Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Wu, Xinming; Geng, Zhicheng; Shi, Yuexi; Pham, Nam; Fomel, Sergey

doi:10.1190/segam2019-3214282.1

Cited by 24 publications

(25 citation statements)

References 41 publications

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“…In this workflow, we begin with an initial 3‐D reflectivity model r 0 ( x , y , z ) with flat layers (as shown in Figure 5a), where the reflectivity values at each layer are randomly generated and smoothly vary in space. We then randomly generate folding structures by vertically shearing the flat model, where the shearing field s ( x , y , z ) is defined as a combination of two functions:

s false(x, y, z false) = s_{1} false(x, y, z false) + s_{2} false(x, y, z false),

where the first function s 1 is a combination of N Gaussian functions scaled by a vertically damping function as suggested by Wu, Liang, et al (2019) and Wu et al (2020):

s_{1} false(x, y, z false) = \frac{1.5}{z_{\max}} true \sum_{k = 1}^{k = N} a_{k} e^{\frac{{false(x - b_{k} false)}^{2} + {false(y - c_{k} false)}^{2}}{2 σ_{k}^{2}}} .

…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

“…In defining this function, the parameters a k , b k , c k , and σ k are all randomly chosen from some predefined ranges as discussed by Wu et al (2020). By using the damping scalar function

\frac{1.5}{z_{\max}}

, we gradually decrease the curvature (or bending extent) vertically upward from bottom to top in the model.…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

“…This linear function is used to generate the purely dipping structures in the reflectivity model. The slopes of the dipping structures in the x − and y − directions, respectively, are defined by the parameters p and q , which are randomly chosen from a limited range of [− 0.25, 0.25] to avoid generating layers with extremely high slopes after combining with s 1 (Wu et al, 2020). We set

c_{0} = - p x_{c} - q y_{c}

to ensure that the center trace r ( x c , y c , z ) of the reflectivity model is not shifted.…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

“…After constructing a reflectivity model (Figure 5c) with simulated folding structures and collapsed paleokarst features, our next step is to simulate a synthetic seismic image (Figure 5d) by convolving a Ricker wavelet with the reflectivity model. Instead of convolving in the vertical direction as in conventional methods, we compute the convolution here in the direction perpendicular to the reflector structures as suggested by Wu, Liang, et al (2019) and Wu et al (2020). The peak frequency of the Ricker wavelet is also randomly chosen from a predefined range (10–35 Hz) to compute a synthetic seismic image with various frequency components.…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

“…CNN‐based methods are powerful for multidimensional image processing tasks including image classification (e.g., He et al, 2016; Krizhevsky et al, 2012; Simonyan & Zisserman, 2014; Szegedy et al, 2015), object detection (e.g., Lin et al, 2017; Liu et al, 2016; Redmon et al, 2016; Ren et al, 2015; Sermanet et al, 2013), and image segmentation (e.g., Badrinarayanan et al, 2017; Chen et al, 2017; Long et al, 2015; Ronneberger et al, 2015). Recently, CNNs have been increasingly applied in geoscience problems (Bergen et al, 2019; Bianco et al, 2019) including the interpretation of geologic faults (e.g., Di et al, 2019; Lu et al, 2018; Wu, Liang, et al, 2019; Wu, Shi, et al, 2019; Zhao & Mukhopadhyay, 2018), horizons (e.g., Geng et al, 2019; Wu, Zhang, et al, 2019; Wu et al, 2020), salt bodies (e.g., Di & AlRegib, 2020; Di et al, 2018; Guillen et al, 2015; Shi et al, 2019), and channels (Pham et al, 2019) in seismic images.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Yan

