Magnetic airborne survey of Britain

Magnetic airborne survey of Britain

This is a digitization of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections.

Unfortunately, the exact date of measurements is not available (only the year).

Contains British Geological Survey materials © UKRI 2021.

Original source: British Geological Survey

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

import numpy as np
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/britain-magnetic.csv.xz

Load the CSV formatted data with pandas

line_and_segment year longitude latitude height_m total_field_anomaly_nt
0 FL1-1 1955 -1.74162 53.48164 792 62
1 FL1-1 1955 -1.70122 53.48352 663 56
2 FL1-1 1955 -1.08051 53.47677 315 30
3 FL1-1 1955 -1.07471 53.47672 315 31
4 FL1-1 1955 -1.01763 53.47586 321 44
... ... ... ... ... ... ...
541503 FL-3(TL10-24)-1 1965 -4.68843 58.26786 1031 64
541504 FL-3(TL10-24)-1 1965 -4.68650 58.26786 1045 74
541505 FL-3(TL10-24)-1 1965 -4.68535 58.26790 1035 94
541506 FL-3(TL10-24)-1 1965 -4.68419 58.26787 1024 114
541507 FL-3(TL10-24)-1 1965 -4.68274 58.26790 1011 120

541508 rows × 6 columns



Make a PyGMT map with the data points colored by the total field magnetic anomaly.

fig = pygmt.Figure()
scale = np.percentile(data.total_field_anomaly_nt, 95)
pygmt.makecpt(cmap="polar", series=[-scale, scale])
fig.plot(
    x=data.longitude,
    y=data.latitude,
    style="c0.02c",
    color=data.total_field_anomaly_nt,
    cmap=True,
    projection="M15c",
)
fig.colorbar(frame='af+l"nT"', position="jBL+h+w7c/0.2c+o1/2")
fig.coast(shorelines=True)
fig.basemap(frame="afg")
fig.show()
britain magnetic

Out:

<IPython.core.display.Image object>

Total running time of the script: ( 0 minutes 30.328 seconds)

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