Note
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ASEG-GDF (Tempest AEM)
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. Specifically, this AEM survey contains the following datasets:
Raw AEM data, from the Tempest system
Inverted resistivity models
An interpolated map of total magnetic intensity
Source 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
import gspy
Initialize the Survey
# Path to example files
data_path = "..//data_files//tempest_aseg"
# Survey Metadata file
metadata = join(data_path, "data//Tempest_survey_md.yml")
# Establish survey instance
survey = gspy.Survey.from_dict(metadata)
Survey YAML file
1dataset_attrs:
2 title: Example Tempest Airborne Electromagnetic (AEM) Dataset
3 institution: USGS Geology, Geophysics, & Geochemistry Science Center
4 source: Contractor provided ASEG-GDF2 formatted data
5 history: This example dataset includes the raw AEM data and gridded magnetic data as provided by the contractor, CGG Canada Services, Ltd, as well as 1-D resistivity models inverted by the USGS using the GALEISBSTDEM time-domain deterministic inversion software (Brodie 2015, Geoscience Australia, Release-20160606).
6 references: "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."
7 comment: This dataset is incomplete and has been downsampled for the purposes of this example.
8 content: Tempest AEM Survey from the Mississippi Alluvial Plain
9
10spatial_ref:
11 wkid: 5070
12 authority: EPSG
13
14survey_information:
15 contractor_project_number: 603756FWA
16 contractor: CGG Canada Services Ltd.
17 client: U.S. Geological Survey
18 survey_type: electromagneticmagneticradiometric
19 survey_area_name: Mississippi Alluvial Plain (MAP)
20 state: MO,AR,TN,MS,LA,IL,KY
21 country: USA
22 acquisition_start: 20191120
23 acquisition_end: 20200307
24 dataset_created: 20200420
25
26survey_units:
27 time: seconds [s]
28 area: square meters [m^2]
29 current: Amperes [A]
30 frequency: Hertz [Hz]
31 electromagnetic_moment: Ampere square meters [Am^2]
32 magnetometer_b_field: nano Tesla [nT]
33 electromagnetic_b_field: femto tesla [fT]
34
35flightline_information:
36 traverse_line_spacing: 6000
37 traverse_line_direction: various
38 nominal_terrain_clearance: 120
39 final_line_kilometers: 24868
40 TRAVERSE LINE NUMBERS: "[192401 - 265021, 400801 - 401401, 500101, 604501 - 608101, 700201 - 700206, 710101 - 710401]"
41 REPEAT LINE NUMBERS: 9100071 - 9180772
42 PRE ZERO LINE NUMBERS: 90200702 - 90207701
43 POST ZERO LINE NUMBERS: 90500702 - 90507701
44
45survey_equipment:
46 aircraft: Cessna C208B
47 aircraft_registration: VH-FHY
48 magnetometer: Bartington MAG-03MS100 three-axis fluxgate
49 magnetometer_installation: Stinger mounted
50 electromagnetic_system: 30Hz TEMPEST
51 electromagnetic_installation: Transmitter loop mounted on the aircraft, Reciver coils in a towed bird
52 electromagnetic_coil_orientations: X,Z
53 spectrometer_system: Radiation Solutions RS-500
54 spectrometer_installation: Mounted in the aircraft
55 radar_altimeter_system: Collins Alt-50
56 radar_altimeter_sample_rate: 0.1 s
57 laser_altimeter_system: Riegl LD90-3
58 laser_altimeter_sample_rate: 0.1 s
59 navigation_system: Real-time differential GPS
60 navigation_sample_rate: 1.0 s
61 acquisition_system: FASDAS
Create a branch and attach data leaves
Create the first branch (container) called “data”
data_container = survey.gs.add_container('data', **dict(content = "raw data"))
Raw Data
Import raw AEM data from ASEG-GDF2 format.
d_data = join(data_path, 'data//Tempest.dat')
d_supp = join(data_path, 'data//Tempest_data_md.yml')
# Add the raw AEM data to the data branch
rd = data_container.gs.add(key='raw_data',
data_filename=d_data,
metadata_file=d_supp)
Note for ASEG-GDF2 files, variable metadata is pulled directly from the DFN file associated with the DAT file. Any additional variable metadata, or desired overwrites to the DFN values, can be passed through the YAML.
