Extract data at the intersection of a horizon and 3D volume¶
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import matplotlib.pyplot as plt
import xarray as xr
%matplotlib inline
from segysak.segy import (
get_segy_texthead,
segy_header_scan,
segy_header_scrape,
)
from os import path
import matplotlib.pyplot as plt
import xarray as xr
%matplotlib inline
from segysak.segy import (
get_segy_texthead,
segy_header_scan,
segy_header_scrape,
)
from os import path
Load Small 3D Volume from Volve¶
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volve_3d_path = path.join("data", "volve10r12-full-twt-sub3d.sgy")
print("3D", volve_3d_path, path.exists(volve_3d_path))
volve_3d_path = path.join("data", "volve10r12-full-twt-sub3d.sgy")
print("3D", volve_3d_path, path.exists(volve_3d_path))
3D data/volve10r12-full-twt-sub3d.sgy True
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get_segy_texthead(volve_3d_path)
get_segy_texthead(volve_3d_path)
Out[4]:
Text Header
C 1 SEGY OUTPUT FROM Petrel 2017.2 Saturday, June 06 2020 10:15:00
C 2 Name: ST10010ZDC12-PZ-PSDM-KIRCH-FULL-T.MIG_FIN.POST_STACK.3D.JS-017534 ÝCro
C 3
C 4 First inline: 10090 Last inline: 10150
C 5 First xline: 2150 Last xline: 2351
C 6 CRS: ED50-UTM31 ("MENTOR:ED50-UTM31:European 1950 Based UTM, Zone 31 North,
C 7 X min: 433955.09 max: 436589.56 delta: 2634.47
C 8 Y min: 6477439.46 max: 6478790.23 delta: 1350.77
C 9 Time min: -3402.00 max: -2.00 delta: 3400.00
C10 Lat min: 58.25'52.8804"N max: 58.26'37.9493"N delta: 0.00'45.0689"
C11 Long min: 1.52'7.1906"E max: 1.54'50.9616"E delta: 0.02'43.7710"
C12 Trace min: -3400.00 max: -4.00 delta: 3396.00
C13 Seismic (template) min: -58.55 max: 54.55 delta: 113.10
C14 Amplitude (data) min: -58.55 max: 54.55 delta: 113.10
C15 Trace sample format: IEEE floating point
C16 Coordinate scale factor: 100.00000
C17
C18 Binary header locations:
C19 Sample interval : bytes 17-18
C20 Number of samples per trace : bytes 21-22
C21 Trace date format : bytes 25-26
C22
C23 Trace header locations:
C24 Inline number : bytes 5-8
C25 Xline number : bytes 21-24
C26 Coordinate scale factor : bytes 71-72
C27 X coordinate : bytes 73-76
C28 Y coordinate : bytes 77-80
C29 Trace start time/depth : bytes 109-110
C30 Number of samples per trace : bytes 115-116
C31 Sample interval : bytes 117-118
C32
C33
C34
C35
C36
C37
C38
C39
C40 END EBCDIC
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volve_3d = xr.open_dataset(
volve_3d_path,
dim_byte_fields={"iline": 5, "xline": 21},
extra_byte_fields={"cdp_x": 73, "cdp_y": 77},
)
volve_3d.segysak.scale_coords()
volve_3d.data
volve_3d = xr.open_dataset(
volve_3d_path,
dim_byte_fields={"iline": 5, "xline": 21},
extra_byte_fields={"cdp_x": 73, "cdp_y": 77},
)
volve_3d.segysak.scale_coords()
volve_3d.data
Out[5]:
<xarray.DataArray 'data' (iline: 61, xline: 202, samples: 850)> Size: 42MB [10473700 values with dtype=float32] Coordinates: * iline (iline) int16 122B 10090 10091 10092 10093 ... 10148 10149 10150 * xline (xline) int16 404B 2150 2151 2152 2153 2154 ... 2348 2349 2350 2351 * samples (samples) float32 3kB 4.0 8.0 12.0 ... 3.392e+03 3.396e+03 3.4e+03 Attributes: seisnc: {"source_file": "data/volve10r12-full-twt-sub3d.sgy", "measurem... text: C 1 SEGY OUTPUT FROM Petrel 2017.2 Saturday, June 06 2020 10:15...
