TY - JOUR
T1 - Towards Automated/Semiautomated Extraction of Faults from Lidar Data
AU - Pope, Paul A.
AU - Crawford, Brandon M.
AU - Lavadie-Bulnes, Anita F.
AU - Schultz-Fellenz, Emily S.
AU - Milazzo, Damien M.
AU - Solander, Kurt C.
AU - Talsma, Carl J.
N1 - Publisher Copyright:
© 2022 American Society for Photogrammetry and Remote Sensing.
PY - 2022/6
Y1 - 2022/6
N2 - The Pajarito fault system is a complex zone of deformation and a seismically active region nestled within the Rio Grande rift in north-central New Mexico. Numerous laterally discontinuous faults and associated folds and fractures interact in a manner that has important implications for seismic hazards and risk mitigation. Previous efforts have established a foundation for the location of lineaments and structures in the Pajarito fault system; however, ensuring the completeness of the current lineament mapping is required for identifying areas for field validation, evaluating the potential for future seismic activity, and better understanding fault interaction. Assistance with this fault-mapping task via automated or semiautomated techniques as applied to lidar data over a large area of interest is highly desirable. A proof-of-concept processing flow which transforms lidar point-cloud data into a raster of surfi-cial fault candidates is described and illustrated herein. These initial results hold great promise toward achieving our ultimate goal.
AB - The Pajarito fault system is a complex zone of deformation and a seismically active region nestled within the Rio Grande rift in north-central New Mexico. Numerous laterally discontinuous faults and associated folds and fractures interact in a manner that has important implications for seismic hazards and risk mitigation. Previous efforts have established a foundation for the location of lineaments and structures in the Pajarito fault system; however, ensuring the completeness of the current lineament mapping is required for identifying areas for field validation, evaluating the potential for future seismic activity, and better understanding fault interaction. Assistance with this fault-mapping task via automated or semiautomated techniques as applied to lidar data over a large area of interest is highly desirable. A proof-of-concept processing flow which transforms lidar point-cloud data into a raster of surfi-cial fault candidates is described and illustrated herein. These initial results hold great promise toward achieving our ultimate goal.
UR - http://www.scopus.com/inward/record.url?scp=85147328275&partnerID=8YFLogxK
U2 - 10.14358/PERS.21-00058R2
DO - 10.14358/PERS.21-00058R2
M3 - Article
AN - SCOPUS:85147328275
SN - 0099-1112
VL - 88
SP - 391
EP - 397
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
IS - 6
ER -