MTpy Example 07 - Open-source Python packages for Magnetotellurics

Resistivity and Phase Pseudosections¶

A pseudosection plot distance on the x-axis and period on the y-axis. This provides a plot that is an proxy for structures at depth. You will see obvious static shifts and suspect data. The phase is often better to analyze because it is invariant to static shifts.

from pathlib import Path
from mtpy import MTCollection
%matplotlib inline

Open MTCollection¶

In the previous example we created a MTH5 file from existing Yellowstone data. Let’s open that file here for plotting.

%%time
with MTCollection() as mc:
    mc.open_collection(Path().cwd().joinpath("yellowstone_mt_collection.h5"))
    mc.working_dataframe = mc.master_dataframe[mc.master_dataframe.station.str.startswith("SR2")]
    dgh_mt_data = mc.to_mt_data()

24:10:17T16:26:00 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/jovyan/earthscope-mt-course/notebooks/mtpy/yellowstone_mt_collection.h5
CPU times: user 20 s, sys: 248 ms, total: 20.3 s
Wall time: 21.1 s

Set the UTM Coordinate System¶

To estimate physical distances in meters, we need to set the UTM coordinate system. The 2D line is in WGS84 UTM Zone 12N, which is EPSG number 32612

dgh_mt_data.utm_epsg = 32612

Plot Phase Tensor Pseudosection for the Snake River Plane stations¶

Since the DeGroot-Hedlin Snake River Plane line was collected with 2D modeling in mind, lets use that for an example.

plot_rp_pseudosection = dgh_mt_data.plot_resistivity_phase_pseudosections( 
    fig_num=1, 
    profile_reverse=True,
    station_id=[3, None],
    station_step=2,
    y_stretch=80000,
)

Adjust Interpolation¶

If you want to try to minimize the bad data points we can play around with interpolation methods and median filtering. This should be done with caution in that the stations are spaced equally which can make weighting unnatural.

Cubic Spline interpolation¶

plot_rp_pseudosection.interpolation_method = "cubic"
plot_rp_pseudosection.fig_num = 2
plot_rp_pseudosection.redraw_plot()

Median Filter 2D¶

plot_rp_pseudosection.median_filter_kernel = (13, 7)
plot_rp_pseudosection.fig_num = 3
plot_rp_pseudosection.redraw_plot()

Delaunay Triangulation¶

plot_rp_pseudosection.interpolation_method = "delaunay"
plot_rp_pseudosection.fig_num = 4
plot_rp_pseudosection.redraw_plot()

plot_rp_pseudosection.interpolation_power = 13
plot_rp_pseudosection.nearest_neighbors = 5
plot_rp_pseudosection.fig_num = 5
plot_rp_pseudosection.redraw_plot()