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A Study on Seismic Big Data Handling at Seismic Exploration Industry

Cumulative size as well as a changeable pattern of composed geographical large data boons issues in storage, handling, unfolding, studying, anticipating and proving the eminence of input data files. These issues become big challenges, especially in the oil and gas industries. At the same time, seism...

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Main Authors: Bhattacharjee, S., Rahim, L.B.A., Ramadhani, A.W., Midhunchakkravarthy,, Midhunchakkravarthy, D.
Format: Article
Published: Springer 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085162069&doi=10.1007%2f978-981-15-3284-9_48&partnerID=40&md5=15a368c0a78f2c581a3a12d705a6ae5d
http://eprints.utp.edu.my/24794/
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spelling my.utp.eprints.247942021-08-27T06:25:08Z A Study on Seismic Big Data Handling at Seismic Exploration Industry Bhattacharjee, S. Rahim, L.B.A. Ramadhani, A.W. Midhunchakkravarthy, Midhunchakkravarthy, D. Cumulative size as well as a changeable pattern of composed geographical large data boons issues in storage, handling, unfolding, studying, anticipating and proving the eminence of input data files. These issues become big challenges, especially in the oil and gas industries. At the same time, seismic exploration is to cultivate an image of the subsurface geology. The geophysical exploration in overall and seismic acquisition in specific is challenged vastly in terms of the tough logistics and intricate subsurface geology. Hence, this research proposes a unified technique to figure out time complexity in large seismic data dispensation with parallel processing, smart indexing and reducing latency time. Furthermore, this research uses a combined platform of Hadoop and Hive where MapReduce analyzes the data and HDFS stores it after processing. The result shows its high time efficiencies by offering high throughputs, I/O rates as well as low latencies. © Springer Nature Singapore Pte Ltd. 2020. Springer 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085162069&doi=10.1007%2f978-981-15-3284-9_48&partnerID=40&md5=15a368c0a78f2c581a3a12d705a6ae5d Bhattacharjee, S. and Rahim, L.B.A. and Ramadhani, A.W. and Midhunchakkravarthy, and Midhunchakkravarthy, D. (2020) A Study on Seismic Big Data Handling at Seismic Exploration Industry. Lecture Notes in Networks and Systems, 118 . pp. 421-429. http://eprints.utp.edu.my/24794/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Cumulative size as well as a changeable pattern of composed geographical large data boons issues in storage, handling, unfolding, studying, anticipating and proving the eminence of input data files. These issues become big challenges, especially in the oil and gas industries. At the same time, seismic exploration is to cultivate an image of the subsurface geology. The geophysical exploration in overall and seismic acquisition in specific is challenged vastly in terms of the tough logistics and intricate subsurface geology. Hence, this research proposes a unified technique to figure out time complexity in large seismic data dispensation with parallel processing, smart indexing and reducing latency time. Furthermore, this research uses a combined platform of Hadoop and Hive where MapReduce analyzes the data and HDFS stores it after processing. The result shows its high time efficiencies by offering high throughputs, I/O rates as well as low latencies. © Springer Nature Singapore Pte Ltd. 2020.
format Article
author Bhattacharjee, S.
Rahim, L.B.A.
Ramadhani, A.W.
Midhunchakkravarthy,
Midhunchakkravarthy, D.
spellingShingle Bhattacharjee, S.
Rahim, L.B.A.
Ramadhani, A.W.
Midhunchakkravarthy,
Midhunchakkravarthy, D.
A Study on Seismic Big Data Handling at Seismic Exploration Industry
author_facet Bhattacharjee, S.
Rahim, L.B.A.
Ramadhani, A.W.
Midhunchakkravarthy,
Midhunchakkravarthy, D.
author_sort Bhattacharjee, S.
title A Study on Seismic Big Data Handling at Seismic Exploration Industry
title_short A Study on Seismic Big Data Handling at Seismic Exploration Industry
title_full A Study on Seismic Big Data Handling at Seismic Exploration Industry
title_fullStr A Study on Seismic Big Data Handling at Seismic Exploration Industry
title_full_unstemmed A Study on Seismic Big Data Handling at Seismic Exploration Industry
title_sort study on seismic big data handling at seismic exploration industry
publisher Springer
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085162069&doi=10.1007%2f978-981-15-3284-9_48&partnerID=40&md5=15a368c0a78f2c581a3a12d705a6ae5d
http://eprints.utp.edu.my/24794/
_version_ 1738656640357367808
score 13.211869

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