segyio

Index

• Introduction
• Feature summary
• Getting started
  • Quick start
  • Get segyio
  • Build segyio
• Tutorial
  • Basics
  • Modes
  • Mode examples
• Goals
• Contributing
• Examples
• History

Introduction

Segyio is a small LGPL licensed C library for easy interaction with SEG-Y formatted seismic data, with language bindings for Python and Matlab. Segyio is an attempt to create an easy-to-use, embeddable, community-oriented library for seismic applications. Features are added as they are needed; suggestions and contributions of all kinds are very welcome.

To catch up on the latest development and features, see the changelog.

Feature summary

• A low-level C interface with few assumptions; easy to bind to other languages
• Read and write binary and textual headers
• Read and write traces and trace headers
• Simple, powerful, and native-feeling Python interface with numpy integration
• xarray integration with netcdf_segy
• Some simple applications with unix philosophy

Getting started

When segyio is built and installed, you're ready to start programming! Check out the tutorial, examples, and the example programs. For a technical reference with examples and small recipes, start your favourite Python interpreter and type help(segyio) to get started - it is written with pydoc and should integrate well with IDLE, pycharm and other Python tools.

Quick start

import segyio
import numpy as np
with segyio.open('file.sgy') as f:
    for trace in f.trace:
        filtered = trace[np.where(trace < 1e-2)]

See the examples for more.

Get segyio

A copy of segyio is available both as pre-built binaries and source code:

• In Debian unstable
  • apt install python3-segyio
• Wheels for Python from PyPI
  • pip install segyio
• Source code from github
  • git clone https://github.com/statoil/segyio
• Source code in tarballs

Build segyio

To build segyio you need:

• A C99 compatible C compiler (tested mostly on gcc and clang)
• A C++ compiler for the Python extension
• CMake version 2.8.12 or greater
• Python 2.7 or 3.x.
• numpy version 1.10 or greater
• setuptools version 28 or greater
• setuptools-scm

To build and install segyio, perform the following actions in your console:

git clone https://github.com/Statoil/segyio
mkdir segyio/build
cd segyio/build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local
make
make install

make install must be done as root for a system install; if you want to install in your home directory, add -DCMAKE_INSTALL_PREFIX=~/ or some other appropriate directory, or make DESTDIR=~/ install. Please ensure your environment picks up on non-standard install locations (PYTHONPATH, LD_LIBRARY_PATH and PATH).

If you have multiple Python installations, or want to use some alternative interpreter, you can help cmake find the right one by passing -DPYTHON_EXECUTABLE=/opt/python/binary along with install prefix and build type.

To build the matlab bindings, invoke CMake with the option -DBUILD_MEX=ON. In some environments the Matlab binaries are in a non-standard location, in which case you need to help CMake find the matlab binaries by passing -DMATLAB_ROOT=/path/to/matlab.

Developers

It's recommended to build in debug mode to get more warnings and to embed debug symbols in the objects. Substituting Debug for Release in the CMAKE_BUILD_TYPE is plenty.

Tests are located in the language/tests directories, and it's highly recommended that new features added are demonstrated for correctness and contract by adding a test. All tests can be run by invoking ctest. Feel free to use the tests already written as a guide.

After building segyio you can run the tests with ctest, executed from the build directory.

Please note that to run the Python examples you need to let your environment know where to find the Python library. It can be installed as a user, or on adding the segyio/build/python library to your pythonpath.

Tutorial

All code in this tutorial assumes segyio is imported, and that numpy is available as np.

import segyio
import numpy as np

This tutorial assumes you're familiar with Python and numpy. For a refresh, check out the python tutorial and numpy quickstart

Basics

Opening a file for reading is done with the segyio.open function, and idiomatically used with context managers. Using the with statement, files are properly closed even in the case of exceptions. By default, files are opened read-only.

with segyio.open(filename) as f:
    ...

Open accepts several options (for more a more comprehensive reference, check the open function's docstring with help(segyio.open). The most important option is the second (optional) positional argument. To open a file for reading, do segyio.open(filename, 'r+'), from the C fopen function.

Files can be opened in unstructured mode, either by passing segyio.open the optional arguments strict=False, in which case not establishing structure (inline numbers, crossline numbers etc.) is not an error, and ignore_geometry=True, in which case segyio won't even try to set these internal attributes.

The segy file object has several public attributes describing this structure:

• f.ilines Inferred inline numbers
• f.xlines Inferred crossline numbers
• f.offsets Inferred offsets numbers
• f.samples Inferred sample offsets (frequency and recording time delay)
• f.unstructured True if unstructured, False if structured
• f.ext_headers The number of extended textual headers

If the file is opened unstructured, all the line properties will will be None.

Modes

In segyio, data is retrived and written through so-called modes. Modes are abstract arrays, or addressing schemes, and change what names and indices mean. All modes are properties on the file handle object, support the len function, and reads and writes are done through f.mode[]. Writes are done with assignment. Modes support array slicing inspired by numpy. The following modes are available:

• trace

  The trace mode offers raw addressing of traces as they are laid out in the file. This, along with header, is the only mode available for unstructured files. Traces are enumerated 0..len(f.trace).

