Alternating iterative tie line levelling

7. Alternating iterative tie line levelling#

[1]:

%load_ext autoreload
%autoreload 2


import logging

import geopandas as gpd
import pandas as pd
import plotly.io as pio

import airbornegeo

logging.getLogger("airbornegeo").setLevel("INFO")
logging.basicConfig()
pio.renderers.default = "notebook"

/home/airbornegeo/airbornegeo/.pixi/envs/default/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

7.1. Load data#

This is a subset of the BAS AGAP survey over Antarctica’s Gamburtsev Subglacial Mountains. The file is download and subset in the notebook AGAP_magnetic_survey, and the BAS processing steps are repeated in the notebook processing_AGAP_magnetic_survey.

[2]:

data_df = pd.read_csv("data/AGAP_magnetic_survey_processed_blocked.csv")
data_df = data_df[
    [
        "easting",
        "northing",
        "height",
        "line",
        "unixtime",
        "distance_along_line",
        "mag",
    ]
]

# for testing limit number of lines
data_df = data_df[~data_df.line.between(133, 142)]
data_df = data_df[~data_df.line.between(168, 176)]
data_df = data_df[
    (data_df.line.isin(data_df.line.unique()[::2])) | (data_df.line >= 143)
]

# define flight lines vs tie lines with column 'tie' which is True or False
data_df["tie"] = False
data_df.loc[data_df.line >= 142, "tie"] = True

data_df.head()

[2]:
easting northing height line unixtime distance_along_line mag tie
0 621099.093988 159056.748193 4110.45 1 1.229500e+09 27.181565 -33.385 False
1 621206.517953 159071.301512 4114.50 1 1.229500e+09 135.587530 -35.120 False
2 621313.794221 159085.113599 4117.90 1 1.229500e+09 243.749367 -36.875 False
3 621420.722903 159099.184618 4120.80 1 1.229500e+09 351.600337 -38.585 False
4 621527.158177 159114.303863 4123.15 1 1.229500e+09 459.104857 -40.205 False

7.2. Find intersections#

[3]:

# convert dataframe into geodataframe
data_df = gpd.GeoDataFrame(
    data_df,
    geometry=gpd.points_from_xy(data_df.easting, data_df.northing),
    crs="EPSG:3031",
)

[4]:

# calculate theoretical intersection points
inters = airbornegeo.create_intersection_table(data_df)
inters

Line/tie combinations: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3630/3630 [00:00<00:00, 7629.54it/s]
Potential intersections: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 670/670 [00:06<00:00, 110.66it/s]
INFO:airbornegeo:found 662 intersections

[4]:
line tie geometry max_dist easting northing
0 1 143 POINT (1158153 254083) 16.441255 1158153.0 254083.0
1 1 144 POINT (1190820 259865) 24.124887 1190820.0 259865.0
2 1 145 POINT (1223524 265689) 17.269650 1223524.0 265689.0
3 1 146 POINT (1256193 271497) 31.608179 1256193.0 271497.0
4 1 147 POINT (1288901 277249) 50.174377 1288901.0 277249.0
... ... ... ... ... ... ...
657 125 199 POINT (1318165 302398) 33.689493 1318165.0 302398.0
658 125 200 POINT (1350929 308239) 36.343660 1350929.0 308239.0
659 127 148 POINT (1319894 292699) 22.093103 1319894.0 292699.0
660 127 199 POINT (1319922 292702) 23.297236 1319922.0 292702.0
661 127 200 POINT (1352642 298507) 36.743873 1352642.0 298507.0

662 rows Γ— 6 columns

_images/alternating_iterative_tie_line_levelling_6_4.png

7.3. Add intersections as rows to the dataframe#

[5]:

data_df, inters = airbornegeo.interpolate_intersections(
    data_df,
    inters,
    to_interp=["mag", "height"],
    window_width=500,
    method="cubic",
    extrapolate=False,
)

Line 206: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 121/121 [00:22<00:00,  5.28it/s]

[6]:

airbornegeo.lines_without_intersections(data_df, inters)

[6]:

[np.int64(107),
 np.int64(109),
 np.int64(129),
 np.int64(131),
 np.int64(188),
 np.int64(189),
 np.int64(190),
 np.int64(192),
 np.int64(193),
 np.int64(194),
 np.int64(203)]

