Deep learning for characterizing paleokarst features in 3D seismic images

A 3D seismic image contains structural and stratigraphic features such as reflections, faults, and channels. When smoothing such an image, we want to enhance all of these features so that they are easier to interpret. Most smoothing methods aim to enhance reflections but may blur faults and channels in the image. A few methods smooth seismic reflections while preserving faults and channel boundaries. However, it has not well-discussed to smooth simultaneously along the seismic reflections and channels, which are linear features apparent within dipping reflections. In addition, to interpret faults and channels, extra steps are required to compute attributes or mappings of faults and channels from a seismic image. Such fault and channel attributes are often sensitive to noise because they are typically computed as discontinuities of seismic reflections. In this paper, we have developed methods to simultaneously enhance seismic reflections, faults, and channels while obtaining mappings of the faults and channels. In these methods, we first estimate the orientations of the reflections, faults, and channels directly in a seismic image. We then use the estimated orientations to control the smoothing directions in an efficient iterative diffusion scheme to smooth a seismic image along the reflections and channels. In this iterative scheme, we also efficiently compute mappings of faults and channels, which are used to control smoothing extents in the diffusion to stop smoothing across them. This diffusion scheme iteratively smooths a seismic image along reflections and channels while updating the mappings of faults and channels. By doing this, we will finally obtain an enhanced seismic image (with enhanced reflections and channels and sharpened faults) and cleaned mappings of faults and channels (discontinuities related to noise are cleaned up). We have examined the methods using 2D and 3D real seismic images.

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Edge-aware filtering with Siamese neural networks

The Leading Edge ◽

10.1190/tle39100711.1 ◽

2020 ◽

Vol 39 (10) ◽

pp. 711-717

Author(s):

Mehdi Aharchaou ◽

Michael Matheney ◽

Joe Molyneux ◽

Erik Neumann

Keyword(s):

Neural Networks ◽

Seismic Data ◽

Learning Ability ◽

Seismic Exploration ◽

Data Sets ◽

3D Seismic ◽

New Class ◽

Seismic Image ◽

Siamese Networks ◽

Seismic Images

Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.

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Fault surfaces and fault throws from 3D seismic images

10.1190/segam2012-0734.1 ◽

2012 ◽

Cited By ~ 2

Author(s):

Dave Hale

Keyword(s):

3D Seismic ◽

Seismic Images

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A method for estimating apparent displacement vectors from time-lapse seismic images

Geophysics ◽

10.1190/1.3184015 ◽

2009 ◽

Vol 74 (5) ◽

pp. V99-V107 ◽

Cited By ~ 46

Author(s):

Dave Hale

Keyword(s):

Time Lapse ◽

Phase Correlation ◽

3D Seismic ◽

Local Phase ◽

Apparent Displacement ◽

Displacement Vectors ◽

Cyclic Sequence ◽

Seismic Images ◽

Three Components

Reliable estimates of vertical, inline, and crossline components of apparent displacements in time-lapse seismic images are difficult to obtain for two reasons. First, features in 3D seismic images tend to be locally planar, and components of displacement within the planes of such features are poorly resolved. Second, searching directly for peaks in 3D crosscorrelations is less robust, more complicated, and computationally more costly than searching for peaks of 1D crosscorrelations. I estimate all three components of displacement with a process designed to mitigate these two problems. I address the first problem by computing for each image sample a local phase correlation instead of a local crosscorrelation. I address the second problem with a cyclic sequence of searches for peaks of correlations computed for lags constrained to one of the three image axes.

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Automatically interpreting all faults, unconformities, and horizons from 3D seismic images

Interpretation ◽

10.1190/int-2015-0160.1 ◽

2016 ◽

Vol 4 (2) ◽

pp. T227-T237 ◽

Cited By ~ 21

Author(s):

Xinming Wu ◽

Dave Hale

Keyword(s):

Image Processing ◽

Partial Differential Equations ◽

Differential Equations ◽

Array Processing ◽

3D Seismic ◽

Partial Differential ◽

Seismic Image ◽

Processing Procedure ◽

Seismic Images

Extracting fault, unconformity, and horizon surfaces from a seismic image is useful for interpretation of geologic structures and stratigraphic features. Although others automate the extraction of each type of these surfaces to some extent, it is difficult to automatically interpret a seismic image with all three types of surfaces because they could intersect with each other. For example, horizons can be especially difficult to extract from a seismic image complicated by faults and unconformities because a horizon surface can be dislocated at faults and terminated at unconformities. We have proposed a processing procedure to automatically extract all the faults, unconformities, and horizon surfaces from a 3D seismic image. In our processing, we first extracted fault surfaces, estimated fault slips, and undid the faulting in the seismic image. Then, we extracted unconformities from the unfaulted image with continuous reflectors across faults. Finally, we used the unconformities as constraints for image flattening and horizon extraction. Most of the processing was image processing or array processing and was achieved by efficiently solving partial differential equations. We used a 3D real example with faults and unconformities to demonstrate the entire image processing.

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