SEG-Y to Vector DataFrames and Back¶

The connection of segysak to xarray greatly simplifies the process of vectorising segy 3D data and returning it to SEGY. To do this, one can use the close relationship between pandas and xarray.

Loading Data¶

We start by loading data normally using the segy_loader utility. For this example we will use the Volve example sub-cube.

In [2]:

import pathlib
import xarray as xr
from IPython.display import display

volve_3d_path = pathlib.Path("data/volve10r12-full-twt-sub3d.sgy")
print("3D", volve_3d_path.exists())

volve_3d = xr.open_dataset(volve_3d_path, dim_byte_fields={'iline': 5, 'xline': 21}, extra_byte_fields={'cdp_x': 73, 'cdp_y': 77})

import pathlib import xarray as xr from IPython.display import display volve_3d_path = pathlib.Path("data/volve10r12-full-twt-sub3d.sgy") print("3D", volve_3d_path.exists()) volve_3d = xr.open_dataset(volve_3d_path, dim_byte_fields={'iline': 5, 'xline': 21}, extra_byte_fields={'cdp_x': 73, 'cdp_y': 77})

3D True

Vectorisation¶

Once the data is loaded it can be converted to a pandas.DataFrame directly from the loaded Dataset. The Dataframe is multi-index and contains columns for each variable in the originally loaded dataset. This includes the seismic amplitude as data and the cdp_x and cdp_y locations. If you require smaller volumes from the input data, you can use xarray selection methods prior to conversion to a DataFrame.

In [3]:

volve_3d_df = volve_3d.to_dataframe()
display(volve_3d_df)

volve_3d_df = volve_3d.to_dataframe() display(volve_3d_df)

			cdp_x	cdp_y	data
iline	xline	samples
10090	2150	4.0	43640052	647744704	0.020575
		8.0	43640052	647744704	0.022041
		12.0	43640052	647744704	0.019659
		16.0	43640052	647744704	0.025421
		20.0	43640052	647744704	0.025436
...	...	...	...	...	...
10150	2351	3384.0	43414413	647878266	0.000000
		3388.0	43414413	647878266	0.000000
		3392.0	43414413	647878266	0.000000
		3396.0	43414413	647878266	0.000000
		3400.0	43414413	647878266	0.000000

10473700 rows × 3 columns

We can remove the multi-index by resetting the index of the DataFrame. Vectorized workflows such as machine learning can then be easily applied to the DataFrame.

In [4]:

volve_3d_df_reindex = volve_3d_df.reset_index()
display(volve_3d_df_reindex)

volve_3d_df_reindex = volve_3d_df.reset_index() display(volve_3d_df_reindex)

	iline	xline	samples	cdp_x	cdp_y	data
0	10090	2150	4.0	43640052	647744704	0.020575
1	10090	2150	8.0	43640052	647744704	0.022041
2	10090	2150	12.0	43640052	647744704	0.019659
3	10090	2150	16.0	43640052	647744704	0.025421
4	10090	2150	20.0	43640052	647744704	0.025436
...	...	...	...	...	...	...
10473695	10150	2351	3384.0	43414413	647878266	0.000000
10473696	10150	2351	3388.0	43414413	647878266	0.000000
10473697	10150	2351	3392.0	43414413	647878266	0.000000
10473698	10150	2351	3396.0	43414413	647878266	0.000000
10473699	10150	2351	3400.0	43414413	647878266	0.000000

10473700 rows × 6 columns

Return to Xarray¶

It is possible to return the DataFrame to the Dataset for output to SEGY. To do this the multi-index must be reset. Afterward, pandas provides the to_xarray method.

