GSA Connects 2021 in Portland, Oregon

Paper No. 60-4
Presentation Time: 2:30 PM-6:30 PM


STEVENS, Zachary and MATTINSON, Chris G., Geological Sciences, Central Washington University, 400 E University Way, Ellensburg, WA 98926

Geophysical imaging of the sub-surface requires that the various rock types expected to occur at depth must be sampled at the surface and examined to understand their geophysical (density and magnetic) characteristics. The outcrops that we sampled for the field portion of this project are projected to form the basement of Ellensburg, WA, which is an area of current Washington Geological Survey research, including the application of potential field geophysics methods.

To target sampling effectively, we used various Geographic Information System (GIS) overlay methods to determine where areas of outcrop are likely, what geologic unit each outcrop represents, and where aeromagnetic anomalies are located. Initially, LiDAR (Light Detection and Ranging) data was used to create a reclassified slope layer in ArcGIS Pro, showing locations where local slope exceeds 50° and thus represent possible outcrop. However, this approach yielded too many false positives to be useful in the steep, forested terrain of the central Cascades, where stumps and downed trees are common. A more successful approach is to identify potential outcrop visually from patterns in the LiDAR data in combination with Google Earth satellite imagery. This LiDAR data was then overlain with layers showing geologic unit contacts from existing geologic maps, and a map of aeromagnetic anomaly to identify locations to target in the field. An overlay showing roads and trails helped identify the easiest way to access each area but finding GIS data showing roads and trails at the level of detail and accuracy suitable for our pursuits has proven challenging. Other challenges have included gaps in LiDAR coverage on the edges of the study area, as well as finding a suitable way to display all necessary data such that one layer does not obscure the other. So far the best approach has been to overlay geologic units, aeromagnetic anomalies, and roads on a LiDAR base, but colored geologic units partially obscure the aeromagnetic data, so plotting only the geologic contacts would be more effective. Despite these difficulties, using GIS as a tool with which relevant outcrops can be identified has proven an effective way to make fieldwork more efficient, and could be modified for different data types in various environments.