Gravity ground-based surveys of Southern Africa

Gravity ground-based surveys of Southern Africa

This dataset is a compilation of several public domain ground measurements of gravity from Southern Africa. The observations are the absolute gravity values in mGal. The horizontal datum is not specified and heights are referenced to “sea level”, which we will interpret as the geoid (which realization is likely not relevant since the uncertainty in the height is probably larger than geoid model differences).

Original source: NOAA NCEI

Pre-processing: Source code for preparation of the original dataset for redistribution in Ensaio

import pandas as pd
import pygmt

import ensaio

Download and cache the data and return the path to it on disk

Out:

/home/runner/work/_temp/cache/ensaio/v1/southern-africa-gravity.csv.xz

Load the CSV formatted data with pandas

longitude latitude height_sea_level_m gravity_mgal
0 18.34444 -34.12971 32.2 979656.12
1 18.36028 -34.08833 592.5 979508.21
2 18.37418 -34.19583 18.4 979666.46
3 18.40388 -34.23972 25.0 979671.03
4 18.41112 -34.16444 228.7 979616.11
... ... ... ... ...
14354 21.22500 -17.95833 1053.1 978182.09
14355 21.27500 -17.98333 1033.3 978183.09
14356 21.70833 -17.99166 1041.8 978182.69
14357 21.85000 -17.95833 1033.3 978193.18
14358 21.98333 -17.94166 1022.6 978211.38

14359 rows × 4 columns



Make a PyGMT map with the data points colored by the gravity data.

fig = pygmt.Figure()
fig.basemap(
    region=[
        data.longitude.min() - 1,
        data.longitude.max() + 1,
        data.latitude.min() - 1,
        data.latitude.max() + 1,
    ],
    projection="M15c",
    frame=True,
)
pygmt.makecpt(cmap="viridis", series=[data.gravity_mgal.min(), data.gravity_mgal.max()])
fig.plot(
    x=data.longitude,
    y=data.latitude,
    color=data.gravity_mgal,
    cmap=True,
    style="c0.05c",
)
fig.colorbar(frame='af+l"gravity [mGal]"')
fig.coast(shorelines=True, water="royalblue4", area_thresh=1e4)
fig.show()
southern africa gravity

Out:

<IPython.core.display.Image object>

Total running time of the script: ( 0 minutes 6.476 seconds)

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