Field Camp Work (2020)

Geologic Maps


Cross Sections

Sage Hen Flat, White-Inyo Mountains, California

7.24 Sage Hen Cross Section (form lines).pdf

After examining the existing two maps (below) and where they disagreed, as well as interpreting all the new AMS and gravity data I had access to, I put together a cross section from my own interpretations. I used mostly the same color scheme as Bilodeau & Nelson (1993). By this point in field camp, I had gotten fully comfortable with uncertainty and making the most geologically reasonable interpretation instead of the "correct" interpretation. I'm also very proud of my Illustrator drafting skills on display here. :)

Using field photos, structural measurements and satellite mapping, I constructed a field map of the Sage Hen Pluton. I reconciled differences in interpretation in geologic contacts and fault locations to create the most geologically reasonable map. This was an exercise in quantifying uncertainty and making reasonable interpretations from limited data. Look at all those faults!

7.23 Sage Hen Final Map.pdf
7.16 Ernst & Hall (1987).pdf
7.16 Bilodeau & Nelson (1993).pdf

Past geologic maps of the Sage Hen Pluton. Ernst & Hall (1987) (top) Bilodeau & Nelson (1993) (bottom)

Cross sections from geologic maps by Ernst & Hall (1987) (top) and Bilodeau & Nelson (1993) (bottom).

Notice the differences between all three maps. They look very similar at first glance, but each has significant differences from the other. For example, Ernst & Hall interpret the granite pluton (pink) having a thickness beyond 3000ft. I had access to gravity anomaly data, which suggest a maximum thickness around 600 meters, with a specific geometry.

Additionally, I had access to field data to map faults and dikes, AMS to map lineations and foliations, and mineralogical data to suggest textural zonation.

Ghost Ranch, New Mexico

6.26 USGS GhostRanch Map.pdf

USGS map of the Ghost Ranch quadrangle, by Koning, et. al., 2006. This map is much further zoomed out compared to my map, but the area in the center is covered by my map. I created mine independently from this interpretation, of course.

6.29 Ghost Ranch Map.pdf

Here's a map I made from field photos, structural data and Google Earth. Knowledge of the stratigraphy and how it looks like in outcrops is vital to understanding this area. The faults on this map are interpreted by both myself and the USGS map to make a horst and graben strucutre, characteristic of the Rio Grande Rift.

Elk Basin, Park County, Wyoming and Carbon County, Montana

I made this cross section across a similar area in Elk Basin. I was tasked with mapping the geologic units (top right) in Google Earth, any faults in the area and make an interpretation cross section that agrees with the map I produced. Both myself and Jackson et. al., 2016 determined this area to be a anticline structure, controlled by a subsurface fault bend fold.

This is a reasonable interpretation in the larger regional context as well. Just to the West are the Beartooth Mountains, placing this solidly in the Laramide province. Most large-scale structures in the region are controlled by thick-skinned thrust faults that displace Precambrian basement rock. This context is very useful in inferring subsurface structure without directly observing it.

7.6 Elk Basin Cross Section.pdf
2016_Jacksonetal Elk Basin Map.pdf
2016_Jacksonetal Elk Basin Cross Section.pdf

These are three figures from Jackson et. al., 2016. They show the location of the Elk Basin (top right), a geologic map of the field area (top), and the author's cross section interpretation (bottom).

Field Reports

ArcGIS: Seattle Landslides

The purpose of this project was to identify areas in Seattle that are most susceptible to future landslides. We used ArcGIS and a host of tools it offers to answer that question, as well as suggest the factors most responsible for landslides in Seattle.

7.15 Landslide Susceptibilty Map.pdf
7.14 Seattle Data.pdf
7.15 Seattle Landslides Report.pdf

Do you like ArcGIS figures? Well this is the project for you. I mapped previous landslides in ArcGIS and did several analyses to identify areas of high risk of landslides in Seattle. Here's some reading material for you if you're into Quaternary geology.

Most interesting to me in this project, we wanted to say something broadly about what factor correlates the strongest with landslides. In other words, we determined the strongest factors correlating areas to mapped landslides. We did a frequency ratio analysis (bottom left) for the area we mapped as landslides, with 5 different factors: slope, relief, precipitation and the geology of the host rock.

We broadly concluded that areas with high relief, high slope and most strongly, coastline areas are most susceptible to landslides. Here's an interesting thought though: I think we proved correlation, not causation. I was careful in my conclusions not to say high relief was causing landslides, because that's not true. That's an important part of science: to prove what your evidence supports and nothing more.