In [5]:

volve_3d_df_multi = volve_3d_df_reindex.set_index(["iline", "xline", "samples"])
display(volve_3d_df_multi)
volve_3d_ds = volve_3d_df_multi.to_xarray()
display(volve_3d_ds)

volve_3d_df_multi = volve_3d_df_reindex.set_index(["iline", "xline", "samples"]) display(volve_3d_df_multi) volve_3d_ds = volve_3d_df_multi.to_xarray() display(volve_3d_ds)

			cdp_x	cdp_y	data
iline	xline	samples
10090	2150	4.0	43640052	647744704	0.020575
		8.0	43640052	647744704	0.022041
		12.0	43640052	647744704	0.019659
		16.0	43640052	647744704	0.025421
		20.0	43640052	647744704	0.025436
...	...	...	...	...	...
10150	2351	3384.0	43414413	647878266	0.000000
		3388.0	43414413	647878266	0.000000
		3392.0	43414413	647878266	0.000000
		3396.0	43414413	647878266	0.000000
		3400.0	43414413	647878266	0.000000

10473700 rows × 3 columns

<xarray.Dataset> Size: 126MB
Dimensions:  (iline: 61, xline: 202, samples: 850)
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
Data variables:
    cdp_x    (iline, xline, samples) int32 42MB 43640052 43640052 ... 43414413
    cdp_y    (iline, xline, samples) int32 42MB 647744704 ... 647878266
    data     (iline, xline, samples) float32 42MB 0.02057 0.02204 ... 0.0 0.0

xarray.Dataset

• Dimensions:
  • iline: 61
  • xline: 202
  • samples: 850
• Coordinates: (3)
  • iline
    (iline)
    int16
    10090 10091 10092 ... 10149 10150

    array([10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099,
           10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109,
           10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119,
           10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129,
           10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139,
           10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149,
           10150], dtype=int16)

  • xline
    (xline)
    int16
    2150 2151 2152 ... 2349 2350 2351

    array([2150, 2151, 2152, ..., 2349, 2350, 2351], dtype=int16)

  • samples
    (samples)
    float32
    4.0 8.0 12.0 ... 3.396e+03 3.4e+03

    array([   4.,    8.,   12., ..., 3392., 3396., 3400.], dtype=float32)

• Data variables: (3)
  • cdp_x
    (iline, xline, samples)
    int32
    43640052 43640052 ... 43414413

    array([[[43640052, 43640052, 43640052, ..., 43640052, 43640052,
             43640052],
            [43638839, 43638839, 43638839, ..., 43638839, 43638839,
             43638839],
            [43637626, 43637626, 43637626, ..., 43637626, 43637626,
             43637626],
            ...,
            [43398692, 43398692, 43398692, ..., 43398692, 43398692,
             43398692],
            [43397480, 43397480, 43397480, ..., 43397480, 43397480,
             43397480],
            [43396267, 43396267, 43396267, ..., 43396267, 43396267,
             43396267]],
    
           [[43640354, 43640354, 43640354, ..., 43640354, 43640354,
             43640354],
            [43639141, 43639141, 43639141, ..., 43639141, 43639141,
             43639141],
            [43637928, 43637928, 43637928, ..., 43637928, 43637928,
             43637928],
    ...
            [43416536, 43416536, 43416536, ..., 43416536, 43416536,
             43416536],
            [43415323, 43415323, 43415323, ..., 43415323, 43415323,
             43415323],
            [43414110, 43414110, 43414110, ..., 43414110, 43414110,
             43414110]],
    
           [[43658198, 43658198, 43658198, ..., 43658198, 43658198,
             43658198],
            [43656985, 43656985, 43656985, ..., 43656985, 43656985,
             43656985],
            [43655772, 43655772, 43655772, ..., 43655772, 43655772,
             43655772],
            ...,
            [43416839, 43416839, 43416839, ..., 43416839, 43416839,
             43416839],
            [43415626, 43415626, 43415626, ..., 43415626, 43415626,
             43415626],
            [43414413, 43414413, 43414413, ..., 43414413, 43414413,
             43414413]]], dtype=int32)

  • cdp_y
    (iline, xline, samples)
    int32
    647744704 647744704 ... 647878266

    array([[[647744704, 647744704, 647744704, ..., 647744704, 647744704,
             647744704],
            [647745006, 647745006, 647745006, ..., 647745006, 647745006,
             647745006],
            [647745309, 647745309, 647745309, ..., 647745309, 647745309,
             647745309],
            ...,
            [647804889, 647804889, 647804889, ..., 647804889, 647804889,
             647804889],
            [647805192, 647805192, 647805192, ..., 647805192, 647805192,
             647805192],
            [647805494, 647805494, 647805494, ..., 647805494, 647805494,
             647805494]],
    