  Reading a trace yields a numpy ndarray, and reading multiple traces yields a generator of ndarrays. Generator semantics are used and the same object is reused, so if you want to cache or address trace data later, you must explicitly copy.

  >>> f.trace[10]
  >>> f.trace[-2]
  >>> f.trace[15:45]
  >>> f.trace[:45:3]

• header

  With addressing behaviour similar to trace, accessing items yield header objects instead of numpy ndarrays. Headers are dict like objects, where keys are integers, seismic unix-style keys (in segyio.su module) and segyio enums (segyio.TraceField).

  Header values can be updated by assigning a dict-like to it, and keys not present on the right-hand-side of the assignment are unmodified.

  >>> f.header[5] = { segyio.su.tracl: 10 }
  >>> f.header[5].items()
  >>> f.header[5][25, 37] # read multiple values at once

• iline, xline

  These modes will raise an error if the file is unstructured. They consider arguments to [] as the keys of the respective lines. Line numbers are always increasing, but can have arbitrary, uneven spacing. The valid names can be found in the ilines and xlines properties.

  As with traces, getting one line yields an ndarray, and a slice of lines yields a generator of ndarrays. When using slices with a step, some intermediate items might be skipped if it is not matched by the step, i.e. doing f.line[1:10:3] on a file with lines [1,2,3,4,5] is equivalent of looking up 1, 4, 7, and finding [1,4].

  When working with a 4D pre-stack file, the first offset is implicitly read. To access a different or a range of offsets, use comma separated indices or ranges, as such: f.iline[120, 4].

• fast, slow

  These are aliases for iline and xline, determined by how the traces are laid out. For inline sorted files, fast would yield iline.

• depth_slice

  The depth slice is a horizontal, file-wide cut at a depth. The yielded values are ndarrays and generators-of-arrays.

• gather

  The gather is the intersection of an inline and crossline, a vertical column of the survey, and unless a single offset is specified returns an offset x samples ndarray. In the presence of ranges, it returns a generator of such ndarrays.

• text

  The text mode is an array of the textual headers, where text[0] is the standard-mandated textual header, and 1..n are the optional extended headers.

  The text headers are returned as 3200-byte string-like blobs (bytes in Python 3, str in Python 2), as it is in the file. The segyio.tools.wrap function can create a line-oriented version of this string.

• bin

  The values of the file-wide binary header with a dict-like interface. Behaves like the header mode, but without the indexing.

Mode examples

>>> for line in f.iline[:2430]:
...     print(np.average(line))

>>> for line in f.xline[2:10]:
...     print(line)

>>> for line in f.fast[::2]:
...     print(np.min(line))

>>> for factor, offset in enumerate(f.iline[10, :]):
...     offset *= factor
        print(offset)

>>> f.gather[200, 241, :].shape

>>> text = f.text[0]
>>> type(text)
<type 'bytes'> # 'str' in Python 2

>>> f.trace[10] = np.zeros(len(f.samples))

More examples and recipes can be found in the docstrings help(segyio) and the examples section.

Project goals

Segyio does necessarily attempt to be the end-all of SEG-Y interactions; rather, we aim to lower the barrier to interacting with SEG-Y files for embedding, new applications or free-standing programs.

Additionally, the aim is not to support the full standard or all exotic (but standard compliant) formatted files out there. Some assumptions are made, such as:

• All traces in a file are assumed to be of the same size
• All samples are 4-byte floats

Currently, segyio supports:

• Post-stack 3D volumes, sorted with respect to two header words (generally INLINE and CROSSLINE)
• Pre-stack 4D volumes, sorted with respect to three header words (generally INLINE, CROSSLINE, and OFFSET)
• Unstructured data

The writing functionality in segyio is largely meant to modify or adapt files. A file created from scratch is not necessarily a to-spec SEG-Y file, as we only necessarily write the header fields segyio needs to make sense of the geometry. It is still highly recommended that SEG-Y files are maintained and written according to specification, but segyio does not enforce this.

Contributing

We welcome all kinds of contributions, including code, bug reports, issues, feature requests, and documentation. The preferred way of submitting a contribution is to either make an issue on github or by forking the project on github and making a pull request.

xarray integration

Alan Richardson has written a great little tool for using xarray with segy files, which he demos in this notebook

Reproducing the test data

Small SEG-Y formatted files are included in the repository for test purposes. The data is non-sensical and made to be predictable, and it is reproducible by using segyio. The tests file are located in the test-data directory. To reproduce the data file, build segyio and run the test program make-file.py, make-ps-file.py, and make-rotated-copies.py as such:

python examples/make-file.py small.sgy 50 1 6 20 25
python examples/make-ps-file.py small-ps.sgy 10 1 5 1 4 1 3
python examples/make-rotated-copies.py small.sgy

If you have have small data files with a free license, feel free to submit it to the project!

