Lately I’ve been working on using the yt library (https://yt-project.org) for 3D visualization of seismic data sets. Seismologists generally avoid 3D plots in lieu of 2D slices of 3D data as it’s often hard to interpret 3D structure. Much of that difficultly comes from highlighting iso-surfaces, but yt is different in that it uses ray-tracing to project through a data set, integrating information as it goes. The result is a 3D rendering that maintains accumulated structure.
Here’s an example of a 3D rendering of negative velocity anomalies in the Western U.S. from 50-1200 km deep, using the shear wave data from James et al. 2011, one of the 3D datasets available via IRIS (https://ds.iris.edu/ds/products/emc-nwus11-s/):
The highlighted structures represent regions in the Earth’s mantle that exhibit slower seismic shear wave speeds when compared to a reference model and the faint grid in the middle of the model domain is at 410 km, the upper limit of the mantle transition zone. The white dot on the surface is Yellowstone.
This is a preliminary image, so I won’t dive into interpretation of the structure embedded here, so for now, just enjoy this mesmerizing GIF:
Today I found myself needing to plot the physiographic boundary of the Colorado Plateau in Python. It’s been a while since I’ve touched on shapefiles (or anything on the blog) so I figured I’d write a quick blurb on reading and plotting this particular shapefile.
What you’ll learn: reading shapefiles, merging polygon shapes in Python with shapely
The first challenge was finding the actual lat/lon coordinates defining the edge of the Colorado Plateau… it’s amazing how many papers in geology/geophysics plot the boundary but don’t actually reference where the heck they got their coordinates from. After much digging I FINALLY found a paper that actually cited their source: Hopper and Fischer 2018  reference a 1946 publication by Fenneman and Johnson  titled “Physiographic divisions of the conterminous U. S.” and after a quick search I found the digitized data from that publication online at water.usgs.gov.
The dataset contains a large number of physiographic regions and the Colorado Plateau is subdivided into multiple regions, so the code below pulls out the regions within the Colorado Plateau and joins them into a single shape defining the full boundary. To run the code below, unpack physio_shp.zip wherever you downloaded it to and rename the folder to physio (to match expectations for the pyshp shapefile reader).
The XML data for the shapefile defines a province code for different provinces, for which the Colorado Plateau sub-regions have a value of 21. So the code (1) reads the shapefiles, (2) finds the shapes with a province code of 21 and (3) combines them.
Step 1: imports, reading arguments, reading the shapefile.
shapefile is the library for pyshp, otherwise pretty self explanatory:
import shapefile, os,sys
import matplotlib.pyplot as plt
from shapely.geometry import Polygon
from shapely.ops import cascaded_union
# read the arguments
fname=sys.argv # path to physio.shp
outfolder=sys.argv # folder to store output
# read the shapefile
sf = shapefile.Reader(fname)
Step 2: Find the Colorado Plateau shapes.
The shapes are described in the records list of the shapefile object:
records() is a list of attributes for each shape and a single record looks like
The final value is the province code — so we just need to save off the indeces for which that value is 21. It turns out the 3rd value in the record list is actually a cross-reference to a shape ID, but for some reason the indexing is offset by 2 when reading this shapefile with python. So the shape data for this shape would be accessed with:
rather than 220. Not sure why it’s off by 2 (would expect it to be off by 1 due to python indexing), but in any case, my code simply records the list index as python sees it:
# find the record indeces for colorado plateau (province ID = 21)
for rec in sf.records():
# plot the individual records
for rec in recs_to_plot:
lons=[pt for pt in pts]
lats=[pt for pt in pts]
As seen above — the coordinates for the shape boundaries for a record are in
which is a list of longitude and latitude points (which the code unpacks for plotting). This section of code will generate the following outline of the Colorado Plateau regions:
Step 3: merging shapes
This is the fun bit! What we want is just the outer boundary of the union of all the shapes. The python library shapely lets us do this very easily by creating a list of shapely Polygon objects then combining them with the cascaded_union method:
# create a single shape for Colorado Plateau from union of sub-shapes
for rec in recs_to_plot:
# plot the exterior shape
lon,lat = CP_bound.exterior.xy
and the resulting plot of just the exterior boundary:
Step 4: output the processed data
The code also exports the lat/lon points defining that exterior boundary with:
# export the final shape as a CSV of boundary points
if outfolder is not None:
for i,j in zip(lon,lat):
I could have written some code to save the data in a shapefile format, but for such a small amount of data I find it easier to save a CSV and just create a Polygon from the list of points as I need it. I’m actually planning to create a Polygon that will be combined with GeoPandas to find sets of points falling within the plateau (GeoPandas lets you do database joins on geospatial data, it’s awesome!).
where the first argument is the path to the downloaded and unpacked shapefile and the second argument is the location to save the CSV file (this argument is optional — no data will be saved if not included).
 Hopper, E., & Fischer, K. M. (2018), The changing face of the lithosphere-asthenosphere boundary: Imaging continental scale patterns in upper mantle structure across the contiguous U.S. with Sp converted waves. Geochemistry, Geophysics, Geosystems, 19 , 2 593 – 2 614 . https://doi.org/10. 1029/2018GC007476