Spark-based intelligent parameter inversion method for prestack seismic data

  • Xuesong Yan
  • Zhixin Zhu
  • Chengyu Hu
  • Wenyin Gong
  • Qinghua Wu
S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems


Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are characterized by large data size and rich information. Rich reservoir parameter information can be obtained through inversion of prestack data. Due to mass prestack seismic data, existing single computer environment cannot satisfy computation requirement of huge data size. Thus, an efficient and fast method is urgently needed to solve the inversion problem of prestack seismic big data. Since local optimum may be easily caught when genetic algorithm is used to optimize elastic parameters, the inversion effect is not obvious. In particular, the optimization effect for the density parameters is not good. An intelligent optimization algorithm is proposed in this paper for elastic parameter inversion of prestack seismic data. The algorithm improves genetic manipulation. The improved algorithm has been used for model trial for log data, and good inversion effect has been achieved. The inverted elastic parameters well fit with the log curve of the theoretical model. The improved algorithm effectively improves the inversion accuracy of density parameters. In this paper, the algorithm has been implemented on Spark model, and the results show that the parallel model can effectively reduce operation time of the algorithm.


Intelligent optimization algorithm Prestack seismic data Elastic parameter inversion Apache Spark 



This paper is supported by Natural Science Foundation of China. (Nos. 61673354 and 61573324), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), the State Key Lab of Digital Manufacturing Equipment and Technology (DMETKF2018020) and the State Key Laboratory of Intelligent Control and Decision of Complex Systems.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Xuesong Yan
    • 1
    • 2
  • Zhixin Zhu
    • 1
  • Chengyu Hu
    • 1
  • Wenyin Gong
    • 1
  • Qinghua Wu
    • 3
  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.Faculty of Computer Science and EngineeringWuHan Institute of TechnologyWuhanChina

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