           [[647745917, 647745917, 647745917, ..., 647745917, 647745917,
             647745917],
            [647746219, 647746219, 647746219, ..., 647746219, 647746219,
             647746219],
            [647746522, 647746522, 647746522, ..., 647746522, 647746522,
             647746522],
    ...
            [647876448, 647876448, 647876448, ..., 647876448, 647876448,
             647876448],
            [647876751, 647876751, 647876751, ..., 647876751, 647876751,
             647876751],
            [647877053, 647877053, 647877053, ..., 647877053, 647877053,
             647877053]],
    
           [[647817476, 647817476, 647817476, ..., 647817476, 647817476,
             647817476],
            [647817778, 647817778, 647817778, ..., 647817778, 647817778,
             647817778],
            [647818081, 647818081, 647818081, ..., 647818081, 647818081,
             647818081],
            ...,
            [647877661, 647877661, 647877661, ..., 647877661, 647877661,
             647877661],
            [647877963, 647877963, 647877963, ..., 647877963, 647877963,
             647877963],
            [647878266, 647878266, 647878266, ..., 647878266, 647878266,
             647878266]]], dtype=int32)

  • data
    (iline, xline, samples)
    float32
    0.02057 0.02204 0.01966 ... 0.0 0.0

    array([[[ 2.05745399e-02,  2.20407024e-02,  1.96589418e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 1.23256370e-02,  1.59417503e-02,  1.38800517e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-1.63372169e-05,  4.92771342e-03,  3.24293785e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            ...,
            [ 5.47898412e-02,  5.22681139e-02,  5.00054434e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 6.27492070e-02,  6.00568764e-02,  5.75365834e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 6.91987872e-02,  6.67222738e-02,  6.40410781e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
    
           [[-3.46487835e-02, -3.38801444e-02, -3.20093483e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-4.10056897e-02, -4.02579159e-02, -3.83855253e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-5.01492284e-02, -4.94211540e-02, -4.75146063e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
    ...
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-8.89039040e-03, -9.03325155e-03, -8.77474993e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-8.80905986e-03, -9.18869302e-03, -8.81006569e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
    
           [[ 2.17335932e-02,  1.93170868e-02,  2.07530260e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 2.12477669e-02,  1.89855732e-02,  2.04903930e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 2.13435888e-02,  1.91475898e-02,  2.07546689e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            ...,
            [ 6.19068742e-03,  5.15908375e-03,  6.23933598e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 7.53349811e-03,  6.49160147e-03,  7.51641765e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 9.05360654e-03,  7.92511553e-03,  8.82008299e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],
          dtype=float32)

• Indexes: (3)
  • iline
    PandasIndex

    PandasIndex(Index([10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099,
           10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109,
           10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119,
           10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129,
           10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139,
           10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149,
           10150],
          dtype='int16', name='iline'))

  • xline
    PandasIndex

    PandasIndex(Index([2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159,
           ...
           2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351],
          dtype='int16', name='xline', length=202))

  • samples
    PandasIndex

    PandasIndex(Index([   4.0,    8.0,   12.0,   16.0,   20.0,   24.0,   28.0,   32.0,   36.0,
             40.0,
           ...
           3364.0, 3368.0, 3372.0, 3376.0, 3380.0, 3384.0, 3388.0, 3392.0, 3396.0,
           3400.0],
          dtype='float32', name='samples', length=850))

• Attributes: (0)

The resulting dataset requires some changes to make it compatible again for export to SEGY. Firstly, the attributes need to be set. The simplest way is to copy these from the original SEG-Y input. Otherwise they can be set manually. segysak specifically needs the sample_rate and the coord_scalar attributes.

In [6]:

volve_3d_ds.attrs = volve_3d.attrs
display(volve_3d_ds.attrs)

volve_3d_ds.attrs = volve_3d.attrs display(volve_3d_ds.attrs)

{'seisnc': '{"coord_scalar": -100.0, "coord_scaled": false}'}

The cdp_x and cdp_y positions must be reduced to 2D along the vertical axis "twt" and set as coordinates.