In this example, multiple systems are defined in the Tempest_data_md.yml metadata file:
Raw Data YAML file
1dataset_attrs:
2 content: raw AEM data
3 comment: This dataset includes minimally processed (raw) AEM data
4 type: data
5 mode: airborne
6 method: [magnetic, radiometric, "electromagnetic, time domain"]
7 instrument: 30Hz Tempest
8
9coordinates:
10 x: Easting_Albers
11 y: Northing_Albers
12 z: DTM
13
14tempest_system:
15 type: system
16 mode: airborne
17 method: electromagnetic, time domain
18 instrument: 30Hz Tempest
19
20 dimensions:
21 gate_times:
22 standard_name: gate_times
23 long_name: receiver gate times
24 units: seconds
25 missing_value: not_defined
26 bounds: [[5.430000e-06, 1.628000e-05],
27 [2.713000e-05, 3.798000e-05],
28 [4.883000e-05, 5.968000e-05],
29 [7.053000e-05, 1.030800e-04],
30 [1.139400e-04, 1.681900e-04],
31 [1.790400e-04, 2.767000e-04],
32 [2.875500e-04, 4.503200e-04],
33 [4.611700e-04, 7.107400e-04],
34 [7.215900e-04, 1.101380e-03],
35 [1.112230e-03, 1.709030e-03],
36 [1.719880e-03, 2.663920e-03],
37 [2.674770e-03, 4.161360e-03],
38 [4.172210e-03, 6.505170e-03],
39 [6.516030e-03, 1.008600e-02],
40 [1.009686e-02, 1.666171e-02]]
41 centers: [1.085000e-05, 3.255000e-05, 5.426000e-05, 8.681000e-05,
42 1.410600e-04, 2.278700e-04, 3.689300e-04, 5.859500e-04,
43 9.114800e-04, 1.410630e-03, 2.191900e-03, 3.418070e-03,
44 5.338690e-03, 8.301020e-03, 1.337928e-02]
45
46 variables:
47
48 data_normalized: True
49 output_data_type: B
50 reference_frame: right-handed positive up
51 output_sample_frequency: 5
52 digitization_frequency: 92160
53
54 transmitter:
55 label: Z
56 area: 155
57 waveform_type: square
58 waveform_time:
59 values: [-1.66666667e-02, -1.66612413e-02, -5.4255e-06, 0.0, 5.4255e-06, 1.66612413e-02, 1.66666667e-02]
60 long_name: not_defined
61 missing_value: not_defined
62 units: s
63 waveform_current:
64 values: [0.0, 1.0, 1.0, 0.0, -1.0, -1.0, 0.0]
65 dimensions: 'waveform_time'
66 scale_factor: 0.5
67 peak_current: 560
68 peak_moment: 86800
69 base_frequency: 30
70 orientation: Z
71 number_of_turns: 1
72 on_time: 0.00833
73 off_time: 0.00833
74
75 receiver:
76 label: [z, x]
77 orientation: [z, x]
78
79 couplet:
80 transmitters: [z, z]
81 receivers: [z, x]
82 txrx_dx: [-120, -120]
83 txrx_dy: [0, 0]
84 txrx_dz: [-52, -52]
85
86variables:
87 Easting_Albers:
88 long_name: Easting_Albers:PROJECTION=WGS84/Albers
89 Northing_Albers:
90 long_name: Northing_Albers:PROJECTION=WGS84/Albers
91 DTM:
92 positive: up
93 datum: NAD88
94 EMX_NonHPRG:
95 system_couplet: z_x
96 dimensions: [index, gate_times]
97 EMX_HPRG:
98 system_couplet: z_x
99 dimensions: [index, gate_times]
100 EMZ_NonHPRG:
101 system_couplet: z_z
102 dimensions: [index, gate_times]
103 EMZ_HPRG:
104 system_couplet: z_z
105 dimensions: [index, gate_times]
Create a 2nd branch and attach model data leaves
model_container = survey.gs.add_container('models', **dict(content = "inverted 1-D electrical resistivity models"))
# Define Path to inverted AEM models and corresponding metadata file
m_data = join(data_path, 'model//Tempest_model.dat')
m_supp = join(data_path, 'model//Tempest_model_md.yml')
