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SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents — arXiv:2608.18272

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18272
License (as archived): CC0-1.0
Archived: 2026-10-18T13:44:58.853Z
SHA-256 of the archived text: 95fd04e1ef8ed08c5dc76a60e7093984dafc8ea1dab9a09703b287839336854f
Stable URL of this page: https://metraipolis.com/documento/dc_7c7ba59bf2c2

How to cite

SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents — arXiv:2608.18272 — arXiv (en), https://arxiv.org/abs/2608.18272 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7c7ba59bf2c2 (Metraipolis archive dc_7c7ba59bf2c2, sha256 95fd04e1ef8ed08c5dc76a60e7093984dafc8ea1dab9a09703b287839336854f)

BibTeX

@misc{dc7c7ba59bf2c2,
  title = {SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents — arXiv:2608.18272},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18272},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7c7ba59bf2c2; sha256 95fd04e1ef8ed08c5dc76a60e7093984dafc8ea1dab9a09703b287839336854f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18272. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger than manual trial and error can explore systematically. Deep-learning methods encode the reconstruction rules in learned weights rather than in an explicit operator that can be inspected and modified. We propose SeisEvo (Seismic Algorithm Evolution), which does not optimize a single reconstruction result but searches for the algorithm that produces it. Starting from a classical reconstruction algorithm, an LLM-driven multi-agent search modifies only the components that the user has opened for editing, without prescribing the mechanism to be discovered. Candidates that violate the physical constraints of the task are rejected outright, and the remaining ones are scored by execution. The output is neither an agent system nor a neural network, but a standalone white-box algorithm that requires no agent or neural network at inference time. For interpolation without added noise, the search discovered a residual-gated, phase-aligned dip-consistency projection; Evo-POCS improves the SNR over classic POCS by 3.49 dB on average across missing ratios from 30% to 70%. For simultaneous interpolation and denoising, it discovered a reliability-grouped singular-value shrinkage; Evo-MSSA improves the average reconstruction SNR by more than 7 dB over classic MSSA and by more than 3 dB over a stronger rank-reduction baseline. Both operators retain their gains on data not used during the search. To the best of our knowledge, this is the first study to formulate the design of a seismic reconstruction operator as a constrained, LLM-driven program evolution task. Agentic algorithm evolution can thus complement deep learning in discovering explicit, inspectable, and deployable seismic processing algorithms.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.AI).
Categorie arXiv: Geophysics (physics.geo-ph) ; Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.18272 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:41:17.923Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).