Pith Number

pith:JKV6N3CA

pith:2026:JKV6N3CADLPJA2H4N4WJSRT7X3

not attested not anchored not stored refs resolved

Observation Modeling of Reference--Background Residuals in Single-Snapshot FDA-MIMO-GPR

Jifeng Guo, Yisu Yan

Reference-background residuals in single-snapshot FDA-MIMO-GPR produce structured cross-frequency and cross-channel covariance that appears as low-dimensional pseudo-anomaly errors after Tikhonov reconstruction.

arxiv:2605.17728 v1 · 2026-05-18 · eess.SP

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{JKV6N3CADLPJA2H4N4WJSRT7X3}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Numerical results show pronounced cross-frequency and cross-channel covariance under mismatched reference states. After Tikhonov reconstruction, these structures appear as low-dimensional, concentrated pseudo-anomaly errors.

C2weakest assumption

The distorted Born approximation combined with the Cole-Cole dispersive mapping and reference propagation kernels fully captures the residual response without additional unmodeled propagation effects or higher-order scattering.

C3one line summary

Derives the observation response and covariance of reference-background residuals in FDA-MIMO-GPR and shows they produce low-dimensional pseudo-anomaly errors in Tikhonov reconstructions under mismatched references.

References

47 extracted · 47 resolved · 0 Pith anchors

[1] Electromagnetic determina- tion of soil water content: Measurements in coaxial transmission lines, 1980

[2] Calibration functions for estimating soil moisture from GPR dielectric constant measurements, 2014

[3] Application of ground penetrating radar methods in soil studies: A review, 2019

[4] Soil moisture estimation using tomographic ground penetrating radar in a MCMC–bayesian framework, 2018

[5] Inference of multi-Gaussian relative permittivity fields by probabilistic inversion of crosshole GPR data, 2017

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:04:55.103583Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4aabe6ec401ade9068fc6f2c99467fbef28168c84d04e1c7decb3df21c0750ef

Aliases

arxiv: 2605.17728 · arxiv_version: 2605.17728v1 · doi: 10.48550/arxiv.2605.17728 · pith_short_12: JKV6N3CADLPJ · pith_short_16: JKV6N3CADLPJA2H4 · pith_short_8: JKV6N3CA

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JKV6N3CADLPJA2H4N4WJSRT7X3 \
  | 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: 4aabe6ec401ade9068fc6f2c99467fbef28168c84d04e1c7decb3df21c0750ef

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "89028162cdc1160492c76933d40aa302be8975ee5a1e69ebaf0d0d20601b8c10",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-18T01:13:26Z",
    "title_canon_sha256": "72aec33ff4b8c69712a64cbbb935c48a2202f719991d8ed229ecdad8f5795b94"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17728",
    "kind": "arxiv",
    "version": 1
  }
}