# Add models to the model container, note this example contains a "parameters" group that
# is added as a leaflet below the model group.
mod = model_container.gs.add(key='inverted_models',
data_filename=m_data,
metadata_file=m_supp,
system=rd.tempest_system,
derived_from=rd)
Inverted Model YAML file
1dataset_attrs:
2 content: inverted resistivity models
3 comment: This dataset includes inverted resistivity models derived from processed AEM data produced by USGS
4 type: model
5 method: electromagnetic, time domain
6 instrument: 30Hz Tempest
7 mode: airborne
8 property: electrical conductivity
9
10coordinates:
11 x: easting
12 y: northing
13 z: elevation
14
15dimensions:
16 layer_depth:
17 standard_name: layer_depth
18 long_name: inverted model layer depth
19 units: m
20 missing_value: not_defined
21 bounds: [[ 0.0 , 3.0 ],
22 [ 3.0 , 6.3 ],
23 [ 6.3 , 9.93],
24 [ 9.93, 13.92],
25 [ 13.92, 18.31],
26 [ 18.31, 23.14],
27 [ 23.14, 28.45],
28 [ 28.45, 34.3 ],
29 [ 34.3 , 40.73],
30 [ 40.73, 47.8 ],
31 [ 47.8 , 55.58],
32 [ 55.58, 64.14],
33 [ 64.14, 73.56],
34 [ 73.56, 83.92],
35 [ 83.92, 95.31],
36 [ 95.31, 107.84],
37 [107.84, 121.62],
38 [121.62, 136.78],
39 [136.78, 153.46],
40 [153.46, 171.8 ],
41 [171.8 , 191.98],
42 [191.98, 214.18],
43 [214.18, 238.6 ],
44 [238.6 , 265.46],
45 [265.46, 295.01],
46 [295.01, 327.51],
47 [327.51, 363.26],
48 [363.26, 402.59],
49 [402.59, 445.85],
50 [445.85, 489.11]]
51 centers: [ 1.5 , 4.65 , 8.115, 11.925, 16.115, 20.725, 25.795,
52 31.375, 37.515, 44.265, 51.69 , 59.86 , 68.85 , 78.74 ,
53 89.615, 101.575, 114.73 , 129.2 , 145.12 , 162.63 , 181.89 ,
54 203.08 , 226.39 , 252.03 , 280.235, 311.26 , 345.385, 382.925,
55 424.22 , 467.48 ]
56
57 gate_times:
58 standard_name: gate_times
59 long_name: receiver gate times
60 units: seconds
61 missing_value: not_defined
62 bounds: [[5.430000e-06, 1.628000e-05],
63 [2.713000e-05, 3.798000e-05],
64 [4.883000e-05, 5.968000e-05],
65 [7.053000e-05, 1.030800e-04],
66 [1.139400e-04, 1.681900e-04],
67 [1.790400e-04, 2.767000e-04],
68 [2.875500e-04, 4.503200e-04],
69 [4.611700e-04, 7.107400e-04],
70 [7.215900e-04, 1.101380e-03],
71 [1.112230e-03, 1.709030e-03],
72 [1.719880e-03, 2.663920e-03],
73 [2.674770e-03, 4.161360e-03],
74 [4.172210e-03, 6.505170e-03],
75 [6.516030e-03, 1.008600e-02],
76 [1.009686e-02, 1.666171e-02]]
77 centers: [1.085000e-05, 3.255000e-05, 5.426000e-05, 8.681000e-05,
78 1.410600e-04, 2.278700e-04, 3.689300e-04, 5.859500e-04,
79 9.114800e-04, 1.410630e-03, 2.191900e-03, 3.418070e-03,
80 5.338690e-03, 8.301020e-03, 1.337928e-02]
81
82variables:
83 elevation:
84 positive: up
85 datum: NAD88
86
87 conductivity:
88 dimensions: [index, layer_depth]
89
90 thickness:
91 dimensions: [index, layer_depth]
92
93 observed_EMSystem_1_XS:
94 system_couplet: z_x
95 dimensions: [index, gate_times]
96
97 observed_EMSystem_1_ZS:
98 system_couplet: z_z
99 dimensions: [index, gate_times]
100
101 noise_EMSystem_1_XS:
102 system_couplet: z_x
103 dimensions: [index, gate_times]
104
105 noise_EMSystem_1_ZS:
106 system_couplet: z_z
107 dimensions: [index, gate_times]
108
109 predicted_EMSystem_1_XS:
110 system_couplet: z_x
111 dimensions: [index, gate_times]
112
113 predicted_EMSystem_1_ZS:
114 system_couplet: z_z
115 dimensions: [index, gate_times]
116
117inversion_parameters:
118 dataset_attrs:
119 type: parameters
120 method: electromagnetic, time domain
121 instrument: 30Hz Tempest
122 mode: airborne
123 property: electrical conductivity
124
125 variables:
126 software: GALEISBSTDEM 1-D time-domain deterministic inversion software
127 software_reference: "Brodie, R. C., 2015, GALEISBSTDEM: A deterministic algorithm for 1D sample by sample inversion of time-domain AEM data – theoretical details, accessed May 1, 2020, at https://github.com/GeoscienceAustralia/ga-aem/blob/master/docs/GALEISBSTDEM%20Inversion%20Algorithm%20Theoretical%20Details%20.pdf."
