UPDATING THE STRUCTURAL GEOLOGISTS TOOLKIT: A NOVEL APPROACH TO STRIKE AND DIP ACQUISITION FOR REMOTE TERRAIN TO AID IN STRUCTURAL ANALYSIS

Geologic maps are keystone datasets in aiding structural characterization in fold-and-thrust belts and other tectonic regimes. The structural style(s) recorded in such maps has widespread implications for hazard assessment, trap analysis, and understanding the processes of mountain building.

Over the past decade, we have seen remote datasets revolutionize the field of geologic mapping. One area where we have yet to fully leverage the richness of these datasets is field-based structural geology, especially in the method of collecting bedding attitude or strike and dip data. Since Bailey Willis’ seminal 1902 paper, geologists have tackled the problem of remotely acquiring strike and dip measurements using the three-point problem (TPP). The TPP is at the heart of countless studies and has proved to be highly capable. However, they are typically performed manually and limited to user-defined locations, and thus do not take full advantage of the richness of remote sensing datasets.

We have developed a numerical method to automate strike and dip calculations from shapefiles and DEMs with the same degree of precision as standard TPP methods. Our method is far more efficient, typically yielding ~3,500 strike and dip measurements for an 800,000 km² section in a matter of minutes. We optimize the selection of three points by using linear least squares to find a plane of best fit and iteratively splitting planes based on the sum of squared errors. We find that the strike a dip of this plane is accurate – consistently within a half degree.

We examine several sites to test and validate our method with field data, including regions with flat-lying stratigraphy, near-vertical stratigraphy, and a fold and thrust belt. In each case, our method provides a quantitative estimate of fit for each and illustrates that we extract regional scale strike and dip measurements by comparing the measurements to field data. Moreover, we highlight regions where field and remotely acquired data do not correlate (i.e., where features are smaller than the data resolution, or limited topographic expression of a geologic feature). Such regions allow one to more quantitively and rigorously target certain zones for field expeditions. Overall, our method provides fast and reliable bedding attitude data that can be used to support structural analysis.