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

fname = ensaio.fetch_southern_africa_gravity(version=1)
print(fname)

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

Load the CSV formatted data with pandas

data = pd.read_csv(fname)
data

	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,
    fill=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()