7.4. Calculate initial cross-over errors#

[7]:

inters = airbornegeo.calculate_crossover_errors(
    data_df,
    inters,
    data_col="mag",
    plot_map=True,
)
_images/alternating_iterative_tie_line_levelling_11_1.png 
[8]:

inters.head()

[8]:
line tie geometry max_dist easting northing dist_along_flight_line dist_along_flight_tie flight_interpolation_type tie_interpolation_type flight_height tie_height mistie_0
0 1 143 POINT (1158153 254083) 16.441255 1158153.0 254083.0 545768.072695 138071.419338 interpolated interpolated 4168.038983 4008.397691 100.458661
1 1 144 POINT (1190820 259865) 24.124887 1190820.0 259865.0 578952.394781 131585.125284 interpolated interpolated 4174.500813 4010.840823 32.151483
2 1 145 POINT (1223524 265689) 17.269650 1223524.0 265689.0 612181.972720 147253.496664 interpolated interpolated 3544.149925 3995.961216 72.936553
3 1 146 POINT (1256193 271497) 31.608179 1256193.0 271497.0 645372.035087 136211.666619 interpolated interpolated 3564.579994 3578.174277 84.125118
4 1 147 POINT (1288901 277249) 50.174377 1288901.0 277249.0 678598.170740 155115.224990 interpolated interpolated 3536.160719 3545.947314 -7.731396

7.5. Alternative between levelling lines to ties, and ties to lines#

This will perform up to 5 iterations where each iteration first levels the lines to the ties, then the ties to the lines. Since each iteation performs two instances of leveling, there will be up to 10 new misties columns in the intersection table. If the misties values dont decrease, or begin increasing, the iterations will stop.

[9]:

data_df, inters = airbornegeo.alternating_iterative_line_levelling(
    data_df,
    inters,
    data_col="mag",
    levelled_col="mag_levelled_trend0",
    degree=0,
    iterations=5,
)
inters.head()

Iteration: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 5/5 [01:06<00:00, 13.24s/it]

[9]:
line tie geometry max_dist easting northing dist_along_flight_line dist_along_flight_tie flight_interpolation_type tie_interpolation_type ... mistie_1 mistie_2 mistie_3 mistie_4 mistie_5 mistie_6 mistie_7 mistie_8 mistie_9 mistie_10
0 1 143 POINT (1158153 254083) 16.441255 1158153.0 254083.0 545768.072695 138071.419338 interpolated interpolated ... 96.945621 72.958517 71.904752 67.038524 67.150418 63.393096 63.723800 60.721570 61.069625 58.628182
1 1 144 POINT (1190820 259865) 24.124887 1190820.0 259865.0 578952.394781 131585.125284 interpolated interpolated ... 28.638444 19.088634 18.034870 13.168642 13.280535 9.523214 9.853917 6.851688 7.199742 4.758299
2 1 145 POINT (1223524 265689) 17.269650 1223524.0 265689.0 612181.972720 147253.496664 interpolated interpolated ... 69.423514 49.368210 48.314446 43.448218 43.560111 39.802790 40.133493 37.131264 37.479318 35.037876
3 1 146 POINT (1256193 271497) 31.608179 1256193.0 271497.0 645372.035087 136211.666619 interpolated interpolated ... 80.612078 60.446633 59.392868 54.526640 54.638534 50.881212 51.211916 48.209686 48.557741 46.116298
4 1 147 POINT (1288901 277249) 50.174377 1288901.0 277249.0 678598.170740 155115.224990 interpolated interpolated ... -11.244436 -2.791611 -3.845375 -8.711603 -8.599710 -12.357032 -12.026328 -15.028558 -14.680503 -17.121946

5 rows Γ— 23 columns

[10]:

airbornegeo.plot_levelling_convergence(inters)
_images/alternating_iterative_tie_line_levelling_15_1.png 
[11]:

inters = airbornegeo.calculate_crossover_errors(
    data_df,
    inters,
    data_col="mag_levelled_trend0",
    plot_map=True,
)
_images/alternating_iterative_tie_line_levelling_16_1.png 
[12]:

airbornegeo.plotly_points(
    data_df[::10],
    color_col="mag_levelled_trend0",
)