zfbi / osv Goto Github PK

This repository contains computer programs written and used by Xinming Wu for 2D and 3D fault interpretation that is discussed in our Geophysics paper Automatic fault interpretation with optimal surface voting.

If you find this work helpful in your research, please cite:

@article{wu2018automatic,
    author = {Xinming Wu and Sergey Fomel},
    title = {Automatic fault interpretation with optimal surface voting},
    journal = {GEOPHYSICS},
    volume = {83},
    issue = {5},
    pages = {O67-O82},
    year = {2018},
    doi = {10.1190/GEO2018-0115.1},
    URL = {https://library.seg.org/doi/abs/10.1190/geo2018-0115.1},
}

If you want to do more than browse the source code, you must first download and build the package using Gradle.

Here are brief descriptions of key components:

Computes optimal voting surfaces, voting scores and a final 3D voting score map.

Computes optimal voting paths, voting scores and a final 2D voting score map.

Quickly scan for approximate fault dips

Quickly scan for approximate fault strikes and dips

Automatically construct fault skins/surfaces from a final voting score map

A simple linked data structure to represent a fault surface as discussed by Wu and Hale (2016)

1. download the 3D seismic data "xs.dat" into the folder ./data/3d/f3d/

2. go to ./src/osv/ and type ./jy demoF3d.py to run a test on the F3 dataset

2D and 3D examples published in the paper.

Dimensions: n1=380, n2=591

Data type: binary with BIG_ENDIAN

Seismic: ./data/2d/f3d/gx56.dat

Linearity: ./data/2d/f3d/ep56.dat

OSV fault: ./data/2d/f3d/fv.dat

Thinned OSV fault: ./data/2d/f3d/fvt.dat

seismic time/depth slice (left) and 1-planarity (input for optimal path voting)

optimal path voting faults before (left) and after (right) thinning

Dimensions: n1=300, n2=550

Data type: binary with BIG_ENDIAN

Seismic: ./data/2d/campos/gx373.dat

OSV fault: ./data/2d/campos/fv.dat

Thinned OSV fault: ./data/2d/campos/fvt.dat

---------------------seismic image (left) and fault likelihood (Hale, 2013; Wu and Hale, 2016)-----------------------

input 1-linearity (left) and output optimal path voting faults (right)

Dimensions: n1=210, n2=825

Data type: binary with BIG_ENDIAN

Seismic: ./data/2d/crf/gx3366.dat

OSV fault: ./data/2d/crf/fv.dat

Thinned OSV fault: ./data/2d/crf/fvt.dat

Fault likelihood: ./data/2d/crf/fl.dat

Thinned fault likelihood: ./data/2d/crf/flt.dat

---------------------seismic image (left) and fault likelihood (Hale, 2013; Wu and Hale, 2016)-----------------------

input 1-linearity (left) and output optimal path voting faults (right)

The datasets can be downloaded from: https://drive.google.com/open?id=1InfMvCSZWdJclykiTBIXDgV7HYdBj5_K

Dimensions: n1=100, n2=400, n3=420;

Data type:binary with BIG_ENDIAN

Seismic: xs.dat;

Input planarity: ep.dat

Output OSV fault: fv.dat

Thinned OSV fault: fvt.dat

Fault likelihood: fl.dat

---------------------seismic image (left) and fault likelihood (Hale, 2013; Wu and Hale, 2016)-----------------------

input 1-planarity (left) and output optimal surface voting (OSV) fault (right)

thinned OSV fault (left) and automatic fault surfaces colored by fault strike (right)

The datasets can be downloaded from: https://drive.google.com/open?id=10x1uO-GBJekmD2wS7S6VFbVsrxGXzMCC

Dimensions: n1=400, n2=801, n3=300

Data type:binary with BIG_ENDIAN

Seismic: gx.dat

OSV fault: fv.dat

---------------------seismic image (left) and fault likelihood (Hale, 2013; Wu and Hale, 2016)-----------------------

input 1-planarity (left) and output OSV fault (right)

thinned OSV fault (left) and automatic fault surfaces colored by fault strike (right)

The datasets can be downloaded from: https://drive.google.com/open?id=1fjZuonXYc55ytiKiFboUVkQWVRbht0yQ

Dimensions: n1=210, n2=920, n3=825;

Data type:binary with BIG_ENDIAN

Seismic: gx.dat;

OSV fault: fv.dat;

Thinned OSV fault: fvt.dat;

seismic image (left) and thinned OSV fault (right)

seismic image (left) and thinned OSV fault (right)

Copyright (c) 2018, Xinming Wu. All rights reserved. This software and accompanying materials are made available under the terms of the Common Public License - v1.0, which accompanies this distribution.