128 description: Inversions were done using a multilayered smooth model formulation in which 30 layer thicknesses were fixed and layer conductivities were solved for. Horizontal (X) and vertical (Z) components of the data were inverted separately. A vertical conductivity smoothing constraint, alpha_s = 1000, was applied. The inversion reference model used a half-space conductivity of 0.04 Siemens per meter (S/m) with a standard deviation of 1 S/m. The relative importance of the reference conductivity model, alpha_c, was set to 1.0. The horizontal and vertical separation between transmitter and receiver was given a lateral and vertical standard deviation constraint of 0.5 meters (m) in the reference model. The receiver pitch was also included with a 0.5 m standard deviation. These steps were repeated using the same inversion parameters but for reference models of higher (0.2 S/m) and lower (0.008 S/m) conductivity representing lower (5 Ohm-m) and higher (125) resistivity, respectively. A number of inversions were conducted with various homogenous prior model values, and constraints on resistivity. The final model parameters described above were selected because they best represent the physical understanding of the system and minimized data misfit. Final inverted resistivity values for each layer, layer thicknesses, and the uncertainty associated with these values can be found in the model dataset.
129 doi_calculation: The depth of investigation (DOI) for each model location was calculated using the difference between the low and high reference conductivity model results. Using the approach from Oldenburg and Li (1999), models from the low and high reference inversions were divided and rescaled producing a metric of their similarity. Models were similar where constrained by the data (shallow depths) and diverge back to their distinct reference model values when no longer constrained by the data. Therefore, the DOI was calculated as the threshold below which models were no longer informed by the data.
130 phid_cut: Individual models with a data misfit, "PhiD", less than or equal to 1.5 were accepted for final outputs and products. A new channel, "ACCEPT_FLAG" was added to the data file representing this misfit cutoff, with 0 = rejected models and 1 = accepted models.
Create a 3rd branch for the magnetic intensity map
# Create a new branch for the contractor-derived total magnetic intensity map
map_container = survey.gs.add_container('derived_maps', **dict(content = "derived maps"))
# Import the magnetic data from TIF-format.
d_supp = join(data_path, 'data//Tempest_raster_md.yml')
# Add the magnetic map to the data
maps = map_container.gs.add(key='maps', metadata_file = d_supp)
Note: raster data files are defined within the metadata file
Magnetic Raster Map YAML file
1dataset_attrs:
2 comment: contractor-derived product
3 content: gridded map of total magnetic intensity
4 type: data
5 mode: airborne
6 method: magnetic
7 instrument: Scintrex CS-3 cesium vapor magnetometer
8
9magnetic_system:
10 type: system
11 mode: airborne
12 method: magnetic
13 instrument: Scintrex CS-3 c cesium-vapor magnetometer
14
15 prefixes: ['base_magnetometer']
16
17 dimensions:
18 base_mag_locations:
19 standard_name: base_mag_locations
20 long_name: Base Magnetometer Location Index Numbers
21 units: not_defined
22 missing_value: not_defined
23 length: 6
24 increment: 1
25 origin: 1
26
27 variables:
28 transmitter:
29 label: passive
30 description: No artificial transmitter was used; the system measures the Earth's natural magnetic field (passive field).
31
32 receiver:
33 label: scalar_magnetometer
34 sensor_type: cesium_vapor
35 sensor_model: CS-3
36 sensor_manufacturer: Scintrex
37 description: Scalar cesium-vapor magnetometer mounted in the aircraft tail stinger.
