# ⚛  L1 Principle — Guided-Wave Ultrasonic Testing (long-range pipe/rail inspection)

**ID:** `L1-122` · **Status:** ⊙ Testnet (genesis catalog)

> **🌐 Domain:** Industrial Inspection — *Dispersive Lamb/torsional wave NDT*
> **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** 1D axial defect profile
> **📡 Carrier:** acoustic · **🌫 Noise:** gaussian
> **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41554212

---

## 🧠 1. Introduction

**Guided-Wave Ultrasonic Testing (long-range pipe/rail inspection)** is a **nonlinear inverse problem** whose unknown lives in **1D axial defect profile** space, within the **Dispersive Lamb/torsional wave NDT** sub-domain of **Industrial Inspection**.

Measurements consist of acoustic pressure waves recorded by transducers via a **guided wave ultrasonics** sensing mechanism.

The forward operator applies, in order: L · emit · guided wave operator; L · dispersive propagation operator; D · reflection transmission operator; detector accumulates flux over the exposure window.

Observations are corrupted by additive Gaussian noise. Existence of the recovered 1D axial defect profile is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 14); mode_mixing dominates the stability cliff; temperature_drift and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.

## ⚙ 2. Forward Model

Physical chain: **x** → L · emit · guided wave → L · dispersive propagation → D · reflection transmission → Temporal integration → **y** (detector).

```
y = ∫_t dt `D.reflection_transmission` `L.dispersive_propagation` `L.emit.guided_wave` x + n,    n ~ 𝒩(0, σ²)
```

**Measurement DAG:**

| Primitive | What it does |
|---|---|
| `L.emit.guided_wave` | L · emit · guided wave operator |
| `L.dispersive_propagation` | L · dispersive propagation operator |
| `D.reflection_transmission` | D · reflection transmission operator |
| `int.temporal` | Detector accumulates flux over the exposure window |

## 🔬 3. Physics Fingerprint

| Property | Value |
|---|---|
| Domain | Industrial Inspection |
| Sub domain | Dispersive Lamb/torsional wave NDT |
| Carrier | acoustic |
| Problem class | nonlinear_inverse |
| Solution space | 1D_axial_defect_profile |
| Noise model | gaussian |
| Integration axis | temporal |
| Difficulty delta | 5 |
| L dag | 3.6 |

## 📡 4. Measurement Model

Existence of the recovered 1D axial defect profile is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 14); mode_mixing dominates the stability cliff; temperature_drift and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.

| Metric | Value |
|---|---|
| Metric | PSNR_dB |
| Secondary | SSIM |

## 📏 5. Operating Range (Ω)

**Center problem class:** `guided_wave` · **Forward operator:** `guided_wave_forward`

**Center point:**

| Parameter | Unit | Value |
|---|---|---|
| F khz | kHz | 40 |
| N time | — | 4096 |
| Snr db | dB | 25 |
| N modes | — | 2 |
| Range m | m | 30 |
| Mode mixing | — | 0 |
| Temperature drift | — | 0 |
| Coating attenuation | — | 0 |

**Allowed bounds:**

| Parameter | Unit | Range |
|---|---|---|
| F khz | kHz | 10 – 500 |
| N time | — | 512 – 16384 |
| Snr db | dB | 0.0 – 35.0 |
| N modes | — | 1 – 5 |
| Range m | m | 1 – 200 |
| Mode mixing | — | 0.0 – 0.5 |
| Temperature drift | — | 0.0 – 30.0 |
| Coating attenuation | — | 0.0 – 0.5 |
| Support reflections | — | 0.0 – 0.3 |

## 🎯 6. Tolerance (ε)

**Center tolerance:** 22.0

| Metric | Range |
|---|---|
| Psnr db | 5.0 – 40.0 |

## ⚖ 7. Hardness Function

Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `280` and δ = `5`.

## 💾 8. Reference Dataset

- **primary** · weight 1.0 · IPFS _(not pinned yet)_

## 9. On-chain Registration

- **Chain hash:** `0x6052505f548f08fdda651f8014f2e92283c52fee67ed4eb9126234fec91d0346`
- **Chain tx hash:** `0x490c7bef923c05878d8b8df6cdef48aebf4c1574e3f715f189adf2dfc6623072`
- **Chain block:** `41554212`

---

## File Mapping

This bundle consists of: `L1-122.md`, `L1-122.json`.

| File | Role | How to regenerate |
|------|------|-------------------|
| `L1-122.md` | Source of truth — edit this | Human or LLM |
| `L1-122.json` | Structured metadata for the registry | LLM regenerates from the sections above |

**Prompt for your LLM after editing this Markdown:**

> Read the attached Markdown. Regenerate the sibling `.json` so every field matches.
> Preserve the schema documented in the rows above.
> Output each file in its own fenced code block tagged with the filename.
> Output only the JSON object.

_This Markdown was auto-synthesized from the catalog row for `L1-122`._
_Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._