ASEG to NetCDF

This example demonstrates the workflow for creating a GS file from the ASEG file format, as well as how to add multiple associated datasets to the Survey (e.g., Tabular and Raster groups). Specifically, this AEM survey contains the following datasets:

1. Raw AEM data, from the Tempest system

2. Inverted resistivity models

3. An interpolated map of total magnetic intensity

Dataset Reference: Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., Hoogenboom, B.E., and Burton, B.L., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9E44CTQ.

import matplotlib.pyplot as plt
from os.path import join
from gspy import Survey

Convert the ASEG data to NetCDF

Initialize the Survey

# Path to example files
data_path = '..//..//supplemental//region//MAP'

# Survey Metadata file
metadata = join(data_path, "data//Tempest_survey_md.json")

# Establish survey instance
survey = Survey(metadata)

1. Raw Data - Import raw AEM data from ASEG-format. Define input data file and associated metadata file

d_data = join(data_path, 'data//Tempest.dat')
d_supp = join(data_path, 'data//Tempest_data_md.json')

# Add the raw AEM data as a tabular dataset
survey.add_tabular(type='aseg', data_filename=d_data, metadata_file=d_supp)

2. Inverted Models - Import inverted AEM models from ASEG-format. Define input data file and associated metadata file

m_data = join(data_path, 'model//Tempest_model.dat')
m_supp = join(data_path, 'model//Tempest_model_md.json')

# Read model data and format as Tabular class object
survey.add_tabular(type='aseg', data_filename=m_data, metadata_file=m_supp)

3. Magnetic Intensity Map - Import the magnetic data from TIF-format. Define input metadata file (which contains the TIF filenames linked with desired variable names)

r_supp = join(data_path, 'data//Tempest_raster_md.json')

# Read data and format as Raster class object
survey.add_raster(metadata_file = r_supp)

# Save NetCDF file
d_out = join(data_path, 'data//Tempest.nc')
survey.write_netcdf(d_out)

Read back in the NetCDF file

new_survey = Survey.open_netcdf(d_out)

# Once the survey is read in, we can access variables like a standard xarray dataset.
print(new_survey.raster.magnetic_tmi)

<xarray.DataArray 'magnetic_tmi' (y: 1212, x: 599)>
[725988 values with dtype=float64]
Coordinates:
    spatial_ref  float64 ...
  * x            (x) float64 2.928e+05 2.934e+05 2.94e+05 ... 6.51e+05 6.516e+05
  * y            (y) float64 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05
Attributes:
    standard_name:  total_magnetic_intensity
    null_value:     1.70141e+38
    units:          nT
    grid_mapping:   spatial_ref
    valid_range:    [-17504.6640625   11490.32324219]
    long_name:      Total magnetic intensity, diurnally corrected and filtered

Plotting

# Make a scatter plot of a specific tabular variable, using GSPy's plotter
plt.figure()
# new_survey.tabular[0]['Tx_Height'].plot(x='x', marker='o', linestyle='None')
new_survey.tabular[0].gs_tabular.scatter(x='x', hue='Tx_Height', cmap='jet')

# Make a 2-D map plot of a specific raster variable, using Xarrays's plotter
plt.figure()
new_survey.raster['magnetic_tmi'].plot(vmin=-1000, vmax=1000, cmap='jet')
plt.show()

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