38 Magnetic samples are processed by a FASDAS magnetometer processor
39 board and synchronized via GPS PPS; a Bartington MAG-03MS100
40 three-axis fluxgate provides aircraft attitude for compensation.
41 orientation: tail-stinger mounted
42 coordinates:
43 values: "[-10.74, 0.0, -0.55]"
44 long_name: magnetometer location relative to Tx loop center (X,Y,Z)
45 units: meters
46 acquisition_system: FASDAS survey/magnetometer computer; NovAtel OEMV-3 GPS with
47 OMNISTAR differential corrections; dynamic compensation driven by IMU
48 and fluxgate attitude inputs. A tail stinger mounted Bartington MAG-03MS100 three-axis fluxgate magnetometer is used to provide information on the attitude of the aircraft. This information is used for compensation of the measured magnetic total field.
49 sample_frequency:
50 values: 0.2
51 units: s
52 sensitivity:
53 values: 0.001
54 units: nT
55 typical_noise:
56 values: 1.0
57 units: nT
58 compensation: fully digital
59 parallax:
60 values: 1.8
61 units: s
62
63 base_magnetometer:
64 label: base_magnetometer
65 description: Ground magnetic base stations established at low-gradient sites; two CF1 magnetometers operated continuously at 1 s sampling with ~0.01 nT sensitivity. Base data edited to remove spikes/level shifts and used for diurnal correction per base location.
66 sensor_type: CF1 magnetometer
67 sample_frequency:
68 values: 1
69 units: s
70 sensitivity:
71 values: 0.01
72 units: nT
73
74 location_names:
75 values: ["Greenwood, MS", "Alexandria, LA", "Monroe, LA", "West Memphis, AR", "Sikeston, MO", "Greenwood, MS"]
76 dimensions: 'base_mag_locations'
77
78 flights:
79 values: ["007-027", "028-036", "038-042", "044-062", "063-074", "075-077"]
80 dimensions: 'base_mag_locations'
81
82 values:
83 values: [49170.0, 47640.7, 48362.6, 50083.5, 51678.8, 49170.0]
84 dimensions: 'base_mag_locations'
85
86
87 dynamic_compensation: Compensation calibration flights at high altitude; pitches/rolls/yaws used to derive coefficients that remove aircraft-induced magnetic noise. Reported improvement ratio ~2.91 (std. dev. uncompensated vs. compensated).
88 diurnal_correction: Diurnal base values applied by location (see base_magnetometer section); base data edited and filtered to remove non-geophysical disturbances.
89 igrf_model_date: "2019-12-01"
90 igrf_model_height: "167.9 m"
91 igrf_removed_model_epoch: "2015.0"
92 tieline_levelling: RMI levelled to prior RESOLVE data; due to height differences and limited tie lines, manual adjustments and micro-levelling were applied.
93 deliverables: "Total Magnetic Intensity (TMI), provided with EM & terrain data"
94
95 couplet:
96 transmitters: [passive]
97 receivers: [scalar_magnetometer]
98 description: Passive Earth field transmitter paired with single scalar magnetometer receiver mounted in tail stinger.
99
100coordinates:
101 x: Easting_Albers
102 y: Northing_Albers
103
104dimensions:
105 x: Easting_Albers
106 y: Northing_Albers
107
108variables:
109 magnetic_tmi:
110 dimensions: [x, y]
111 system_couplet: passive_scalar_magnetometer
112 standard_name: total_magnetic_intensity
113 long_name: Total magnetic intensity, diurnally corrected and filtered
114 units: nT
115 missing_value: 1.70141e+38
116 files: [mag.tif]
117
118 Easting_Albers:
119 standard_name: easting_albers
120 long_name: Easting
121 units: meter
122 missing_value: not_defined
123
124 Northing_Albers:
125 standard_name: northing_albers
126 long_name: Northing
127 units: meter
128 missing_value: not_defined
Save NetCDF file
d_out = join(data_path, 'Tempest.nc')
survey.gs.to_netcdf(d_out)
Read back in the NetCDF file
new_survey = gspy.open_datatree(d_out)['survey']
View the Data Tree
print(new_survey)
<xarray.DataTree 'survey'>
Group: /survey
│ Dimensions: ()
│ Coordinates:
│ spatial_ref float64 8B ...
│ Data variables:
│ survey_information float64 8B ...
│ survey_units float64 8B ...
│ flightline_information float64 8B ...
