The application of a new machine learning paradigm based on pre-training and fine-tuning, StorSeismic, on field seismic data

Randy Harsuko*, Tariq Alkhalifah

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

We often apply a number of processing modules to a field dataset to perform various tasks, like denoising, first arrival picking, velocity estimation, and so on. Selecting the architecture, collecting labels, and tuning a deep learning network for a specific seismic processing task have been common practice in the field, yet these, often independent, networks do not take advantage of the fact that most processing tasks utilize the same features embedded in the seismic data that actually can be stored in a common pre-trained network in a self-supervised manner, we refer to as StorSeismic. In this framework, we utilize a Bidirectional Encoder Representations from Transformers (BERT) model to promote pre-training for storing the features of a seismic dataset and then efficiently fine-tune it to adapt to a wide spectrum of seismic processing tasks. Here, we combine field data, with synthetic generated data, in the self-supervised pre-training step to store the seismic features. Then, we use the labeled synthetic data to fine tune the pre-trained network in a supervised fashion to perform various seismic processing tasks, like denoising, low frequency extrapolation, first arrival picking, and velocity estimation, with satisfactory results.

Original languageEnglish (US)
Pages1610-1614
Number of pages5
DOIs
StatePublished - Aug 15 2022
Event2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022 - Houston, United States
Duration: Aug 28 2022Sep 1 2022

Conference

Conference2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022
Country/TerritoryUnited States
CityHouston
Period08/28/2209/1/22

Bibliographical note

Publisher Copyright:
© 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics

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