Pith Number

pith:2X3C3MHU

pith:2026:2X3C3MHUGMWXMBLDSNPW64AJYB

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Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves

Ana Carpio, Babak Maboudi Afkham

An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.

arxiv:2602.01099 v2 · 2026-02-01 · stat.AP · cs.NA · math.NA

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Claims

C1strongest 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.

C2weakest assumption

The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable.

C3one line summary

An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability.

References

22 extracted · 22 resolved · 2 Pith anchors

[1] [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, 2025 · doi:10.1016/j.amc.2025.129453

[2] 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 2024 · doi:10.1007/s10851-024-01207-9

[3] [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 2025 · doi:10.1121/10.0037871

[4] [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, 2023 · doi:10.1088/1361-6420/acd5f8

[5] In: Handbook of Uncertainty Quantification, pp 2017 · doi:10.1007/978-3-319-12385-1

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Receipt and verification

First computed	2026-05-18T03:09:24.046148Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51

Aliases

arxiv: 2602.01099 · arxiv_version: 2602.01099v2 · doi: 10.48550/arxiv.2602.01099 · pith_short_12: 2X3C3MHUGMWX · pith_short_16: 2X3C3MHUGMWXMBLD · pith_short_8: 2X3C3MHU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/2X3C3MHUGMWXMBLDSNPW64AJYB \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "592accfab5b48fac937ee9132f9bf36af9a3dcc98b112f41e92a16e767d585ec",
    "cross_cats_sorted": [
      "cs.NA",
      "math.NA"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.AP",
    "submitted_at": "2026-02-01T08:46:45Z",
    "title_canon_sha256": "ae3ab79e6f06fd4df93f3d2720718bfb3e227e1090366031cb81ee4175a96b36"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.01099",
    "kind": "arxiv",
    "version": 2
  }
}