Joint learning for seismic inversion: An acoustic impedance estimation case study

@article{Mustafa2020JointLF,
  title={Joint learning for seismic inversion: An acoustic impedance estimation case study},
  author={Ahmad Mustafa and Ghassan AlRegib},
  journal={Seg Technical Program Expanded Abstracts},
  year={2020}
}
  • Ahmad Mustafa, G. AlRegib
  • Published 28 June 2020
  • Computer Science, Engineering, Physics
  • Seg Technical Program Expanded Abstracts
Seismic inversion helps geophysicists build accurate reservoir models for exploration and production purposes. Deep learning-based seismic inversion works by training a neural network to learn a mapping from seismic data to rock properties using well log data as the labels. However, well logs are often very limited in number due to the high cost of drilling wells. Machine learning models can suffer overfitting and poor generalization if trained on limited data. In such cases, well log data from… 

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