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Yuanyuan Wang
Henan Polytechnic University
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Seismic attributes, which are extracted from seismic information, are physical indexes used specificallyfor the measurement of geometric, dynamic, or statistical characteristics of seismic data. Current methods for seismic multi-attribute inversion include linear and nonlinear methods. By adopting the wireless module of NFC24l01, combined with the seismic data acquisition sensor, constitutes an intelligent network sensor, and then it sends the collected data to the topmost machine for analysis. Methods for the nonlinear inversion of seismic multi-attributes usually employ tools such as neural networks and support vector machines (SVMs)for mapping. Hence, inversion results obtained via nonlinear methodsare more accurate than those obtained via linear methods. In this work, with spontaneous-potential (SP) curves as the objective of nonlinear inversion, an optimized seismic attribute combination for the inversion of SP curves was identified, and the nonlinear inversion of seismic multi-attributes was achieved via the use of a deep neural network (DNN) to obtain 3D SP data. Finally, the foresetting process of a sand body of the intermediate section in Member 3 of the Shahejie Formation in the Dongying Delta was illustrated via the horizon slice of the SP data.
Keywords
wireless network sensor, seismic multi-attribute inversion, deep neural network (DNN), delta foreset
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CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
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Posted 23 Jul, 2020
No community comments so far
Editorial decision: Accept
On 02 Aug, 2020
Review #2 received
Received 29 Jul, 2020
Review #3 received
Received 29 Jul, 2020
Review #1 received
Received 28 Jul, 2020
Reviewer #3 agreed
On 20 Jul, 2020
Reviewers invited
Invitations sent on 19 Jul, 2020
Reviewer #1 agreed
On 19 Jul, 2020
Reviewer #2 agreed
On 19 Jul, 2020
Editor assigned
On 16 Jul, 2020
Submission checks complete
On 15 Jul, 2020
Editor invited
On 15 Jul, 2020
No comments provided
Editorial decision: Major revision
On 13 Jul, 2020
Review #2 received
Received 27 Jun, 2020
Review #1 received
Received 27 Jun, 2020
Review #3 received
Received 27 Jun, 2020
Reviewer #3 agreed
On 13 Jun, 2020
Reviewers invited
Invitations sent on 12 Jun, 2020
Reviewer #1 agreed
On 12 Jun, 2020
Reviewer #2 agreed
On 12 Jun, 2020
Editor assigned
On 11 Jun, 2020
Editor invited
On 10 Jun, 2020
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On 27 May, 2020
First submitted
On 25 May, 2020
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See the published version of this preprint at EURASIP Journal on Wireless Communications and Networking.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Yuanyuan Wang
Henan Polytechnic University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 2
Posted 23 Jul, 2020
No community comments so far
Editorial decision: Accept
On 02 Aug, 2020
Review #2 received
Received 29 Jul, 2020
Review #3 received
Received 29 Jul, 2020
Review #1 received
Received 28 Jul, 2020
Reviewer #3 agreed
On 20 Jul, 2020
Reviewers invited
Invitations sent on 19 Jul, 2020
Reviewer #1 agreed
On 19 Jul, 2020
Reviewer #2 agreed
On 19 Jul, 2020
Editor assigned
On 16 Jul, 2020
Submission checks complete
On 15 Jul, 2020
Editor invited
On 15 Jul, 2020
No comments provided
Editorial decision: Major revision
On 13 Jul, 2020
Review #2 received
Received 27 Jun, 2020
Review #1 received
Received 27 Jun, 2020
Review #3 received
Received 27 Jun, 2020
Reviewer #3 agreed
On 13 Jun, 2020
Reviewers invited
Invitations sent on 12 Jun, 2020
Reviewer #1 agreed
On 12 Jun, 2020
Reviewer #2 agreed
On 12 Jun, 2020
Editor assigned
On 11 Jun, 2020
Editor invited
On 10 Jun, 2020
Submission checks complete
On 27 May, 2020
First submitted
On 25 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Wireless Communications and Networking.
Seismic attributes, which are extracted from seismic information, are physical indexes used specificallyfor the measurement of geometric, dynamic, or statistical characteristics of seismic data. Current methods for seismic multi-attribute inversion include linear and nonlinear methods. By adopting the wireless module of NFC24l01, combined with the seismic data acquisition sensor, constitutes an intelligent network sensor, and then it sends the collected data to the topmost machine for analysis. Methods for the nonlinear inversion of seismic multi-attributes usually employ tools such as neural networks and support vector machines (SVMs)for mapping. Hence, inversion results obtained via nonlinear methodsare more accurate than those obtained via linear methods. In this work, with spontaneous-potential (SP) curves as the objective of nonlinear inversion, an optimized seismic attribute combination for the inversion of SP curves was identified, and the nonlinear inversion of seismic multi-attributes was achieved via the use of a deep neural network (DNN) to obtain 3D SP data. Finally, the foresetting process of a sand body of the intermediate section in Member 3 of the Shahejie Formation in the Dongying Delta was illustrated via the horizon slice of the SP data.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
The full text of this article is available to read as a PDF.
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