│ survey_equipment float64 8B ...
│ Attributes:
│ type: survey
│ title: Example Tempest Airborne Electromagnetic (AEM) Dataset
│ institution: USGS Geology, Geophysics, & Geochemistry Science Center
│ source: Contractor provided ASEG-GDF2 formatted data
│ history: This example dataset includes the raw AEM data and gridded...
│ references: Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., H...
│ comment: This dataset is incomplete and has been downsampled for th...
│ content: Tempest AEM Survey from the Mississippi Alluvial Plain /su...
│ gspy_version: 2.2.4
│ conventions: GS-2.0, CF-1.13
├── Group: /survey/data
│ │ Dimensions: ()
│ │ Data variables:
│ │ spatial_ref float64 8B ...
│ │ Attributes:
│ │ content: raw data
│ │ type: container
│ └── Group: /survey/data/raw_data
│ │ Dimensions: (index: 2001, gate_times: 15)
│ │ Coordinates:
│ │ * index (index) float64 16kB 0.0 1.0 2.0 ... 1.999e+03 2e+03
│ │ * gate_times (gate_times) float64 120B 1.085e-05 3.255e-05 ... 0.01338
│ │ spatial_ref float64 8B ...
│ │ x (index) float64 16kB ...
│ │ y (index) float64 16kB ...
│ │ z (index) float64 16kB ...
│ │ Data variables: (12/58)
│ │ line (index) float64 16kB ...
│ │ flight (index) float64 16kB ...
│ │ fiducial (index) float64 16kB ...
│ │ proj_cgg (index) float64 16kB ...
│ │ proj_client (index) float64 16kB ...
│ │ date (index) float64 16kB ...
│ │ ... ...
│ │ z_primaryfield (index) float64 16kB ...
│ │ z_vlf1 (index) float64 16kB ...
│ │ z_vlf2 (index) float64 16kB ...
│ │ z_vlf3 (index) float64 16kB ...
│ │ z_vlf4 (index) float64 16kB ...
│ │ z_geofact (index) float64 16kB ...
│ │ Attributes:
│ │ content: raw AEM data
│ │ comment: This dataset includes minimally processed (raw) AEM data
│ │ type: data
│ │ mode: airborne
│ │ method: ['magnetic', 'radiometric', 'electromagnetic, time domain']
│ │ instrument: 30Hz Tempest
│ │ structure: tabular
│ └── Group: /survey/data/raw_data/tempest_system
│ Dimensions: (gate_times: 15, nv: 2, n_transmitter: 1,
│ waveform_time: 7, n_receiver: 2,
│ n_couplet: 2, dim_0: 1)
│ Coordinates:
│ * nv (nv) float64 16B 0.0 1.0
│ * n_transmitter (n_transmitter) float64 8B 0.0
│ * waveform_time (waveform_time) float64 56B -0.01667 ... 0....
│ * n_receiver (n_receiver) float64 16B 0.0 1.0
│ * n_couplet (n_couplet) float64 16B 0.0 1.0
│ Dimensions without coordinates: dim_0
│ Data variables: (12/26)
│ gate_times_bnds (gate_times, nv) float64 240B ...
│ transmitter_label (n_transmitter) <U1 4B ...
│ transmitter_area (n_transmitter) int64 8B ...
│ transmitter_waveform_type (n_transmitter) <U6 24B ...
│ transmitter_waveform_current (waveform_time) float64 56B ...
│ transmitter_scale_factor (n_transmitter) float64 8B ...
│ ... ...
│ couplet_txrx_dz (n_couplet) int64 16B ...
│ data_normalized (dim_0) bool 1B ...
│ output_data_type <U1 4B ...
│ reference_frame <U24 96B ...
│ output_sample_frequency (dim_0) int64 8B ...
│ digitization_frequency (dim_0) int64 8B ...
│ Attributes:
│ type: system
│ mode: airborne
│ method: electromagnetic, time domain
│ instrument: 30Hz Tempest
│ name: tempest_system
├── Group: /survey/models
│ │ Dimensions: ()
│ │ Data variables:
│ │ spatial_ref float64 8B ...
