# Inversion-Based Deblending in Common Midpoint Domain Using Time Domain High-Resolution Radon
## 3 High-Performance Computing (HPC) Considerations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Processor (API)** & **Radon Transform Time (s)** & **LS Deblending Time (s)** \\ \hline i7 6800k 6-core (openMP) & 4462 & 2590 \\ i5 8600 6-core (openMP) & 3773 & 1748 \\ i7 8750H 6-core (openMP) & 4044 & 2203 \\ i7 9800\(\times\) 8-core (openMP) & 2715 & 1390 \\ TR 3960\(\times\) 24-core (openMP) & 1123 & 581 \\ RTX 3060 (CUDA) & 147 & 174 \\ RTX 3080 Ti (CUDA) & 108 & 142 \\ RTX 2060 Max-Q (CUDA) & 184 & 206 \\ RTX 2060 Super (CUDA) & 157 & 198 \\ TR 3960\(\times\) (openMPI) & 420 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of average runtimes for Radon and least squares deblending on different machines. operator, allows us to predict and remap, rather than eliminate, interfering shot information. This, in turn, facilitates the accurate recovery of low-amplitude events, even in data that are heavily blended. The implementation of a sparse constraint on the inversion algorithm enables us to arrive at a unique solution that enhances the coherency of the signal in hyperbolic arrivals. Furthermore, the use of hyperbolic Radon transforms in the CMP domain provides us with an operator that is efficient and well matched to our data, promoting coherence in our recovery and circumventing aliasing issues. Consequently, we are confident that our inversion approach is an effective strategy for deblending simultaneously sourced data while safeguarding low-amplitude events. We have also explored additional methods to accelerate this framework through applications on both the CPU and GPU, each with its own set of advantages and disadvantages. Conceptualization, K.Z. and D.T. ; methodology, K.Z. and D.T. ; software, K.Z. ; validation, K.Z., D.T. and A.I. ; formal analysis, K.Z. ; investigation, K.Z. ; data curation, K.Z. and A.I. ; writing--original draft preparation, K.Z. ; writing--review and editing, A.I. and D.T. ; supervision, D.T. All authors have read and agreed to the published version of the manuscript. We thank the sponsors of the Consortium for Research in Elastic Wave Exploration Seismology (CREWES) for their continued support. This work was funded by CREWES industrial sponsors and NSERC (Natural Science and Engineering Research Council of Canada) through the grant CRDPJ 543578-19. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} COF & Common offset \\ CMP & Common midpoint \\ CRG & Common receiver gather \\ HRT & Hyperbolic Radon transform \\ ASHRT & Apex-shifted hyperbolic Radon transform \\ CG & Conjugate gradient \\ IRLS & Iterative reweighted least squares \\ CUDA & Compute Unified Device Architecture (Nvidia programming API) \\ API & Application programming interface \\ \end{tabular} 

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