Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box

Xiujuan Liu; Lifeng Liang; Zhiming Kang; Qiang Guo

doi:doi:10.11648/j.earth.20221104.15

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Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box

Xiujuan Liu, Lifeng Liang, Zhiming Kang, Qiang Guo

Published in Earth Sciences (Volume 11, Issue 4)

Received: 22 June 2022 Accepted: 13 July 2022 Published: 22 July 2022

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Abstract

The process of hybrid deep learning is highly integrated with the seismic inversion, leading to the black box phenomena, which hampers the understanding of deep-learning inversion process. In this paper, a numerical example is presented to visualize the process of deep learning from the perspective of elastic inversion. Synthetic seismic data is generated by forward modeling on a wedge model, after which the GRU-CNN hybrid deep learning algorithm is applied to obtain the inverted impedance method. In specific, the extraction of local seismic features by CNN, the extraction of low-frequency seismic features by GRU, activation layer, Adam and learning rate schedules, initialization model, loss function, and training process are detailed illustrated and visualized, all of which reveal the internal operating mechanism of the black box. The results show that: 1) after required epoch iterations, the features extracted by CNN becomes close to the real impedance, while the features extracted by GRU is close to the low-frequency information involved in conventional seismic inversion (which is consistent with the cognition from commercial software, e.g., Jason, Geoview, etc.), 2) The learning rate is a very critical parameter in the optimization process. Comparing with the constant learning rate, the cosine learning rate converges faster with better performance, and 3) the initial impedance model in hybrid deep learning is to initialize the weights of all neurons, which is essentially different from those of conventional seismic inversion scheme, e.g., constrained sparse pulse inversion.

Published in	Earth Sciences (Volume 11, Issue 4)
DOI	10.11648/j.earth.20221104.15
Page(s)	194-203
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Elastic Impedance Inversion, GRU, CNN, Wedge Model, The Black Box

References

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APA Style

Xiujuan Liu, Lifeng Liang, Zhiming Kang, Qiang Guo. (2022). Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box. Earth Sciences, 11(4), 194-203. https://doi.org/10.11648/j.earth.20221104.15

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ACS Style

Xiujuan Liu; Lifeng Liang; Zhiming Kang; Qiang Guo. Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box. Earth Sci. 2022, 11(4), 194-203. doi: 10.11648/j.earth.20221104.15

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AMA Style

Xiujuan Liu, Lifeng Liang, Zhiming Kang, Qiang Guo. Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box. Earth Sci. 2022;11(4):194-203. doi: 10.11648/j.earth.20221104.15

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@article{10.11648/j.earth.20221104.15,
  author = {Xiujuan Liu and Lifeng Liang and Zhiming Kang and Qiang Guo},
  title = {Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box},
  journal = {Earth Sciences},
  volume = {11},
  number = {4},
  pages = {194-203},
  doi = {10.11648/j.earth.20221104.15},
  url = {https://doi.org/10.11648/j.earth.20221104.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20221104.15},
  abstract = {The process of hybrid deep learning is highly integrated with the seismic inversion, leading to the black box phenomena, which hampers the understanding of deep-learning inversion process. In this paper, a numerical example is presented to visualize the process of deep learning from the perspective of elastic inversion. Synthetic seismic data is generated by forward modeling on a wedge model, after which the GRU-CNN hybrid deep learning algorithm is applied to obtain the inverted impedance method. In specific, the extraction of local seismic features by CNN, the extraction of low-frequency seismic features by GRU, activation layer, Adam and learning rate schedules, initialization model, loss function, and training process are detailed illustrated and visualized, all of which reveal the internal operating mechanism of the black box. The results show that: 1) after required epoch iterations, the features extracted by CNN becomes close to the real impedance, while the features extracted by GRU is close to the low-frequency information involved in conventional seismic inversion (which is consistent with the cognition from commercial software, e.g., Jason, Geoview, etc.), 2) The learning rate is a very critical parameter in the optimization process. Comparing with the constant learning rate, the cosine learning rate converges faster with better performance, and 3) the initial impedance model in hybrid deep learning is to initialize the weights of all neurons, which is essentially different from those of conventional seismic inversion scheme, e.g., constrained sparse pulse inversion.},
 year = {2022}
}

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TY  - JOUR
T1  - Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box
AU  - Xiujuan Liu
AU  - Lifeng Liang
AU  - Zhiming Kang
AU  - Qiang Guo
Y1  - 2022/07/22
PY  - 2022
N1  - https://doi.org/10.11648/j.earth.20221104.15
DO  - 10.11648/j.earth.20221104.15
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 194
EP  - 203
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20221104.15
AB  - The process of hybrid deep learning is highly integrated with the seismic inversion, leading to the black box phenomena, which hampers the understanding of deep-learning inversion process. In this paper, a numerical example is presented to visualize the process of deep learning from the perspective of elastic inversion. Synthetic seismic data is generated by forward modeling on a wedge model, after which the GRU-CNN hybrid deep learning algorithm is applied to obtain the inverted impedance method. In specific, the extraction of local seismic features by CNN, the extraction of low-frequency seismic features by GRU, activation layer, Adam and learning rate schedules, initialization model, loss function, and training process are detailed illustrated and visualized, all of which reveal the internal operating mechanism of the black box. The results show that: 1) after required epoch iterations, the features extracted by CNN becomes close to the real impedance, while the features extracted by GRU is close to the low-frequency information involved in conventional seismic inversion (which is consistent with the cognition from commercial software, e.g., Jason, Geoview, etc.), 2) The learning rate is a very critical parameter in the optimization process. Comparing with the constant learning rate, the cosine learning rate converges faster with better performance, and 3) the initial impedance model in hybrid deep learning is to initialize the weights of all neurons, which is essentially different from those of conventional seismic inversion scheme, e.g., constrained sparse pulse inversion.
VL  - 11
IS  - 4
ER  -

