# Inversion-Based Deblending in Common Midpoint Domain Using Time Domain High-Resolution Radon
## 3 High-Performance Computing (HPC) Considerations
Figure 5: Wedge model test results (CMP): Deblending test 1 in CMP domain with (**a**) the pseudo-deblended data, (**b**) the deblended data, and (**c**) the reconstruction error shown, respectively. Figure 6: Wedge model test results (shot domain): Deblending test 1 in shot domain with (**a**) the pseudo-deblended data, (**b**) the deblended data, and (**c**) the reconstruction error shown, respectively. Synthetic test number 2 was generated via finite difference using the standard Marmousi model [21]. As test 1 showed that our deblending engine effectively deblended our data, we decided to make test number 2 more of a stress test, one in which a non-inversion-based deblending method would have difficulty effectively deblending. We significantly increased the difficulty by reducing the range of our random delay times and also increasing the number of overlapping shots to five up from two, conditions we do not expect to see in the real world currently. Figure 8a shows the blended data in the CMP domain; it is easy to visualize how difficult it would be for a normal denoiser without the blending operator to distinguish the data from the interference in this image. Analyzing the results of the second test, we can see in Figure 8 that our recovery of low-amplitude reflections is effective even when completely overwhelmed by high-amplitude interference from other shots. Taking a look at the reconstruction error for the shot domain in Figure 9, we can see that the main differences are reduced amplitudes of the higher-amplitude events and the introduction of very low amplitude background noise; we believe that the amplitude error may come from enforcing too much sparsity in the model space, which generally causes these issues in high-resolution Radon, or not fully separating the blending shots and smearing the higher amplitudes across the data as background noise. Our final test was a real-world dataset pulled from the Gulf of Mexico, known as the BP Tiber WATS dataset [8; 22], which was then numerically blended. The blending scheme was configured differently compared to the others, as this was also a test of another acquisition schedule known as continuous listening. In continuous listening, we effectively have a single supershot, as the data are recorded until all shots are fired and shots are allowed to overlap. The cut-down dataset consisted of 90 shots and 90 receivers, with a shot-to-shot overlap of 70% randomized between 0 and 200 samples. The results of the deblending of the Gulf of Mexico dataset can be seen in the CMP domain in Figure 10 and the shot domain in Figure 11. Analyzing the results, we can see that the deblending algorithm is exceptionally effective at shot separation on the real-world dataset, with good recovery of all events seen in the data. The main error we can observe is that in the shot

Figure 7: Comparison of wedge model results between inversion and denoise approach: inversion and denoising approaches in shot domain, with (**a**) blended, (**b**) inversion deblended, and (**c**) denoise deblended. domain, shots are not perfectly deblended, and some amplitude from the trailing shot can still be seen in the recovered shot. This can be observed around 5.2 s in Figure 11c. Figure 8: Marmousi model test results (CMP): Deblending test 2 with 5 shots, with (**a**) blended, (**b**) deblended, and (**c**) reconstruction error, respectively. Figure 4.0 Marmousi model test results (Shot domain): Deblending test 2 with 5 shots, with (**a**) blended, (**b**) deblended, and (**c**) reconstruction error, respectively. Limitations to this approach lie in the assumptions inherent in the use of the hyperbolic Radon transform. The main assumption of this approach is the hyperbolic trajectory of all seismic arrivals, which are expected to be accurately represented in Radon space. However, this presumption becomes problematic when considering the unique characteristics of land seismic data. The statics inherent to land data can induce vertical shifts in the samples, deviating from their hyperbolic alignment.