Towards Automated/Semiautomated Extraction of Faults from Lidar Data

Paul A. Pope, Brandon M. Crawford, Anita F. Lavadie-Bulnes, Emily S. Schultz-Fellenz, Damien M. Milazzo, Kurt C. Solander, Carl J. Talsma

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)391-397
Number of pages7
JournalPhotogrammetric Engineering and Remote Sensing
Volume88
Issue number6
DOIs
StatePublished - Jun 2022
Externally publishedYes

Fingerprint

Dive into the research topics of 'Towards Automated/Semiautomated Extraction of Faults from Lidar Data'. Together they form a unique fingerprint.

Cite this