│ │ Attributes:
│ │ content: inverted 1-D electrical resistivity models
│ │ type: container
│ └── Group: /survey/models/inverted_models
│ │ Dimensions: (layer_depth: 30, nv: 2, gate_times: 15,
│ │ index: 2001)
│ │ Coordinates:
│ │ * layer_depth (layer_depth) float64 240B 1.5 4.65 ... 424.2 467.5
│ │ * nv (nv) float64 16B 0.0 1.0
│ │ * gate_times (gate_times) float64 120B 1.085e-05 ... 0.01338
│ │ * index (index) float64 16kB 0.0 1.0 ... 1.999e+03 2e+03
│ │ spatial_ref float64 8B ...
│ │ x (index) float64 16kB ...
│ │ y (index) float64 16kB ...
│ │ z (index) float64 16kB ...
│ │ Data variables: (12/46)
│ │ layer_depth_bnds (layer_depth, nv) float64 480B ...
│ │ gate_times_bnds (gate_times, nv) float64 240B ...
│ │ uniqueid (index) float64 16kB ...
│ │ survey (index) float64 16kB ...
│ │ date (index) float64 16kB ...
│ │ flight (index) float64 16kB ...
│ │ ... ...
│ │ phic (index) float64 16kB ...
│ │ phit (index) float64 16kB ...
│ │ phig (index) float64 16kB ...
│ │ phis (index) float64 16kB ...
│ │ lambda (index) float64 16kB ...
│ │ iterations (index) float64 16kB ...
│ │ Attributes:
│ │ content: inverted resistivity models
│ │ comment: This dataset includes inverted resistivity models derived fr...
│ │ type: model
│ │ method: electromagnetic, time domain
│ │ instrument: 30Hz Tempest
│ │ mode: airborne
│ │ property: electrical conductivity
│ │ structure: tabular
│ ├── Group: /survey/models/inverted_models/tempest_system
│ │ Dimensions: (gate_times: 15, nv: 2, n_transmitter: 1,
│ │ waveform_time: 7, n_receiver: 2,
│ │ n_couplet: 2, dim_0: 1)
│ │ Coordinates:
│ │ * n_transmitter (n_transmitter) float64 8B 0.0
│ │ * waveform_time (waveform_time) float64 56B -0.01667 ... 0....
│ │ * n_receiver (n_receiver) float64 16B 0.0 1.0
│ │ * n_couplet (n_couplet) float64 16B 0.0 1.0
│ │ Dimensions without coordinates: dim_0
│ │ Data variables: (12/26)
│ │ gate_times_bnds (gate_times, nv) float64 240B ...
│ │ transmitter_label (n_transmitter) <U1 4B ...
│ │ transmitter_area (n_transmitter) int64 8B ...
│ │ transmitter_waveform_type (n_transmitter) <U6 24B ...
│ │ transmitter_waveform_current (waveform_time) float64 56B ...
│ │ transmitter_scale_factor (n_transmitter) float64 8B ...
│ │ ... ...
│ │ couplet_txrx_dz (n_couplet) int64 16B ...
│ │ data_normalized (dim_0) bool 1B ...
│ │ output_data_type <U1 4B ...
│ │ reference_frame <U24 96B ...
│ │ output_sample_frequency (dim_0) int64 8B ...
│ │ digitization_frequency (dim_0) int64 8B ...
│ │ Attributes:
│ │ type: system
│ │ mode: airborne
│ │ method: electromagnetic, time domain
│ │ instrument: 30Hz Tempest
│ │ name: tempest_system
│ └── Group: /survey/models/inverted_models/inversion_parameters
│ Dimensions: ()
│ Data variables:
│ software <U61 244B ...
│ software_reference <U298 1kB ...
│ description <U1402 6kB ...
│ doi_calculation <U592 2kB ...
│ phid_cut <U256 1kB ...
│ Attributes:
│ type: parameters
│ method: electromagnetic, time domain
│ instrument: 30Hz Tempest
│ mode: airborne
│ property: electrical conductivity
│ name: inversion_parameters
└── Group: /survey/derived_maps
│ Dimensions: ()
│ Data variables:
│ spatial_ref float64 8B ...
│ Attributes:
│ content: derived maps
│ type: container
└── Group: /survey/derived_maps/maps
│ Dimensions: (x: 599, nv: 2, y: 1212)
│ Coordinates:
│ * x (x) float64 5kB 2.928e+05 2.934e+05 ... 6.51e+05 6.516e+05
│ * nv (nv) float64 16B 0.0 1.0
│ * y (y) float64 10kB 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05
│ spatial_ref float64 8B ...
