L1 ⚛ L1-422 ⊙ Testnet

Sediment Transport Inversion

Environmental Science · Geomorphology

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⬇ L1-422.md

# ⚛ L1 Principle — Sediment Transport Inversion **ID:** `L1-422` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Environmental Science — *Geomorphology* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** sediment flux parameter vector > **📡 Carrier:** N/A · **🌫 Noise:** lognormal > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41555239 --- ## 🧠 1. Introduction **Sediment Transport Inversion** is a **parameter-estimation problem** whose unknown lives in **sediment flux parameter vector** space, within the **Geomorphology** sub-domain of **Environmental Science**. Measurements consist of N/A via a **bedload suspended measurement** sensing mechanism. The forward operator applies, in order: applies a smooth nonlinear function element-wise; S · vanrijn · suspended load operator; O · chi2 · bedload flux operator. Observations are corrupted by log-normal multiplicative noise. Existence of the recovered sediment_flux_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 40); armoring_effect dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Lognormal sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → Pointwise nonlinearity → S · vanrijn · suspended load → O · chi2 · bedload flux → **y** (detector). ``` y = `O.chi2.bedload_flux` `S.vanrijn.suspended_load` f(·) x ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `N.pointwise` | Applies a smooth nonlinear function element-wise | | `S.vanrijn.suspended_load` | S · vanrijn · suspended load operator | | `O.chi2.bedload_flux` | O · chi2 · bedload flux operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Environmental Science | | Sub domain | Geomorphology | | Carrier | N/A | | Problem class | parameter_estimation | | Solution space | sediment_flux_parameter_vector | | Noise model | lognormal | | Integration axis | channel_cross_section | | Difficulty delta | 3 | | L dag | 2.8 | ## 📡 4. Measurement Model Existence of the recovered sediment_flux_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 40); armoring_effect dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Lognormal sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | bedload_flux_RMSE_log | | Secondary | sediment_rating_curve_RMSE | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `bedload_suspended_measurement` **Center point:** | Parameter | Unit | Value | |---|---|---| | D50 mm | mm | 2 | | N measurements | — | 100 | | Discharge m3 s | s | 500 | | Critical shields tau*c | — | 0.047 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | D50 mm | mm | 0.1 – 100.0 | | N measurements | — | 10 – 1000 | | Discharge m3 s | s | 1 – 100000 | | Critical shields tau*c | — | 0.02 – 0.1 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.5 bedload_flux_RMSE_log | Metric | Range | |---|---| | Bedload flux rmse log | 0.1 – 2.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **bedload_flux_RMSE_log**, with κ = `1000` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x64e4ec96dd87f1fe73dab04733c2fcc411af3f88d419c32d83a8bf71d50bcb4c` - **Chain tx hash:** `0x48e68af5737c0ee07a49eb882024e555e613f4998b11466e99a895d1f28d6e99` - **Chain block:** `41555239` --- ## File Mapping This bundle consists of: `L1-422.md`, `L1-422.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-422.md` | Source of truth — edit this | Human or LLM | | `L1-422.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 `L1-422`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-422.md).
Regenerate L1-422.json so every field matches the Markdown.
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L2-422-001 Sediment Transport Inversion — Nominal + Mismatch Specs

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📋 JSON metadata

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  "chain_hash": "0x64e4ec96dd87f1fe73dab04733c2fcc411af3f88d419c32d83a8bf71d50bcb4c",
  "chain_tx_hash": "0x48e68af5737c0ee07a49eb882024e555e613f4998b11466e99a895d1f28d6e99",
  "domain": "Environmental Science",
  "hardness_fn": {
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    "kappa": 1000,
    "metric": "bedload_flux_RMSE_log",
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    "regime": "Existence of the recovered sediment_flux_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 40); armoring_effect dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Lognormal sets the irreducible data-fidelity floor.",
    "secondary": "sediment_rating_curve_RMSE"
  },
  "physics_fingerprint": {
    "L_DAG": 2.8,
    "carrier": "N/A",
    "difficulty_delta": 3,
    "domain": "Environmental Science",
    "integration_axis": "channel_cross_section",
    "noise_model": "lognormal",
    "primitives": [
      "N.pointwise",
      "S.vanrijn.suspended_load",
      "O.chi2.bedload_flux"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "bedload_suspended_measurement",
    "solution_space": "sediment_flux_parameter_vector",
    "sub_domain": "Geomorphology",
    "title": "Sediment Transport Inversion"
  },
  "size_tiers": {
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      "bedload_suspended_measurement"
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    "allowed_omega_dimensions": [
      "N_measurements",
      "d50_mm",
      "critical_shields_tau*c",
      "discharge_m3_s"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "0.5 bedload_flux_RMSE_log",
      "forward_operator": "bedload_suspended_measurement",
      "input_format": "measurement_only",
      "omega": {
        "N_measurements": 100,
        "critical_shields_tau*c": 0.047,
        "d50_mm": 2.0,
        "discharge_m3_s": 500
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "bedload_flux_RMSE_log": [
        0.1,
        2.0
      ]
    },
    "omega_bounds": {
      "N_measurements": [
        10,
        1000
      ],
      "critical_shields_tau*c": [
        0.02,
        0.1
      ],
      "d50_mm": [
        0.1,
        100.0
      ],
      "discharge_m3_s": [
        1,
        100000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Geomorphology",
  "title": "Sediment Transport Inversion"
}

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