et al. 2020

JGR Solid Earth

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Paleokarst systems are found extensively in carbonate-prone basins worldwide. They can form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can also create serious engineering geohazards. The full delineation of potentially buried paleokarst systems plays an important role for reservoir characterization, oil and gas production, and other engineering tasks. We propose a supervised convolutional neural network (CNN) to automatically and accurately characterize paleokarst and associated collapse features from 3-D seismic images. To avoid time-consuming manual labeling for training the CNN, we propose an efficient workflow to automatically generate numerous 3-D training image pairs including synthetic seismic images and the corresponding label images of the collapsed paleokarst features simulated in the seismic images. With this workflow, we are able to simulate realistic and diverse geologic structures and collapsed paleokarst features in the training images from which the CNN can effectively learn to recognize the collapsed paleokarst features in real field seismic images. Two field examples from the Fort Worth Basin demonstrate that our CNN-based method is superior to conventional automatic methods in delineating paleokarst collapse features from seismic images. From the CNN-based paleokarst characterization, we can further automatically extract 3-D collapsed paleokarst systems and quantitatively measure their geometric parameters. Our CNN-based method is highly efficient and takes only seconds to classify collapsed paleokarst features a 3-D seismic image with 320 × 1, 024 × 1, 024 samples (approximately 268 km 2) by using one graphics processing unit. We propose a convolutional neural network (CNN) to fully detect paleokarst collapse features in 3-D seismic images and extract 3-D paleokarst chimney tubes all at once, which would provide a clear 3-D perspective of

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s false(x, y, z false) = s_{1} false(x, y, z false) + s_{2} false(x, y, z false),

where the first function s 1 is a combination of N Gaussian functions scaled by a vertically damping function as suggested by Wu, Liang, et al (2019) and Wu et al (2020):

s_{1} false(x, y, z false) = \frac{1.5}{z_{\max}} true \sum_{k = 1}^{k = N} a_{k} e^{\frac{{false(x - b_{k} false)}^{2} + {false(y - c_{k} false)}^{2}}{2 σ_{k}^{2}}} .

…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

“…In defining this function, the parameters a k , b k , c k , and σ k are all randomly chosen from some predefined ranges as discussed by Wu et al (2020). By using the damping scalar function

\frac{1.5}{z_{\max}}

, we gradually decrease the curvature (or bending extent) vertically upward from bottom to top in the model.…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

c_{0} = - p x_{c} - q y_{c}

to ensure that the center trace r ( x c , y c , z ) of the reflectivity model is not shifted.…”

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

Section: Seismic Simulation Of Paleokarst Collapsesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Yan

et al. 2020

JGR Solid Earth

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A gigabyte interpreted seismic dataset for automatic fault recognition

Jiu-lin

et al. 2021

Data in Brief

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The lack of large-scale open-source expert-labelled seismic datasets is one of the barriers to applying today’s AI techniques to automatic fault recognition tasks. The dataset present in this article consists of a large number of processed seismic images and their corresponding fault annotations. The processed seismic images, which are originally from a seismic survey called Thebe Gas Field in the Exmouth Plateau of the Carnarvan Basin on the NW shelf of Australia, are represented in Python Numpy format, which can be easily adopted by various AI models and will facilitate cooperation with researchers in the field of computer science. The corresponding fault annotations were firstly manually labelled by expert interpreters of faults from seismic data in order to investigate the structural style and associated evolution of the basin. Then the fault interpretation and seismic survey are processed and collected using Petrel software and Python programs separately. This dataset can help to train, validate, and evaluate the performance of different automatic fault recognition workflow.

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Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

Geng

et al. 2021

JGR Solid Earth

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Extracting horizons and detecting faults in a seismic image are basic steps for structural interpretation and important for many seismic processing schemes. A common ground of the two tasks is to analyze seismic structures and they are related to each other. However, previously proposed methods deal with the tasks independently, and challenge remains in each of them. We propose a volume‐to‐volume neural network to estimate a relative geologic time (RGT) volume from a seismic volume, and this RGT volume is further used to simultaneously interpret horizons and faults. The network uses U‐shaped framework with attention mechanism to systematically aggregate multi‐scale information and automatically highlight informative features, and achieves high prediction accuracy with affordable computational costs. To train the network, we build thousands of 3‐D noisy synthetic seismic volumes and corresponding RGT volumes with realistic and various structures. We introduce a loss function based on structure similarity to capture spatial dependencies among seismic samples for better optimizing the network, and use multiple reasonable assessments to evaluate the predicted results. Trained by using synthetic data, our network outperforms the conventional approaches in recognizing structural features in field data examples. Once obtaining an RGT volume, we can not only obtain seismic horizons by simply extracting RGT constant surfaces but also detect faults that are indicated by lateral RGT discontinuities. To be able to deal with large seismic volumes, we further propose a workflow to first estimate sub‐volumes of RGT and merge them to obtain a full RGT volume without boundary artifacts.

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Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Cited by 24 publications

References 41 publications

Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

A gigabyte interpreted seismic dataset for automatic fault recognition

Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

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