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Author Information

Xiujuan Liu

Department of Geography, Lingnan Normal University, Zhanjiang, China
Lifeng Liang

Department of Geography, Lingnan Normal University, Zhanjiang, China
Zhiming Kang

Department of Geography, Lingnan Normal University, Zhanjiang, China
Qiang Guo

College of Information Engineering, China Jiliang University, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Xiujuan Liu, Lifeng Liang, Zhiming Kang, Qiang Guo. (2022). Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box. Earth Sciences, 11(4), 194-203. https://doi.org/10.11648/j.earth.20221104.15

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ACS Style

Xiujuan Liu; Lifeng Liang; Zhiming Kang; Qiang Guo. Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box. Earth Sci. 2022, 11(4), 194-203. doi: 10.11648/j.earth.20221104.15

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AMA Style

Xiujuan Liu, Lifeng Liang, Zhiming Kang, Qiang Guo. Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box. Earth Sci. 2022;11(4):194-203. doi: 10.11648/j.earth.20221104.15

Copy | Download

@article{10.11648/j.earth.20221104.15,
  author = {Xiujuan Liu and Lifeng Liang and Zhiming Kang and Qiang Guo},
  title = {Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box},
  journal = {Earth Sciences},
  volume = {11},
  number = {4},
  pages = {194-203},
  doi = {10.11648/j.earth.20221104.15},
  url = {https://doi.org/10.11648/j.earth.20221104.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20221104.15},
  abstract = {The process of hybrid deep learning is highly integrated with the seismic inversion, leading to the black box phenomena, which hampers the understanding of deep-learning inversion process. In this paper, a numerical example is presented to visualize the process of deep learning from the perspective of elastic inversion. Synthetic seismic data is generated by forward modeling on a wedge model, after which the GRU-CNN hybrid deep learning algorithm is applied to obtain the inverted impedance method. In specific, the extraction of local seismic features by CNN, the extraction of low-frequency seismic features by GRU, activation layer, Adam and learning rate schedules, initialization model, loss function, and training process are detailed illustrated and visualized, all of which reveal the internal operating mechanism of the black box. The results show that: 1) after required epoch iterations, the features extracted by CNN becomes close to the real impedance, while the features extracted by GRU is close to the low-frequency information involved in conventional seismic inversion (which is consistent with the cognition from commercial software, e.g., Jason, Geoview, etc.), 2) The learning rate is a very critical parameter in the optimization process. Comparing with the constant learning rate, the cosine learning rate converges faster with better performance, and 3) the initial impedance model in hybrid deep learning is to initialize the weights of all neurons, which is essentially different from those of conventional seismic inversion scheme, e.g., constrained sparse pulse inversion.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Elastic Impedance Inversion with GRU-CNN Hybrid Deep Learning: Visualizing the Black Box
AU  - Xiujuan Liu
AU  - Lifeng Liang
AU  - Zhiming Kang
AU  - Qiang Guo
Y1  - 2022/07/22
PY  - 2022
N1  - https://doi.org/10.11648/j.earth.20221104.15
DO  - 10.11648/j.earth.20221104.15
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 194
EP  - 203
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20221104.15
AB  - The process of hybrid deep learning is highly integrated with the seismic inversion, leading to the black box phenomena, which hampers the understanding of deep-learning inversion process. In this paper, a numerical example is presented to visualize the process of deep learning from the perspective of elastic inversion. Synthetic seismic data is generated by forward modeling on a wedge model, after which the GRU-CNN hybrid deep learning algorithm is applied to obtain the inverted impedance method. In specific, the extraction of local seismic features by CNN, the extraction of low-frequency seismic features by GRU, activation layer, Adam and learning rate schedules, initialization model, loss function, and training process are detailed illustrated and visualized, all of which reveal the internal operating mechanism of the black box. The results show that: 1) after required epoch iterations, the features extracted by CNN becomes close to the real impedance, while the features extracted by GRU is close to the low-frequency information involved in conventional seismic inversion (which is consistent with the cognition from commercial software, e.g., Jason, Geoview, etc.), 2) The learning rate is a very critical parameter in the optimization process. Comparing with the constant learning rate, the cosine learning rate converges faster with better performance, and 3) the initial impedance model in hybrid deep learning is to initialize the weights of all neurons, which is essentially different from those of conventional seismic inversion scheme, e.g., constrained sparse pulse inversion.
VL  - 11
IS  - 4
ER  -

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