In [7]:

volve_3d_ds["cdp_x"]

volve_3d_ds["cdp_x"]

Out[7]:

<xarray.DataArray 'cdp_x' (iline: 61, xline: 202, samples: 850)> Size: 42MB
array([[[43640052, 43640052, 43640052, ..., 43640052, 43640052,
         43640052],
        [43638839, 43638839, 43638839, ..., 43638839, 43638839,
         43638839],
        [43637626, 43637626, 43637626, ..., 43637626, 43637626,
         43637626],
        ...,
        [43398692, 43398692, 43398692, ..., 43398692, 43398692,
         43398692],
        [43397480, 43397480, 43397480, ..., 43397480, 43397480,
         43397480],
        [43396267, 43396267, 43396267, ..., 43396267, 43396267,
         43396267]],

       [[43640354, 43640354, 43640354, ..., 43640354, 43640354,
         43640354],
        [43639141, 43639141, 43639141, ..., 43639141, 43639141,
         43639141],
        [43637928, 43637928, 43637928, ..., 43637928, 43637928,
         43637928],
...
        [43416536, 43416536, 43416536, ..., 43416536, 43416536,
         43416536],
        [43415323, 43415323, 43415323, ..., 43415323, 43415323,
         43415323],
        [43414110, 43414110, 43414110, ..., 43414110, 43414110,
         43414110]],

       [[43658198, 43658198, 43658198, ..., 43658198, 43658198,
         43658198],
        [43656985, 43656985, 43656985, ..., 43656985, 43656985,
         43656985],
        [43655772, 43655772, 43655772, ..., 43655772, 43655772,
         43655772],
        ...,
        [43416839, 43416839, 43416839, ..., 43416839, 43416839,
         43416839],
        [43415626, 43415626, 43415626, ..., 43415626, 43415626,
         43415626],
        [43414413, 43414413, 43414413, ..., 43414413, 43414413,
         43414413]]], dtype=int32)
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

xarray.DataArray

'cdp_x'

• iline: 61
• xline: 202
• samples: 850

• 43640052 43640052 43640052 43640052 ... 43414413 43414413 43414413

  array([[[43640052, 43640052, 43640052, ..., 43640052, 43640052,
           43640052],
          [43638839, 43638839, 43638839, ..., 43638839, 43638839,
           43638839],
          [43637626, 43637626, 43637626, ..., 43637626, 43637626,
           43637626],
          ...,
          [43398692, 43398692, 43398692, ..., 43398692, 43398692,
           43398692],
          [43397480, 43397480, 43397480, ..., 43397480, 43397480,
           43397480],
          [43396267, 43396267, 43396267, ..., 43396267, 43396267,
           43396267]],
  
         [[43640354, 43640354, 43640354, ..., 43640354, 43640354,
           43640354],
          [43639141, 43639141, 43639141, ..., 43639141, 43639141,
           43639141],
          [43637928, 43637928, 43637928, ..., 43637928, 43637928,
           43637928],
  ...
          [43416536, 43416536, 43416536, ..., 43416536, 43416536,
           43416536],
          [43415323, 43415323, 43415323, ..., 43415323, 43415323,
           43415323],
          [43414110, 43414110, 43414110, ..., 43414110, 43414110,
           43414110]],
  
         [[43658198, 43658198, 43658198, ..., 43658198, 43658198,
           43658198],
          [43656985, 43656985, 43656985, ..., 43656985, 43656985,
           43656985],
          [43655772, 43655772, 43655772, ..., 43655772, 43655772,
           43655772],
          ...,
          [43416839, 43416839, 43416839, ..., 43416839, 43416839,
           43416839],
          [43415626, 43415626, 43415626, ..., 43415626, 43415626,
           43415626],
          [43414413, 43414413, 43414413, ..., 43414413, 43414413,
           43414413]]], dtype=int32)

