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Seismic fault detection based on multi-attribute support vector machine analysis Haibin Di*, Muhammad A. Shafiq, and Ghassan AlRegib
Center for Energy and Geo Processing (CeGP), Georgia Institute of Technology
Summary
Reliable fault detection is one of the major tasks of
subsurface interpretation and reservoir characterization from
three-dimensional (3D) seismic surveying. This study
presents an innovative workflow based on multi-attribute
support vector machine (SVM) analysis of a seismic volume,
which consists of four steps. First, three groups of seismic
attributes are selected and computed from the volume of
seismic amplitude, including edge-detection, geometric, and
texture, all of which clearly highlight the seismic faults in
the attribute images. Second, two sets of training samples are
prepared by manually picking on the faults and the non-
faulting zones, respectively. Third, the SVM analysis is
performed on the training datasets that builds an optimal
classification model for volumetric processing. Finally,
applying the SVM model to the whole seismic survey leads
to a binary volume, in which the presence of a fault is
labelled as ones. The added values of the proposed method
are verified through applications to the seismic dataset over
the Great South Basin in New Zealand, where the dominant
features are polygonal faults of varying sizes and
orientations. The results demonstrate not only good match
between the detected faults and the original seismic images,
but also great potential for quantitative fault interpretation,
such as semi-automatic/automatic fault extraction, to aid
structural framework modeling and reservoir simulation in
the exploration areas of numerous faults and fractures
Introduction
Faults and fractures are often of important geologic
implications for investigating hydrocarbon accumulation
and migration in a petroleum reservoir in the subsurface, and
the presence of a fault can be visually recognized as a
lineament of abrupt changes in reflection patterns from
three-dimensional (3D) reflection seismic data. However,
quantitative fault interpretation, such as patch extraction, is
a time-consuming and lab-intensive process and remains as
a challenging topic, especially for an exploration area
featured with numerous faults. In the past decades, great
efforts have been devoted into developing new attributes and
methods/algorithms to help improve the accuracy of seismic
fault detection.
From the perspective of seismic attribute analysis, both the
edge-detection and geometric attributes are applicable for
fault detection, due to the apparent lateral variation of
seismic waveform and/or amplitude across a fault. The
seismic edge-detection analysis was first presented as the
coherence attribute for highlighting the faults and
stratigraphic features from a seismic cube (Bahorich and
Farmer, 1995), and since then, such attribute and its
derivatives has been improved for better detection resolution
and noise robustness (e.g., Luo et al., 1996; Marfurt et al.,
1998; Tingdahl and de Rooij, 2005; Al-Dossary et al, 2014;
Di and Gao, 2014; Wang et al., 2016). Then for more robust
fault detection and fracture characterization from 3D seismic
data, the seismic geometric attributes are developed by
quantifying the lateral variations of the geometry of seismic
reflectors, including the second-order curvature (e.g., Lisle,
1994; Roberts, 2001; Al-Dossary and Marfurt, 2006), and
the third-order flexure attributes (e.g., Gao, 2013; Yu, 2014;
Di and Gao, 2017a). Meanwhile, computer-aided extraction
of fault patches has been the research focus since 1990s. For
example, Meldahl et al. (1999) presented a semi-automatic
approach for detecting seismic chimneys by combining
directive attributes and neural network, and such approach
was later adapted to fault extraction from 3D seismic data by
Tingdahl and de Rooij (2005). Gibson et al. (2005) and
Zhang et al. (2014) proposed grouping fault points into the
local planar patches and then merging these small patches
into larger fault surfaces under certain geometric constraints.
Hale (2013) proposed a dynamic time warping algorithm to
generate fault surfaces based on the boundary constraints
derived from the thinned discontinuity images. Wang and
AlRegib (2014) introduced the ideas of motion vectors in
video coding and processing to extract fault surfaces.
Unfortunately, all these algorithms are unable to
simultaneously achieve both high resolution in extracting
true faults and high accuracy in avoiding non-fault artifacts.
