Interpolation Problem on Outlier Contaminated Seismogram Using Extreme Learning Machine

Hilal Nuha, Bo Liu, Mohamed Mohandes*, Ali Al-Shaikhi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this work, we present a weighted l1 norm-based Extreme Learning Machine (ELM), namely, enhanced Regularized ELM (eRELM) for regression problems with outliers contaminated data. The enhancement includes the introduction of a weighting scheme based on l1 norm to a regularization term in the optimization problem. Seismogram is typically recorded with high frequency. Therefore, it requires a large amount of storage. Interpolation allows the seismogram to be stored with the same quality with a smaller number of samples. Our proposed method shows its robustness on interpolation of outlier contaminated seismogram outperforming other ELM based regression techniques by achieving 0.001245 of root mean squared error.

Original languageEnglish
Title of host publicationAdvances in Science, Technology and Innovation
PublisherSpringer Nature
Pages211-213
Number of pages3
DOIs
StatePublished - 2022

Publication series

NameAdvances in Science, Technology and Innovation
ISSN (Print)2522-8714
ISSN (Electronic)2522-8722

Bibliographical note

Funding Information:
Acknowledgments This work is supported by the Center for Energy and Geo Processing (CeGP) at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, under Project GTEC1801.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Extreme learning machine
  • L norm
  • Outlier
  • Seismogram interpolation

ASJC Scopus subject areas

  • Architecture
  • Environmental Chemistry
  • Renewable Energy, Sustainability and the Environment

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