• Coordinates: (3)
  • iline
    (iline)
    int16
    10090 10091 10092 ... 10149 10150

    array([10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099,
           10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109,
           10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119,
           10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129,
           10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139,
           10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149,
           10150], dtype=int16)

  • xline
    (xline)
    int16
    2150 2151 2152 ... 2349 2350 2351

    array([2150, 2151, 2152, ..., 2349, 2350, 2351], dtype=int16)

  • samples
    (samples)
    float32
    4.0 8.0 12.0 ... 3.396e+03 3.4e+03

    array([   4.,    8.,   12., ..., 3392., 3396., 3400.], dtype=float32)

• Indexes: (3)
  • iline
    PandasIndex

    PandasIndex(Index([10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099,
           10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109,
           10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119,
           10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129,
           10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139,
           10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149,
           10150],
          dtype='int16', name='iline'))

  • xline
    PandasIndex

    PandasIndex(Index([2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159,
           ...
           2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351],
          dtype='int16', name='xline', length=202))

  • samples
    PandasIndex

    PandasIndex(Index([   4.0,    8.0,   12.0,   16.0,   20.0,   24.0,   28.0,   32.0,   36.0,
             40.0,
           ...
           3364.0, 3368.0, 3372.0, 3376.0, 3380.0, 3384.0, 3388.0, 3392.0, 3396.0,
           3400.0],
          dtype='float32', name='samples', length=850))

• Attributes: (0)

In [8]:

volve_3d_ds["cdp_x"] = volve_3d_ds["cdp_x"].mean(dim=["samples"])
volve_3d_ds["cdp_y"] = volve_3d_ds["cdp_y"].mean(dim=["samples"])
volve_3d_ds = volve_3d_ds.set_coords(["cdp_x", "cdp_y"])
volve_3d_ds

volve_3d_ds["cdp_x"] = volve_3d_ds["cdp_x"].mean(dim=["samples"]) volve_3d_ds["cdp_y"] = volve_3d_ds["cdp_y"].mean(dim=["samples"]) volve_3d_ds = volve_3d_ds.set_coords(["cdp_x", "cdp_y"]) volve_3d_ds

Out[8]:

<xarray.Dataset> Size: 42MB
Dimensions:  (iline: 61, xline: 202, samples: 850)
Coordinates:
    cdp_x    (iline, xline) float64 99kB 4.364e+07 4.364e+07 ... 4.341e+07
    cdp_y    (iline, xline) float64 99kB 6.477e+08 6.477e+08 ... 6.479e+08
  * 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
Data variables:
    data     (iline, xline, samples) float32 42MB 0.02057 0.02204 ... 0.0 0.0
Attributes:
    seisnc:   {"coord_scalar": -100.0, "coord_scaled": false}

xarray.Dataset

• Dimensions:
  • iline: 61
  • xline: 202
  • samples: 850
• Coordinates: (5)
  • cdp_x
    (iline, xline)
    float64
    4.364e+07 4.364e+07 ... 4.341e+07

    array([[43640052., 43638839., 43637626., ..., 43398692., 43397480.,
            43396267.],
           [43640354., 43639141., 43637928., ..., 43398995., 43397782.,
            43396569.],
           [43640657., 43639444., 43638231., ..., 43399297., 43398084.,
            43396872.],
           ...,
           [43657593., 43656380., 43655167., ..., 43416234., 43415021.,
            43413808.],
           [43657895., 43656683., 43655470., ..., 43416536., 43415323.,
            43414110.],
           [43658198., 43656985., 43655772., ..., 43416839., 43415626.,
            43414413.]])