│ Data variables:
│ x_bnds (x, nv) float64 10kB ...
│ y_bnds (y, nv) float64 19kB ...
│ magnetic_tmi (y, x) float64 6MB ...
│ Attributes:
│ comment: contractor-derived product
│ content: gridded map of total magnetic intensity
│ type: data
│ mode: airborne
│ method: magnetic
│ instrument: Scintrex CS-3 cesium vapor magnetometer
│ structure: raster
└── Group: /survey/derived_maps/maps/magnetic_system
Dimensions: (base_mag_locations: 6, nv: 2,
n_transmitter: 1, n_receiver: 1,
n_couplet: 1, n_base_magnetometer: 1)
Coordinates:
* base_mag_locations (base_mag_locations) float64 48B 1.0 ...
* n_transmitter (n_transmitter) float64 8B 0.0
* n_receiver (n_receiver) float64 8B 0.0
* n_couplet (n_couplet) float64 8B 0.0
* n_base_magnetometer (n_base_magnetometer) float64 8B 0.0
Data variables: (12/35)
base_mag_locations_bnds (base_mag_locations, nv) float64 96B ...
transmitter_label (n_transmitter) <U7 28B ...
transmitter_description (n_transmitter) <U107 428B ...
receiver_label (n_receiver) <U19 76B ...
receiver_sensor_type (n_receiver) <U12 48B ...
receiver_sensor_model (n_receiver) <U4 16B ...
... ...
diurnal_correction <U142 568B ...
igrf_model_date <U10 40B ...
igrf_model_height <U7 28B ...
igrf_removed_model_epoch <U6 24B ...
tieline_levelling <U137 548B ...
deliverables <U63 252B ...
Attributes:
type: system
mode: airborne
method: magnetic
instrument: Scintrex CS-3 c cesium-vapor magnetometer
name: magnetic_system
Once the survey is read in, we can access variables like a standard xarray dataset.
Option A:
print(new_survey['derived_maps/maps'].magnetic_tmi)
<xarray.DataArray 'magnetic_tmi' (y: 1212, x: 599)> Size: 6MB
[725988 values with dtype=float64]
Coordinates:
* y (y) float64 10kB 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05
* x (x) float64 5kB 2.928e+05 2.934e+05 ... 6.51e+05 6.516e+05
spatial_ref float64 8B ...
Attributes:
system_couplet: passive_scalar_magnetometer
standard_name: total_magnetic_intensity
long_name: Total magnetic intensity, diurnally corrected and filtered
units: nT
valid_range: [-17504.6640625 11490.32324219]
grid_mapping: spatial_ref
Option B:
print(new_survey['derived_maps/maps']['magnetic_tmi'])
<xarray.DataArray 'magnetic_tmi' (y: 1212, x: 599)> Size: 6MB
[725988 values with dtype=float64]
Coordinates:
* y (y) float64 10kB 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05
* x (x) float64 5kB 2.928e+05 2.934e+05 ... 6.51e+05 6.516e+05
spatial_ref float64 8B ...
Attributes:
system_couplet: passive_scalar_magnetometer
standard_name: total_magnetic_intensity
long_name: Total magnetic intensity, diurnally corrected and filtered
units: nT
valid_range: [-17504.6640625 11490.32324219]
grid_mapping: spatial_ref
Option C:
print(new_survey['derived_maps']['maps']['magnetic_tmi'])
<xarray.DataArray 'magnetic_tmi' (y: 1212, x: 599)> Size: 6MB
[725988 values with dtype=float64]
Coordinates:
* y (y) float64 10kB 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05
* x (x) float64 5kB 2.928e+05 2.934e+05 ... 6.51e+05 6.516e+05
spatial_ref float64 8B ...
Attributes:
system_couplet: passive_scalar_magnetometer
standard_name: total_magnetic_intensity
long_name: Total magnetic intensity, diurnally corrected and filtered
units: nT
valid_range: [-17504.6640625 11490.32324219]
grid_mapping: spatial_ref
Plotting Examples
demonstrating different ways to access and plot variables
# Make a scatter plot of a specific tabular variable, using GSPy's plotter
plt.figure()
new_survey['data']['raw_data'].gs.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['derived_maps/maps']['magnetic_tmi'].plot(cmap='jet', robust=True)
plt.show()
Total running time of the script: (0 minutes 4.706 seconds)