The common result is either an aggressive case with high
resolution (most/all true faults extracted) but low accuracy
(many artifacts introduced), or a conservative one with high
accuracy (few artifacts introduced) but low resolution (few
true faults extracted) (Di and Gao, 2017b). Therefore, semi-
automatic/automatic fault extraction is still in the
experimental phase for testing and not ready for practical
implementation and application to industrial projects.
For resolving such limitation, this paper first proposes a new
fault-detection workflow based on semi-supervised multi-
attribute support vector machines (SVM) analysis, and then
applies it to the 3D seismic dataset over the Great South
Basin (GSB) in New Zealand, where polygonal faults of
varying sizes and orientations are observed in the
subsurface.
Algorithm description
This study adapts the binary SVM classification to work for
multi-attribute seismic fault detection, and the proposed
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SVM-based fault detection
method consists of four steps. First, three groups of seismic
attributes are selected and calculated from a seismic dataset,
which expands the seismic domain into a multi-dimensional
attribute domain and represents every sample with a vector.
Second, two sets of training data are manually picked at both
the faults and the non-faulting areas. Third, a SVM classifier
is trained by the picked training datasets. Finally, the created
classifier is applied to the whole seismic volume, and a
binary volume is generated with the potentials faults labelled
as ones.
For the convenience of illustrating the proposed workflow,
we expand each step in details, using the 3D seismic dataset
over the Great South Basin (GSB) in New Zealand, where
the subsurface geology is dominated by polygonal faulting
in varying sizes and orientations. Figure 1 displays the time
section of original seismic amplitude at 1152 ms, in which a
set of faults are clearly imaged.
A. Attribute selection
For efficient fault detection, the selected attributes are
expected capable of enhancing the faults to be more
distinguishable from the non-fault features (such as
horizons) in the attribute images. Among all possible seismic
attributes, we select and generate fourteen attributes from the
amplitude volume, which are categorized into three groups,
Geometric attribute. It measures the lateral variation of
the geometry of seismic reflectors, including the
reflector dip, the curvature (Roberts, 2001), the flexure
(Di and Gao, 2017a), and the geometric fault (Di and
AlRegib, 2017).
Edge-detection attribute. It evaluates the lateral
changes in seismic waveform and/or amplitude using
various edge-detection operators, including the
coherence (Bahorich and Farmer, 1995), the Sobel filter
(Luo et al., 1996), the semblance (Marfurt et al., 1998),
the Canny edge (Di and Gao, 2014), the similarity, and
the variance (Pedersen et al., 2002).
Texture attribute. It describes the local distribution of
seismic texture using various statistical operators,
including the GLCM contrast and homogeneity (Gao,
2003; Eichkitz et al., 2013), gradient of texture (GoT)
(Shafiq et al., 2017a), and seismic saliency (Shafiq et
al., 2017b).
After extracting all fourteen attributes, every sample in the
seismic volume is represented by a vector of 14 elements,
which allows classifying the samples in the 14-dimensional
attribute domain and estimating a 13-dimensional
hyperplane that well separates these samples into two
classes. To be clear, all the selected attributes are
normalized, due to their different units of measurement and
moreover distinct histograms.
B. Training sample labelling
To ensure accurate SVM classification, two sets of training
samples are manually picked on the faults (171 picks) and
the non-faulting zones (722 picks) (Figure 2), which are
further used in two ways. First, cross plotting of the selected
attributes over the manual pickings could be used for
verifying the step of attribute selection, in which the fault
pickings and the non-fault pickings are partitioned into two
groups with a border between them. Second, the manual
pickings can help check the accuracy of the training SVM
Figure 2: The manual pickings on the vertical section of
crossline 2800, including 171 pickings on the faults (denoted
as cyan dots) and 722 pickings on the surrounding non-
faulting features (denoted as magenta dots).
Figure 1: The time slice of original seismic amplitude at
1152 ms over the Great South Basin (GSB) in New Zealand,
where the dominant structural features are polygonal
faulting in varying sizes and orientations.