  • cdp_y
    (iline, xline)
    float64
    6.477e+08 6.477e+08 ... 6.479e+08

    array([[6.47744704e+08, 6.47745006e+08, 6.47745309e+08, ...,
            6.47804889e+08, 6.47805192e+08, 6.47805494e+08],
           [6.47745917e+08, 6.47746219e+08, 6.47746522e+08, ...,
            6.47806102e+08, 6.47806405e+08, 6.47806707e+08],
           [6.47747130e+08, 6.47747432e+08, 6.47747735e+08, ...,
            6.47807315e+08, 6.47807617e+08, 6.47807920e+08],
           ...,
           [6.47815050e+08, 6.47815352e+08, 6.47815655e+08, ...,
            6.47875235e+08, 6.47875538e+08, 6.47875840e+08],
           [6.47816263e+08, 6.47816565e+08, 6.47816868e+08, ...,
            6.47876448e+08, 6.47876751e+08, 6.47877053e+08],
           [6.47817476e+08, 6.47817778e+08, 6.47818081e+08, ...,
            6.47877661e+08, 6.47877963e+08, 6.47878266e+08]])

  • iline
    (iline)
    int16
    10090 10091 10092 ... 10149 10150

    array([10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099,
           10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109,
           10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119,
           10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129,
           10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139,
           10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149,
           10150], dtype=int16)

  • xline
    (xline)
    int16
    2150 2151 2152 ... 2349 2350 2351

    array([2150, 2151, 2152, ..., 2349, 2350, 2351], dtype=int16)

  • samples
    (samples)
    float32
    4.0 8.0 12.0 ... 3.396e+03 3.4e+03

    array([   4.,    8.,   12., ..., 3392., 3396., 3400.], dtype=float32)

• Data variables: (1)
  • data
    (iline, xline, samples)
    float32
    0.02057 0.02204 0.01966 ... 0.0 0.0

    array([[[ 2.05745399e-02,  2.20407024e-02,  1.96589418e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 1.23256370e-02,  1.59417503e-02,  1.38800517e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-1.63372169e-05,  4.92771342e-03,  3.24293785e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            ...,
            [ 5.47898412e-02,  5.22681139e-02,  5.00054434e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 6.27492070e-02,  6.00568764e-02,  5.75365834e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 6.91987872e-02,  6.67222738e-02,  6.40410781e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
    
           [[-3.46487835e-02, -3.38801444e-02, -3.20093483e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-4.10056897e-02, -4.02579159e-02, -3.83855253e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-5.01492284e-02, -4.94211540e-02, -4.75146063e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
    ...
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-8.89039040e-03, -9.03325155e-03, -8.77474993e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [-8.80905986e-03, -9.18869302e-03, -8.81006569e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
    
           [[ 2.17335932e-02,  1.93170868e-02,  2.07530260e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 2.12477669e-02,  1.89855732e-02,  2.04903930e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 2.13435888e-02,  1.91475898e-02,  2.07546689e-02, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            ...,
            [ 6.19068742e-03,  5.15908375e-03,  6.23933598e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 7.53349811e-03,  6.49160147e-03,  7.51641765e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
            [ 9.05360654e-03,  7.92511553e-03,  8.82008299e-03, ...,
              0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],
          dtype=float32)

• Indexes: (3)
  • iline
    PandasIndex

    PandasIndex(Index([10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099,
           10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109,
           10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119,
           10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129,
           10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139,
           10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149,
           10150],
          dtype='int16', name='iline'))

  • xline
    PandasIndex

    PandasIndex(Index([2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159,
           ...
           2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351],
          dtype='int16', name='xline', length=202))

  • samples
    PandasIndex

    PandasIndex(Index([   4.0,    8.0,   12.0,   16.0,   20.0,   24.0,   28.0,   32.0,   36.0,
             40.0,
           ...
           3364.0, 3368.0, 3372.0, 3376.0, 3380.0, 3384.0, 3388.0, 3392.0, 3396.0,
           3400.0],
          dtype='float32', name='samples', length=850))

• Attributes: (1)

  seisnc :

  {"coord_scalar": -100.0, "coord_scaled": false}

Afterwards, use the to_segy method as normal to return to SEGY.

In [9]:

volve_3d_ds.seisio.to_segy("data/test.segy", iline=189, xline=193, trace_header_map={'cdp_x':181, 'cdp_y':185})

volve_3d_ds.seisio.to_segy("data/test.segy", iline=189, xline=193, trace_header_map={'cdp_x':181, 'cdp_y':185})

Very large datasets¶

If you have a very large dataset (SEG-Y file), it may be possible to use ds.to_dask_dataframe() which can perform operations, including the writing of data in a lazy manner.