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| Natura: | Recurso digital |
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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.19449596 |
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| _version_ | 1866901732139728896 |
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| author | Lin Shicong |
| author_facet | Lin Shicong |
| contents | <p>This repository provides the implementation accompanying the manuscript "Seismic Deblending via an Adjacent-CRG-Assisted Multistep Deep Learning Framework."</p> <p>The proposed framework effectively separates blended seismic signals by leveraging the spatial coherence of adjacent common receiver gathers (CRGs). By achieving deblending through a multistep deep learning approach, our method significantly improves the recovery of high-quality seismic records from strong interference.</p> <p>This open-source release aims to support reproducibility and facilitate further research in AI-driven seismic data processing.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19449596 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | heyLeoLin/Adjacent-CRGs-mulstistep-deblending Lin Shicong <p>This repository provides the implementation accompanying the manuscript "Seismic Deblending via an Adjacent-CRG-Assisted Multistep Deep Learning Framework."</p> <p>The proposed framework effectively separates blended seismic signals by leveraging the spatial coherence of adjacent common receiver gathers (CRGs). By achieving deblending through a multistep deep learning approach, our method significantly improves the recovery of high-quality seismic records from strong interference.</p> <p>This open-source release aims to support reproducibility and facilitate further research in AI-driven seismic data processing.</p> |
| title | heyLeoLin/Adjacent-CRGs-mulstistep-deblending |
| url | https://doi.org/10.5281/zenodo.19449596 |