Deep Learning for Horizon Interpretation on 2D Seismic

82nd EAGE Annual Conference & Exhibition

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


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.


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)


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

Di, H., Li, Z., Maniar, H. and Abubakar, A. [2020]. Seismic stratigraphy interpretation by deep

convolutional neural networks: A semisupervised workflow. Geophysics, 85(4), WA77-WA86.

Gramstad, O., Nickel, M., Goledowski, B. and Etchebes, M. [2020]. Strategies in picking training data

for 3D convolutional neural networks in stratigraphic interpretation. 90th Annual International

Meeting, SEG, Expanded Abstracts.

He, K., Zhang, X., Ren, S. and Sun, J. [2016]. Deep residual learning for image recognition.

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Li, Z. and Abubakar, A. [2020]. Complete sequence stratigraphy from seismic optical flow without

human labeling. 90th Annual International Meeting, SEG, Expanded Abstracts.

Peters, B., Granek, J. and Haber, E. [2019]. Multiresolution neural networks for tracking seismic

horizons from few training images. Interpretation, 7(3), SE201-SE213.

Wu, X. and Hale, D. [2015]. Horizon volumes with interpreted constraints. Geophysics, 80(2), IM21-

IM33.

Xue, Z., Wu, X. and Fomel, S. [2018]. Predictive painting across faults. Interpretation, 6(2), T449-

T455.

Yu, Y., Kelley, C. and Mardanova, I. [2011]. Automatic horizon picking in 3D seismic data using

optical filters and minimum spanning tree (patent pending). 81st Annual International Meeting,

SEG, Expanded Abstracts.

Zhang, B., Qi, J., Lou, Y., Fang, H. and Cao, D. [2020]. Generating seismic horizon using multiple

seismic attributes. IEEE Geoscience and Remote Sensing Letters, early access.

Documents

Deep Learning for Horizon Interpretation on 2D Seismic