Project Info
Assessing the Value of Remote Sensing Data for Rock Fall Hazard Prediction
Whitney Trainor-Guitton | wtrainor@mines.edu and Gabriel Walton | gwalton@mines.edu
Rock fall hazards pose a multimillion dollar challenge for the Colorado Department of Transportation (CDOT). Remote-sensing data (e.g. Lidar, photogrammetry) has the potential to aid in the prediction thus prevention of these hazards. The student will perform statistical analyses to determine the best types of data to prevent/ predict rock fall. This work has the potential to help define methodology of prioritization of resources for hazard mitigation for CDOT.
Elucidate the interdisciplinary nature of the project
The project will expose the student to both statistical tools need to assess the value of information, the fundamentals of rock fall and the fundamentals of remote sensing data. Specifically, the two mentors are from Geophysics (Trainor-Guitton) and Geological Engineering (Walton).
More Information
The project will expose the student to both statistical tools need to assess the value of information, the fundamentals of rock fall and the fundamentals of remote sensing data. Specifically, the two mentors are from Geophysics (Trainor-Guitton) and Geological Engineering (Walton).
Grand Engineering Challenge: Not applicable
Student Preparation
Qualifications
The following are a plus but not obligatory:
Knowledge of basic or Bayesian statistics
Experience manipulating spatial/temporal data
Experience working with point cloud data
Time Commitment
20-40 hours/month
Skills/Techniques Gained
Computer programming, Bayesian statistics, understanding of value of information methodology, fundamentals of remote sensing & rock falls hazards.
Mentoring Plan
Weekly or bi-weekly meetings to address students questions, provide guidance and monitor progress. We can also team up the student with graduate students working with similar data.