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Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks
Geophysics · Submitted 24 Nov 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This indicates the great potential of applying CNN for salt-related interpretations.
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The abstract, sentence by sentence
One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. In this paper, the benefits of CNN-based classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lov\'{a}sz-Softmax loss function, and stratified $K$-fold cross-validation, have been deployed to further improve the prediction accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. This indicates the great potential of applying CNN for salt-related interpretations.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. | background 0.91 | 0.04 | 0.02 | asserted 0.63 |
| s2 | With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s3 | Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s4 | In this paper, the benefits of CNN-based classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. | contribution 0.61 | 0.88 | 0.19 | asserted 1.00 |
| s5 | Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lov\'{a}sz-Softmax loss function, and stratified $K$-fold cross-validation, have been deployed to further improve the prediction accuracy. | method 0.99 | 0.92 | 0.04 | asserted 1.00 |
| s6 | The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. | result 1.00 | 0.95 | 0.22 | asserted 0.98 |
| s7 | This indicates the great potential of applying CNN for salt-related interpretations. | interpretation 1.00 | 0.50 | 0.70 | hedged possibility 0.99 |
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The paper
Original abstract
One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. In this paper, the benefits of CNN-based classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lov\'{a}sz-Softmax loss function, and stratified $K$-fold cross-validation, have been deployed to further improve the prediction accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. This indicates the great potential of applying CNN for salt-related interpretations.
Human review
How the literature uses it
3 arXiv papers cite this in 5 sentences
Papers citing it · 2 sentences without a confident label
Cites it as background 2 papers
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In particular, U-nets were used for the interpretation of geological units [ref] , horizons [ref] , salt [ref] , and faults [ref] .
Does shallow geological knowledge help neural-networks to predict deep units? · Introduction · 0.98
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The advent of convolutional neural networks (CNNs) brought significant advancements in different problems and several attempts have been made to apply CNNs in the field of seismic imaging [ref] .
Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks · Introduction · 0.88
What it cites · 3 sentences without a confident label
Uses the method or tool of 3 papers
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The U-Net [ref] structure, which combines a contracting/down-sampling path to extact context information (what) and a symmetric expanding/up-sampling path to retrieve location information (where), has been used as our baseline architecture.
U-Net: Convolutional Networks for Biomedical Image Segmentation · Network Architecture · 1.00
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To overcome the degradation problem with deep learning networks, a particular advanced variant of U-Net is adopted in our experiments: the Deep Residual Learning or the ResNet [ref] .
Deep Residual Learning for Image Recognition · Network Architecture · 0.97
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In addition to utilizing the state-of-art U-Net + ResNet network structure with conventional configuration for deep neural networks, which includes He initialization [ref] that particularly considers the rectifier nonlinearities, batch normalization and dropout, a few adjustments of the network were implemented to further improve the salt body prediction precision.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification · Network Training · 0.95
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