{"paper":{"title":"Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.","cross_cats":["cs.NA","math.NA"],"primary_cat":"stat.AP","authors_text":"Ana Carpio, Babak Maboudi Afkham","submitted_at":"2026-02-01T08:46:45Z","abstract_excerpt":"This paper introduces an infinite-dimensional Bayesian framework for acoustic seabed tomography, leveraging wave scattering to simultaneously estimate the seabed and its roughness. Tomography is considered an ill-posed problem where multiple seabed configurations can result in similar measurement patterns. We propose a novel approach focusing on the statistical isotropy of the seabed. Utilizing fractional differentiability to identify seabed roughness, the paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties. Extensive numerical experiments validate the"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties using an infinite-dimensional Bayesian framework leveraging wave scattering and fractional differentiability under statistical isotropy.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2abfa24304e919e32e81dc4559236c47487b67cec12e537f8574638c4108689e"},"source":{"id":"2602.01099","kind":"arxiv","version":2},"verdict":{"id":"54dfcd8a-c221-488d-bea4-bda77a141f27","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T08:47:50.985233Z","strongest_claim":"The paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties using an infinite-dimensional Bayesian framework leveraging wave scattering and fractional differentiability under statistical isotropy.","one_line_summary":"An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable.","pith_extraction_headline":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering."},"references":{"count":22,"sample":[{"doi":"10.1016/j.amc.2025.129453","year":2025,"title":"[1]C. Abugattas, A. Carpio, E. Cebri ´an, and G. Oleaga,Quantifying uncertainty in inverse scattering problems set in layered environments, Applied Mathematics and Computation, 500 (2025), p. 129453, ","work_id":"e2712637-ea44-4e2a-96da-6f22f6c1873a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/s10851-024-01207-9","year":2024,"title":"ESTIMATION OF SEABED AND ROUGHNESS WITH LONGITUDINAL WAVES29 [4]B. M. Afkham, K. Knudsen, A. K. Rasmussen, and T. Tarvainen,A bayesian approach for consistent reconstruction of inclusions, Inverse Pro","work_id":"c80676bc-672b-4a2c-8ac6-b18c889ae5eb","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1121/10.0037871","year":2025,"title":"[9]J. Bonnel, A. Vardi, J. Leonard, and S. Dosso,From geoacoustic inversion to seabed tomog- raphy using a distributed network of sources and receivers, The Journal of the Acoustical Society of Americ","work_id":"2d701af5-f78a-458e-87ab-c2094203afdc","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1088/1361-6420/acd5f8","year":2023,"title":"[15]A. Carpio, E. Cebri ´an, and A. Guti ´errez,Object based Bayesian full-waveform inversion for shear elastography, Inverse Problems, 39 (2023), p. 075007, https://doi.org/10.1088/ 1361-6420/acd5f8,","work_id":"f67c9091-2a2b-41d1-91ee-3c39fec220ae","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/978-3-319-12385-1","year":2017,"title":"In: Handbook of Uncertainty Quantification, pp","work_id":"04b71a60-724d-4ea5-987b-89e39872fbe9","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":22,"snapshot_sha256":"43fdb30bc46f3d854a71a4cb4a504cb9a7a910157b8c09715bd3868698dcf9ef","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"39681e9ddb54536a1c07f82776b1ce9c11fca1e92d80d5bf652423a364291add"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}