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Deep Learning for Horizon Interpretation on 2D Seismic Data
Introduction
Horizon interpretation is one of the most important yet time consuming steps in a traditional seismic
interpretation workflow. Traditionally, horizon interpretation is performed interactively. An interactive
horizon tracker takes an interpreter’s sparse picks as constraints and fills in horizon segments in between
the sparse picks, usually by comparing the similarity between two adjacent traces within a small
temporal window. Over the past few decades, advancement in fully automatic or semiautomatic seismic
horizon tracking methods has greatly accelerated the horizon interpretation process. Such methods
usually precompute some seismic attributes as the guidance for horizon tracking, then automatically
track the horizons with some structural constraints from some seed points (Yu et al., 2011; Wu and
Hale, 2015; Xue et al., 2018; Zhang et al., 2020). Recently, researchers have started to apply deep
learning (DL)-based approaches on horizon interpretation problems. In general, based on the direct
output from a DL model, we can group such methods into three categories: using horizon as the DL
target directly (Peters et al., 2019; Gramstad et al., 2020); using stratigraphy/facies as the DL target (Di
et al., 2020); or using a guiding attribute (such as relative geologic time) as the DL target (Li and
Abubakar, 2020). All the methods above can perform reasonably well on modern seismic data with
little to no human input.
In some legacy oil fields, 2D seismic data are abundantly available. With the advancement of seismic
processing and imaging techniques, reprocessing such legacy 2D seismic data may be the key to unlock
additional value and bring new insights to the understanding of the subsurface. Unfortunately, in
addition to the effort in processing and imaging, data reprocessing also requires repeating the
subsequent interpretation steps.
When applied to 3D seismic volumes, most if not all, the traditional methods use 3D information to
ensure loop-tie horizons. However, when the objects are individual 2D seismic lines, the tracking
process often becomes cumbersome as interpreters must assign sparse control points on each of the 2D
lines and then often perform the automatic horizon tracking individually on all the 2D lines. Different
from these tracking approaches, which rely on seed points on each 2D lines to start the horizon tracking
process, most DL-based methods aim to identify the target horizon as the same pattern on different
seismic images and are therefore independent from seed points when it comes to a new 2D line. To best
of our knowledge, DL-based horizon extraction on 2D seismic lines has not been discussed in the
literatures. In this study, we investigate using supervised DL to extract horizons on all the 2D seismic
lines after training on a small subset of the available lines from the same region. Using a 2D shallow
seismic dataset from offshore the Netherlands as an example, we are able to demonstrate that the
proposed method is able to extract horizons consistently across multiple moderate quality 2D seismic
lines.
Deep Learning Model
We use a customized convolutional neural network (CNN) as the DL model for 2D horizon extraction.
To solve horizon extraction with DL methods, the most straightforward way is to form it as an image
segmentation problem, in which for a given 2D seismic image, the output is a 2D probability map of
horizon picks. Unfortunately, such problem formulation does not conform with the definition of seismic
horizons. First, it is not guaranteed to have at most one pick per trace, which would violate the most
common case of single-z nature, and second, the vertical resolution is at seismic scale, violating the
grid-free nature. Therefore, some postprocessing treatments are necessary to transform the DL output
into a standard seismic horizon.
In this study, we experiment with outputting a standard seismic horizon directly from a DL model by
forming the DL problem as a multitask problem, with two branches of output. After some shared
convolutional layers defined as residual blocks (He et al., 2016), the first branch is a regressor that
outputs the grid-less depth of the horizon picks at all traces in the 2D seismic image. Because a horizon
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may not cover all the seismic traces, the second branch is a classifier that outputs true or false flags for
the same traces as used in the first branch, indicating the existence of horizon picks at each seismic
trace. Combining the outputs from these two branches, we are able to sufficiently describe a seismic
horizon compatible to the standard horizon definition. We present the overall structure of the CNN
model in Figure 1.
Figure 1 The overall structure of the CNN model used in this study. The input to the CNN model is a
2D seismic image, which then goes through several residual blocks and reshaped into a multi-channel
1D vector before feeding into the two output branches. We use fully connected layers in both branches.
We use a linear activation in the regression branch and a sigmoid activation in the classification
branch.
Field Example
We apply this dual-branch CNN model to a collection of 2D seismic lines from offshore the
Netherlands. There are 125 2D seismic lines of various lengths within the survey area, along two
orthogonal orientations (Figure 2). We have identified three main horizons, namely the seafloor, major
horizon 1, and major horizon 2. We partially interpreted these horizons manually on some of the 2D
seismic lines for training and evaluation purposes. For the seafloor, we use two lines for training, one
along each orthogonal direction. For the other two horizons, we use 10 lines for training. We show the
interpreted horizons in Figure 3, and the training seismic lines are marked by white arrows. We train
three CNN models individually, one for each horizon, then apply the trained models on all the 2D
seismic lines and show the results in Figure 3. From a map view, we observe that all three horizons
show good consistency between the manual interpretation and the DL extraction.
Figure 2 Map view of the 125 2D seismic lines within the survey area.
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Figure 3 Manual interpretation and DL extraction of the three horizons. (a) Manual interpretation of
seafloor; (b) manual interpretation of major horizon 1; (c) manual interpretation of major horizon 2;
(d) DL extraction of seafloor; (e) DL extraction of major horizon 1; and (f) DL extraction of major
horizon 2. White arrows mark the 2D seismic lines used when training the DL model.
From the map views in Figure 3, we note that although the seafloor covers the entire collection of 2D
seismic lines, both major horizon 1 and major horizon 2 are spatially limited and highly segmented,
which poses challenges to extracting horizons automatically. Moreover, the seismic data are not fully
processed and are therefore contaminated by strong noise and artifacts. We show a vertical section along
a 2D seismic line used for validation in Figure 4a and 4b. Compared with the manual interpretation, the
DL extraction is somewhat noisy, but still tracks the target horizons reasonably well. In Figure 4c, we
further show a zoom-in view from another 2D seismic line where manual interpretation is not available.
We see the seafloor is extracted almost perfectly, with no breakups when it goes across the spikes along
the seismic event. For the other two horizons, we clearly see that they follow the seismic events
consistently, even with the presence of large gaps on seismic data.
Conclusions
In this study, we have investigated using DL for horizon extraction on 2D seismic data, by learning
from a small amount of existing interpretations in the same area. The proposed method is able to extract
(a) (d)
(b) (e)
(c) (f)
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horizons on 2D seismic lines without seed points, with good quality.
Acknowledgement
We thank Schlumberger for the permission to publish this work, and the Netherlands Enterprise Agency
(RVO) for providing the seismic data under the creative commons license 4.0.
Figure 4 (a) Vertical section along a 2D seismic line used for validation, overlaid with manual horizon
interpretation; (b) DL extracted horizons on the same line as shown in (a); and (c) a zoom-in view of
DL extracted horizons on a 2D seismic line where manual interpretation is not available.
References
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