1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. EAGE 2020 Annual Conference & Exhibition Online
  5. Conference paper

Abstract

Summary

Numerical integral operators of convolution type lie at the foundations of most wave-equation-based methods for processing and imaging of seismic data. Several of such methods require the solution of an inverse problem, which in turn calls for multiple forward and adjoint passes of the modelling operator. In this abstract we provide some insights into the numerical aspects of solving such systems of integral equations and present a framework that leverages open-source libraries for distributed storage and computing as well as for high-level symbolic representation of linear operators. To validate the effectiveness of our implementation, we evaluate the scalability of the forward and adjoint operations of the well-known time-domain multi-dimensional convolution (MDC) operator with respect to increasing size of the input data and number of computational resources. Finally, we use this implementation to solve the Marchenko equations by means of least-squares inversion for a 3D synthetic dataset composed of up to 9801 sources and receivers.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202010529
2020-12-08
2024-04-25

  • From This Site

    /content/papers/10.3997/2214-4609.202010529
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. [1]DragosetB., E.Verschuur, I.Moore, and R.Bisley, 2010. A perspective on 3D surface-related multiple elimination, Geophysics, 75, 75A245–75A261.
     [Google Scholar]
  2. [2]van Groenestijn, G. J. and D. J.Verschuur, 2009. Estimating primaries by sparse inversion and application to near-offset data reconstruction:Geophysics, Vol. 74, 1MJ–Z54.
     [Google Scholar]
  3. [3]Peng, H., and I.Vasconcelos [2019] - A study of acquisition-related sub-sampling and aperture effects on Marchenko focusing and redatuming.SEG Technical Program Expanded Abstracts, 248–252.
     [Google Scholar]
  4. [4]Lomas, A., and A.Curtis [2019] - Marchenko methods in a 3-D world, GJI, Vol. 220, 296–307.
     [Google Scholar]
  5. [5]Van der Neut, J., Vasconcelos, I., and Wapenaar, K. [2015] On Green’s function retrieval by iterative substitution of the coupled Marchenko equations:GJI, Vol. 203, 792–813.
     [Google Scholar]
  6. [6]Ravasi, M. [2018] An overview of Marchenko-based redatuming: past, present, (and future).80th EAGE Conference Exhibition.
     [Google Scholar]
  7. [7]Ravasi, M., and I.Vasconcelos. [2019] PyLops - A Linear-Operator Python Library for large scale optimization.SoftwareX, Vol. 11, 100361.
     [Google Scholar]
  8. [8]Wapenaar, K., and J. van derNeut, 2010. A representation for Green’s function retrieval by multidimensional deconvolution:JASA, Vol. 128, EL366–EL371.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202010529
Loading
Implementation of Large-Scale Integral Operators with Modern HPC Solutions
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202010529
/content/papers/10.3997/2214-4609.202010529
/content/papers/10.3997/2214-4609.202010529
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error