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SVM-based fault detection
model. Ideally, the built model should be capable of
labelling these pickings accurately; however, due to the mis-
pickings of the seeds and/or the noises present in the
attributes, errors are often observed.
C. SVM model training and testing
Based on the labelled training datasets, the SVM model is
built for connecting the selected fourteen attributes and the
fault labelling. Before applying for volumetric processing,
the model is first verified through the training datasets.
Figure 3 displays the classification of the 893 pickings.
Compared to the training samples (Figure 2), we notice that
the majority of the pickings are labelled corrected, except
those on the zones where the seismic expressions are similar
to those on the faults (denoted by circles).
D. Volumetric processing
After verifying the accuracy of the built SVM classification
model, it is applied to the whole seismic survey for
volumetric processing, generating a binary volume with fault
labelled as ones.
Results and applications
We apply the proposed multi-attribute SVM classification to
the GSB seismic dataset, and for the convenience of result
interpretation, we then adjust the opacity of the binary fault
volume and overlay them on the original seismic images.
The results are displayed in different ways, including the 3D
view (Figure 4), the corresponding time slice at 1152 ms
(Figure 5), and four randomly-selected vertical sections
(Figure 6), all of which demonstrate good match between the
detected faults and the original seismic images, indicating
the accuracy of the proposed SVM fault classification.
Besides the qualitative mapping of the faults from a seismic
dataset, the binary volume could also serve as input to more
advanced quantitative interpretation. For example, seimi-
automatic/automatic fault extraction can be performed on
the fault volume, and Figure 7 displays the extracted fault
patches from the binary volume, clipped to the time slice at
1152 ms.
Figure 4: 3D view of the fault detection from the Great
South Basin (GSB) in New Zealand, overlaying the original
seismic amplitude. Note the good match between the
detected faults and the seismic images.
Figure 3: The re-classification of the 893 pickings by the
built SVM model, overlaying the vertical section of crossline
2800, with the cyan dots representing the faults and the
magenta dots representing the non-faulting features.
Figure 5: The time slice of the detected faults at 1152 ms by
the proposed multi-attribute SVM classification. Note the
good match between the polygonal faults and the original
seismic image.
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SVM-based fault detection
Conclusions
Reliable detection of subsurface faults and fractures from 3D
seismic data is essential for reservoir characterization and
modeling. This study has presented a new workflow for
seismic fault detection based on multi-attribute support
vector machine (SVM) classification, which consists of three
major components. First, three attribute groups, including
the edge-detection attributes, geometric attributes, and the
texture attributes, are generated from the amplitude volume
that help differentiate the faults of interpretational interest
from the surrounding non-faulting features. Second, two sets
of manual pickings are used for training a SVM model in the
attribute domain and building an optimal model for
volumetric processing. Finally, applying the built
classification model to the whole seismic survey provides a
binary volume, which not only clearly delineates the faults,
but also holds the potential for more advanced fault
interpretation, such as semi-automatic/automatic fault
extraction.
Such multi-attribute seismic feature classification could be
further improved by incorporating more fault attributes,
increasing the amount of training samples, and applying
more advanced machine learning algorithms (e.g., CNN).
However, it might require more time and efforts for data
preparation and computation.
Acknowledgments
This work is supported by the Center for Energy and Geo
Processing at Georgia Tech and King Fahd University of
Petroleum and Minerals. We thank the New Zealand
Petroleum and Minerals (NZP&M) for providing the 3D
seismic dataset over the Great South Basin (GSB). The SVM
classification algorithm is provided by the Turi GraphLab
CreateTM under an academic license.
Figure 6: Four randomly-selected vertical sections of the
fault detection by the proposed multi-attribute SVM
classification. Note the good match between the detected
faults and the original seismic image.
Figure 7: The fault patches extracted from the binary fault
volume by the proposed multi-attribute SVM classification,
clipped to the time slice at 1152 ms.
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EDITED REFERENCES
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SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online
metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web.
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