then used to calculate rigid-body transformation parameters. The selection of appropriate planar feature extraction and feature matching criteria is used to produce robust and accurate deformation mapping results. Rigorously propagated point accuracy estimates are employed to produce realistic estimated errors for the transformation parameters.

Displacements of each aggregate study area are computed separately from left and right sides of the fault and compared to be within 3 mm of seismographic instrument (alinement array) displacements. The least squares residuals show distinct patterns prompting computation of differential displacements compared with alinement arrays.

The findings demonstrate the ability of the algorithms to accurately extract near-field deformations from repeated MLS or ALS scans of earthquake-prone urban areas. ALS is also used in conjunction with the MLS datasets, illustrating the algorithm's ability to accommodate different LiDAR collection modalities at sub-cm level accuracy. The automated planar extraction and registration is an important contribution to the study of near-field earthquake dynamics and can be used as a basis for future geological inversion models.