Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


A Study on the Use of Deep Learning for Detecting Subsurface Structures

Luan Rios Campos, Peterson Nogueira Santos, Davidson Martins Moreira, Erick Giovani Sperandio Nascimento


Beneath the earth there are many structures, such as different types of rocks and salts. Among them are also hydrocarbons that are a valuable resource for the oil and gas industry. One way of studying sub surfaces is using seismograms, which offers a seismic-wave representation with many valuable information of the area. By studying the patterns within the seismic data one can generate a representation of the subsurface based on some parameters that are able to show each one of underlying structures, such as the velocity that the waves propagated. With the advancement of computer-related technology, such as multi-core processors and GPUs, the processing power of computers have increased and the possibility of working with a much larger amount of data and using new and more powerful computational techniques, such as deep learning, was made possible in a variety of fields. Recently, deep learning methods are being applied to solve many geophysical problems, including the estimation of subsurface structures based on the velocity parameter. This work shows an interdisciplinary approach to estimate velocity models from computer modeling seismograms of non-real sub surfaces using a supervised learning artificial intelligence technique. The results obtained can contribute much to the scientific community as it demonstrates how changes in the seismic data modeling process reflects in the velocity model estimation.

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