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How to plot Shear-wave splitting measurements using PyGMT

Utpal Kumar   2 minute read      

How you can plot the shear-wave splitting measurements from splitting database using PyGMT.

You can easily obtain the SKS splitting measurements from the Shear-wave splitting data base. In this post, I will show you how you can do the custum plot of these measurements.

First step is to download and save the splittingDB.txt in your local directory.

Plot shear-wave splitting measurements for arbitrarily selected region

We can read the database using pandas:

import pygmt
import numpy as np
import pandas as pd

df = pd.read_csv('splittingDB.txt', sep='|', encoding='latin1')
df.dropna(inplace=True)
df['Latitude'] = pd.to_numeric(df['Latitude'], errors='coerce')
df['Longitude'] = pd.to_numeric(df['Longitude'], errors='coerce')
df['phi'] = pd.to_numeric(df['phi'], errors='coerce')
df['dt'] = pd.to_numeric(df['dt'], errors='coerce')

We select arbitrary region for plotting the measurements. Then we extract the

minlon, maxlon = -132.89, -66.09
minlat, maxlat = 22.62, 51.64
dftmp1 = df[(minlon < df['Longitude']) & (df['Longitude'] < maxlon)]
dfselected = dftmp1[(minlat < df['Latitude']) & (df['Latitude'] < maxlat)]
dfselected.reset_index(inplace=True)
print(dfselected.head())

   index  id Station  Latitude  Longitude   phi    dt  refID Phase pmax pmin dtmax dtmin remark
0      4   4     LAC     34.39    -116.41 -54.0  1.20      0   SKS                             
1      5   5     LON     46.75    -121.81  84.0  1.00      0   SKS   11        0.2             
2      6   6     MNV     38.43    -118.15  75.0  0.90      0   SKS                             
3      9   9    RSCP     35.59     -85.57  59.0  0.75      0   SKS    6       0.15             
4     11  11    RSNY     44.55     -74.53  74.0  0.90      0   SKS    5       0.15

Finally, we can plot the measurements using the plot_splitting_map function defined below.

plot_splitting_map(dfselected, boxcoordinates=[
                   minlon, maxlon, minlat, maxlat], dcoord=0.5, dtscale=0.2, penwidth="0.5p",
                   proj="M10c", figname='splitting_map.png', frame=["a5f1", "WSen"],
                   markersizescale=0.1)
Shear wave splitting measurements of North America from SKS database
Shear wave splitting measurements of North America from SKS database

Similar posts

Topographic splitting map of Germany

# germany
minlon, maxlon = 4.26, 17.00
minlat, maxlat = 45.14, 54.92
dftmp1 = df[(minlon < df['Longitude']) & (df['Longitude'] < maxlon)]
dfselected = dftmp1[(minlat < df['Latitude']) & (df['Latitude'] < maxlat)]
dfselected.reset_index(inplace=True)
plot_splitting_map(dfselected, boxcoordinates=[
                minlon, maxlon, minlat, maxlat], dcoord=0.5, dtscale=0.5, penwidth="0.5p",
                proj="M10c", figname='splitting_map.png', frame=["a5f1", "WSen"],
                markersizescale=0.1, colormap="topo", markercolormap="jet")
Topographic Shear wave splitting measurements from SKS database of Germany
Topographic Shear wave splitting measurements from SKS database of Germany

Shear-wave splitting function

References

  1. Kumar, U., C. P. Legendre, and B. S. Huang (2021). Crustal structure and upper mantle anisotropy of the Afar triple junction, Earth, Planets Sp. 73, no. 1, 166, doi: 10.1186/s40623-021-01495-0.
  2. Wüstefeld, A.; Bokelmann, G. H. R.; Barruol, G.; Montagner, J.-P. (2009) “Identifying global seismic anisotropy patterns by correlating shear-wave splitting and urface waves data” , Phys. Earth Planet. Int, 176 (3-4), 198-212, doi:10.1016/j.pepi.2009.05.006 (database available online at http://splitting.gm.univ-montp2.fr/DB)
  3. Kumar, Utpal, & Legendre, Cédric P. (2021, January 16). STADIUM-Py: Python Command-line Interface for automated Receiver Functions and Shear-Wave Splitting Measurements (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4686103

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