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: | , , , , |
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| Format: | Article |
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Springer
2020
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| 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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| Summary: | 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. |
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