Examples

Python

Import useful libraries:

import segyio
import numpy as np
from shutil import copyfile

Open segy file and inspect it:

filename = 'name_of_your_file.sgy'
with segyio.open(filename, "r") as segyfile:

    # Memory map file for faster reading (especially if file is big...)
    segyfile.mmap()

    # Print binary header info
    print(segyfile.bin)
    print(segyfile.bin[segyio.BinField.Traces])

    # Read headerword inline for trace 10
    print(segyfile.header[10][segyio.TraceField.INLINE_3D])

    # Print inline and crossline axis
    print(segyfile.xlines)
    print(segyfile.ilines)

Read post-stack data cube contained in segy file:

# Read data along first xline
data = segyfile.xline[segyfile.xlines[1]]

# Read data along last iline
data = segyfile.iline[segyfile.ilines[-1]]

# Read data along 100th time slice
data = segyfile.depth_slice[100]

# Read data cube
data = segyio.tools.cube(filename)

Read pre-stack data cube contained in segy file:

filename = 'name_of_your_prestack_file.sgy'
with segyio.open(filename, "r") as segyfile:

    # Print offsets
    print(segyfile.offset)

    # Read data along first iline and offset 100:  data [nxl x nt]
    data = segyfile.iline[0, 100]

    # Read data along first iline and all offsets gath:  data [noff x nxl x nt]
    data = np.asarray([np.copy(x) for x in segyfile.iline[0:1, :]])

    # Read data along first 5 ilines and all offsets gath:  data [noff nil x nxl x nt]
    data = np.asarray([np.copy(x) for x in segyfile.iline[0:5, :]])

    # Read data along first xline and all offsets gath:  data [noff x nil x nt]
    data = np.asarray([np.copy(x) for x in segyfile.xline[0:1, :]])

Read and understand fairly 'unstructured' data (e.g., data sorted in common shot gathers):

filename = 'name_of_your_prestack_file.sgy'
with segyio.open(filename, "r", ignore_geometry=True) as segyfile:
    segyfile.mmap()

    # Extract header word for all traces
    sourceX = segyfile.attributes(segyio.TraceField.SourceX)[:]

    # Scatter plot sources and receivers color-coded on their number
    plt.figure()
    sourceY = segyfile.attributes(segyio.TraceField.SourceY)[:]
    nsum = segyfile.attributes(segyio.TraceField.NSummedTraces)[:]
    plt.scatter(sourceX, sourceY, c=nsum, edgecolor='none')

    groupX = segyfile.attributes(segyio.TraceField.GroupX)[:]
    groupY = segyfile.attributes(segyio.TraceField.GroupY)[:]
    nstack = segyfile.attributes(segyio.TraceField.NStackedTraces)[:]
    plt.scatter(groupX, groupY, c=nstack, edgecolor='none')

Write segy file using same header of another file but multiply data by *2

input_file = 'name_of_your_input_file.sgy'
output_file = 'name_of_your_output_file.sgy'

copyfile(input_file, output_file)

with segyio.open(output_file, "r+") as src:

    # multiply data by 2
    for i in src.ilines:
        src.iline[i] = 2 * src.iline[i]

Make segy file from sctrach

spec = segyio.spec()
filename = 'name_of_your_file.sgy'

spec = segyio.spec()
file_out = 'test1.sgy'

spec.sorting = 2
spec.format = 1
spec.samples = 30
spec.ilines = np.arange(10)
spec.xlines = np.arange(20)

with segyio.create(filename, spec) as f:

    # write the line itself to the file and the inline number in all this line's headers
    for ilno in spec.ilines:
        f.iline[ilno] = np.zeros(
            (len(spec.xlines), spec.samples), dtype=np.single) + ilno
        f.header.iline[ilno] = {
            segyio.TraceField.INLINE_3D: ilno,
            segyio.TraceField.offset: 0
        }

    # then do the same for xlines
    for xlno in spec.xlines:
        f.header.xline[xlno] = {
            segyio.TraceField.CROSSLINE_3D: xlno,
            segyio.TraceField.TRACE_SAMPLE_INTERVAL: 4000
        }

Visualize data using sibling tool SegyViewer:

from PyQt4.QtGui import QApplication
import segyviewlib
qapp = QApplication([])
l = segyviewlib.segyviewwidget.SegyViewWidget('filename.sgy')
l.show()

MATLAB

filename='name_of_your_file.sgy'

% Inspect segy
Segy_struct=SegySpec(filename,189,193,1);

% Read headerword inline for each trace
Segy.get_header(filename,'Inline3D')

%Read data along first xline
data= Segy.readCrossLine(Segy_struct,Segy_struct.crossline_indexes(1));

%Read cube
data=Segy.get_cube(Segy_struct);

%Write segy, use same header but multiply data by *2
input_file='input_file.sgy';
output_file='output_file.sgy';
copyfile(input_file,output_file)
data = Segy.get_traces(input_file);
data1 = 2*data;
Segy.put_traces(output_file, data1);

History

Segyio was initially written and is maintained by Statoil ASA as a free, simple, easy-to-use way of interacting with seismic data that can be tailored to our needs, and as contribution to the free software community.