Abstract
The parallelisation of big data is emerging as an important framework for large-scale parallel data applications such as seismic data processing. The field of seismic data is so large or complex that traditional data processing software is incapable of dealing with it. For example, the implementation of parallel processing in seismic applications to improve the processing speed is complex in nature. To overcome this issue, a simple technique which that helps provide parallel processing for big data applications such as seismic algorithms is needed. In our framework, we used the Apache Hadoop with its MapReduce function. All experiments were conducted on the RedHat CentOS platform. Finally, we studied the bottlenecks and improved the overall performance of the system for seismic algorithms (stochastic inversion).
Highlights
Oil and gas companies use seismic exploration technology to find oil and gas
The original parallel processing was compared with the Hadoop with multiple data nodes (DN), such as DN1, DN2 and DN3
Hadoop took 84 minutes, 42 minutes and 30 minutes, respectively, which were better than Hadoop with 32 hyper threads
Summary
Oil and gas companies use seismic exploration technology to find oil and gas. Many researchers [1, 2, 3] have used parallel processing techniques (for big data) to reduce the processing time. The processing technology for seismic data is different than normal big data processing technologies. Seismic attribute data are needed due to the pre-processing of input seismic data. Seismic attribute data help improve the utilization value of original seismic data, and improve the application level of seismic technology in big data applications in the industry. Seismic data (which are extracted as an attribute from original seismic data) will be saved into a file. These seismic data, or attribute data, will be suitable for parallelisation using Hadoop technology
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