Load up horizon data¶
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top_hugin_path = path.join("data", "hor_twt_hugin_fm_top.dat")
print("Top Hugin", top_hugin_path, path.exists(top_hugin_path))
top_hugin_path = path.join("data", "hor_twt_hugin_fm_top.dat")
print("Top Hugin", top_hugin_path, path.exists(top_hugin_path))
Top Hugin data/hor_twt_hugin_fm_top.dat True
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import pandas as pd
top_hugin_df = pd.read_csv(top_hugin_path, names=["cdp_x", "cdp_y", "samples"], sep=" ")
top_hugin_df.head()
import pandas as pd
top_hugin_df = pd.read_csv(top_hugin_path, names=["cdp_x", "cdp_y", "samples"], sep=" ")
top_hugin_df.head()
Out[7]:
cdp_x | cdp_y | samples | |
---|---|---|---|
0 | 432186.713151 | 6.477029e+06 | 2776.275147 |
1 | 432189.737524 | 6.477041e+06 | 2779.657715 |
2 | 432192.761898 | 6.477053e+06 | 2780.465088 |
3 | 432195.786271 | 6.477066e+06 | 2780.949951 |
4 | 432198.810645 | 6.477078e+06 | 2781.769775 |
Would be good to plot a seismic (iline,xline) section in Pyvista as well
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# import pyvista as pv
# point_cloud = pv.PolyData(-1*top_hugin_df.to_numpy(), cmap='viridis')
# point_cloud.plot(eye_dome_lighting=True)
# import pyvista as pv
# point_cloud = pv.PolyData(-1*top_hugin_df.to_numpy(), cmap='viridis')
# point_cloud.plot(eye_dome_lighting=True)
Alternativey we can use the points to output a xarray.Dataset
which comes with coordinates for plotting already gridded up for Pyvista.
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top_hugin_ds = volve_3d.seis.surface_from_points(
top_hugin_df, "samples", right=("cdp_x", "cdp_y")
)
top_hugin_ds
top_hugin_ds = volve_3d.seis.surface_from_points(
top_hugin_df, "samples", right=("cdp_x", "cdp_y")
)
top_hugin_ds
Out[9]:
<xarray.Dataset> Size: 99kB Dimensions: (iline: 61, xline: 202) Coordinates: * iline (iline) int16 122B 10090 10091 10092 10093 ... 10148 10149 10150 * xline (xline) int16 404B 2150 2151 2152 2153 2154 ... 2348 2349 2350 2351 samples (iline, xline) float64 99kB 2.741e+03 2.742e+03 ... 2.635e+03 Data variables: *empty* Attributes: seisnc: {"coord_scalar": -100.0, "coord_scaled": true}
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# the twt values from the points now in a form we can relate to the xarray cube.
plt.imshow(top_hugin_ds.samples)
# the twt values from the points now in a form we can relate to the xarray cube.
plt.imshow(top_hugin_ds.samples)
Out[10]:
<matplotlib.image.AxesImage at 0x7f8b2f6662f0>
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# point_cloud = pv.StructuredGrid(
# top_hugin_ds.cdp_x.values,
# top_hugin_ds.cdp_y.values,top_hugin_ds.twt.values,
# cmap='viridis')
# point_cloud.plot(eye_dome_lighting=True)
# point_cloud = pv.StructuredGrid(
# top_hugin_ds.cdp_x.values,
# top_hugin_ds.cdp_y.values,top_hugin_ds.twt.values,
# cmap='viridis')
# point_cloud.plot(eye_dome_lighting=True)
Horizon Amplitude Extraction¶
Extracting horizon amplitudes requires us to interpolate the cube onto the 3D horizon.
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top_hugin_amp = volve_3d.data.interp(
{"iline": top_hugin_ds.iline, "xline": top_hugin_ds.xline, "samples": top_hugin_ds.samples}
)
top_hugin_amp = volve_3d.data.interp(
{"iline": top_hugin_ds.iline, "xline": top_hugin_ds.xline, "samples": top_hugin_ds.samples}
)
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#
fig = plt.figure(figsize=(15, 5))
top_hugin_amp.plot(cmap="bwr")
cs = plt.contour(
top_hugin_amp.xline, top_hugin_amp.iline, top_hugin_ds.samples, levels=20, colors="grey"
)
plt.clabel(cs, fontsize=14, fmt="%.1f")
#
fig = plt.figure(figsize=(15, 5))
top_hugin_amp.plot(cmap="bwr")
cs = plt.contour(
top_hugin_amp.xline, top_hugin_amp.iline, top_hugin_ds.samples, levels=20, colors="grey"
)
plt.clabel(cs, fontsize=14, fmt="%.1f")
Out[13]:
<a list of 24 text.Text objects>