Preprints
https://doi.org/10.5194/gmd-2022-66
https://doi.org/10.5194/gmd-2022-66
Submitted as: development and technical paper
16 Mar 2022
Submitted as: development and technical paper | 16 Mar 2022
Status: this preprint is currently under review for the journal GMD.

Massively Parallel Modeling and Inversion of Electrical Resistivity Tomography data using PFLOTRAN

Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson Piyoosh Jaysaval et al.
  • Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA

Abstract. Electrical resistivity tomography (ERT) is a broadly accepted geophysical method for subsurface investigations. Interpretation of field ERT data usually requires the application of computationally intensive forward modeling and inversion algorithms. For large-scale ERT data, the efficiency of these algorithms depends on the robustness, accuracy, and scalability on high-performance computing resources. In this regard, we present a robust and highly scalable implementation of forward modeling and inversion algorithms for ERT data. The implementation is publicly available and developed within the framework of PFLOTRAN, an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The forward modeling is based on a finite volume discretization of the governing differential equations, and the inversion uses a Gauss–Newton optimization scheme. To evaluate the accuracy of the forward modeling, two examples are first presented by considering layered (1D) and 3D earth conductivity models. The computed numerical results show good agreement with the analytical solutions for the layered earth model and results from a well-established code for the 3D model. Inversion of ERT data, simulated for a 3D model, is then performed to demonstrate the inversion capability by recovering the conductivity of the model. To demonstrate the parallel performance of PFLOTRAN’s ERT process model and inversion capabilities, large-scale scalability tests are performed by using up to 131,072 processes on a leadership class supercomputer. These tests are performed for the two most computationally intensive steps of the ERT inversion: forward modeling and Jacobian computation. For the forward modeling, we consider models with up to 122 million degrees of freedom in the resulting system of linear equations, and demonstrate that the code exhibits almost linear scalability on up to 8,192 cores. On the other hand, the code shows perfectly linear scalability for the Jacobian computation, mainly because all computations are fairly evenly distributed over each core with no parallel communication.

Piyoosh Jaysaval et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-66', Mark Everett, 20 Mar 2022
  • RC2: 'Comment on gmd-2022-66', Michael Tso, 04 Aug 2022
  • AC1: 'Authors response on gmd-2022-66', Piyoosh Jaysaval, 02 Sep 2022

Piyoosh Jaysaval et al.

Data sets

ERT modeling and inversion using PFLOTRAN v4.0 Piyoosh Jaysaval; Glenn E. Hammond; Timothy C. Johnson https://doi.org/10.5281/zenodo.6191926

Model code and software

ERT modeling and inversion using PFLOTRAN v4.0 Piyoosh Jaysaval; Glenn E. Hammond; Timothy C. Johnson https://doi.org/10.5281/zenodo.6191926

Piyoosh Jaysaval et al.

Viewed

Total article views: 542 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
402 123 17 542 6 6
  • HTML: 402
  • PDF: 123
  • XML: 17
  • Total: 542
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 16 Mar 2022)
Cumulative views and downloads (calculated since 16 Mar 2022)

Viewed (geographical distribution)

Total article views: 501 (including HTML, PDF, and XML) Thereof 501 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Sep 2022
Download
Short summary
We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.