Pith Number

pith:I5AFQRLJ

pith:2025:I5AFQRLJQ6OF5FLK5LKX2RTTI7

not attested not anchored not stored refs pending

Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learning

Anand Singh, Jochen Kamm, Pankaj K Mishra, Sanni Laaksonen

Implicit neural representations let gravity inversion recover detailed density structures without regularization or depth weighting.

arxiv:2510.17876 v2 · 2025-10-17 · physics.geo-ph · cs.LG

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\usepackage{pith}
\pithnumber{I5AFQRLJQ6OF5FLK5LKX2RTTI7}

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Record completeness

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4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The INR framework reconstructs detailed structure and geologically plausible boundaries without explicit regularisation or depth weighting, while reducing the number of inversion parameters as the problem size grows bigger.

C2weakest assumption

That a coordinate-based neural network trained solely on the physics forward-model loss will converge to a unique, geologically meaningful density field rather than an overfit or non-unique solution, especially when applied to real noisy field data beyond the synthetic tests described.

C3one line summary

Trains a neural network with spatial encoding to represent density continuously and invert 3D gravity data via physics-informed loss without predefined discretization or explicit regularization.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-10T01:09:45.588875Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4740584569879c5e956aead57d467347fa1f3d785ce3d43a410508b8281c9eb9

Aliases

arxiv: 2510.17876 · arxiv_version: 2510.17876v2 · doi: 10.48550/arxiv.2510.17876 · pith_short_12: I5AFQRLJQ6OF · pith_short_16: I5AFQRLJQ6OF5FLK · pith_short_8: I5AFQRLJ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/I5AFQRLJQ6OF5FLK5LKX2RTTI7 \
  | 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: 4740584569879c5e956aead57d467347fa1f3d785ce3d43a410508b8281c9eb9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "242e8e31a9574d2613f8f017580aa0cb0d5a0c0ed3473a754e680680c2f65078",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.geo-ph",
    "submitted_at": "2025-10-17T03:55:08Z",
    "title_canon_sha256": "facf967c586f986613a767c3968f7094a2fd0fb14898022773d72056b2535753"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2510.17876",
    "kind": "arxiv",
    "version": 2
  }
}