Error control and loss functions for the deep learning inversion of borehole resistivity measurements

Mostafa Shahriari, David Pardo, Jon A. Rivera, Carlos Torres-Verdín, Artzai Picon, Javier Del Ser, Sebastian Ossandón, Victor M. Calo

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of borehole logging measurements for oil and gas applications. In this context, DL methods exhibit two key attractive features: (a) once trained, they enable to solve an inverse problem in a fraction of a second, which is convenient for borehole geosteering operations as well as in other real-time inversion applications. (b) DL methods exhibit a superior capability for approximating highly complex functions across different areas of knowledge. Nevertheless, as it occurs with most numerical methods, DL also relies on expert design decisions that are problem specific to achieve reliable and robust results. Herein, we investigate two key aspects of deep neural networks (DNNs) when applied to the inversion of borehole resistivity measurements: error control and adequate selection of the loss function. As we illustrate via theoretical considerations and extensive numerical experiments, these interrelated aspects are critical to recover accurate inversion results.

Original languageEnglish
Pages (from-to)1629-1657
Number of pages29
JournalInternational Journal for Numerical Methods in Engineering
Issue number6
StatePublished - 30 Mar 2021


  • deep learning
  • deep neural networks
  • error estimation
  • geophysical applications
  • real-time inversion


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