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GP-PH-08YF9SPPhysicsReady

Muon Tomography for Structural and Geological Inspection

A completed physics study comparing eight cosmic-ray muon-imaging system archetypes for structural, geological and shielded-object inspection.

Muon Tomography for Structural and Geological Inspection project visual
GP-PH-08YF9SP · Physics
  • Python 3.12
  • NumPy
  • pandas
  • Matplotlib

Software compatibility

Python 3.12 or later on macOS, Linux and Windows

The released results use the frozen 4 September 2026 evidence catalogue, pinned Python packages, 20,000 uncertainty draws per system and random seed 20260904. Site-specific use requires measured detector and survey data.

Project definition

Problem statement

Muon imaging performance cannot be judged from detector area or exposure time alone. Particle flux, material thickness, geometry, angular resolution, background, calibration and reconstruction all affect what can be resolved.

The physics problem is to compare transmission and scattering system archetypes without turning literature evidence positions into unsupported site-specific detection claims.

Project objectives

  • Compare eight detector-system archetypes across six structural, geological and shielded-object targets.
  • Separate transmission attenuation from multiple Coulomb scattering applications.
  • Evaluate four deployment contexts and four evidence cases through one consistent framework.
  • Map detector, geometry, exposure, reconstruction and validation limitations.
  • Test decision stability through scenario, sensitivity and fixed-seed uncertainty analysis.
  • Retain complete results, figures, tests, references and editable documentation.

Project structure

Project components

01

System register

Defines eight transmission and scattering detector archetypes with declared performance and evidence positions.

02

Target model

Represents geological overburden, tunnels, concrete structures, industrial vessels, cargo and shielded dense objects.

03

Comparison engine

Calculates screening, evidence and gate outcomes for 768 system, target, context and evidence combinations.

04

Decision analysis

Compares balanced, geological, civil and assurance priorities and tests sensitivity to evidence positions.

05

Validation planning

Uses fifteen criteria, workflow FMEA and an assurance crosswalk to define the work required before field claims.

06

Evidence package

Retains CSV, JSON, PNG, Word, PDF, references, tests and Docker reproduction.

Methodology

Project workflow

  1. 01
    Define the inspection question

    Specify the target, density contrast, geometry, access, decision threshold and permitted conclusion.

  2. 02
    Select the imaging mode

    Choose transmission or scattering according to thickness, target scale and detector placement.

  3. 03
    Plan the measurement

    Declare acceptance, exposure, angular coverage, calibration, background and environmental controls.

  4. 04
    Reconstruct and compare

    Process retained evidence while tracking resolution, artefacts, uncertainty and alternative explanations.

  5. 05
    Validate the claim

    Use reference targets, blind cases, repeat measurements and qualified domain review before operational use.

Demonstration scenario

The completed study compares a tunnel and rock-overburden inspection across transmission detector systems, then contrasts that decision with a dense shielded-object case suited to scattering tomography. The retained scenarios show why the preferred system changes when geometry, exposure, access or assurance priorities change.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, pandas, Matplotlib for subject analysis, simulation, and results.
Declared inputs
Python data structures retain every detector, target, context and evidence position.
Deterministic analysis
NumPy and pandas generate all 768 comparison cases and four decision scenarios.
Risk analysis
Validation criteria, workflow FMEA and assurance links expose the highest-priority evidence gaps.
Uncertainty
Fixed-seed Monte Carlo and one-factor sensitivity test whether the comparative ranking is stable.
Reporting
Matplotlib, CSV, JSON, Word and PDF retain the method, results and evidence boundaries.
Verification
Tests, linting, vulnerability audit, document QA, repository validation and Docker verify the handover.

Testing

Evaluation

Evaluation measures

  • Screening and evidence scores by detector, target and deployment context
  • Transmission and scattering suitability for each inspection target
  • Gate-pass rate and severity-weighted validation gaps
  • Balanced, geological, civil and assurance scenario rankings
  • Workflow FMEA priorities and assurance coverage
  • Monte Carlo intervals and evidence-position sensitivity

Project boundaries

  • All zero-to-ten inputs are literature-informed positions rather than measurements from one controlled detector campaign.
  • The study does not claim a site-specific detection limit, spatial resolution, exposure duration or probability of detection.
  • It does not provide radiological clearance, structural certification, geological interpretation or procurement advice.
  • The comparison does not replace raw counts, detector-response calibration, background treatment or reconstruction validation.
  • A real survey requires measured geometry, calibration, reference targets, uncertainty and qualified domain review.

Included

  1. 01Complete Python source and declared study configuration
  2. 02Eight detector-system archetypes and six inspection targets
  3. 03768 system, target, context and evidence cases
  4. 04Four decision scenarios and 20,000 fixed-seed uncertainty draws per system
  5. 05Fifteen validation criteria, 180 workflow FMEA cells and 120 assurance links
  6. 06Twelve generated figures and complete CSV result tables
  7. 07101-page project report in PDF and editable Word formats
  8. 0816-page setup and usage guide in PDF and editable Word formats
  9. 09Sixty annotated references and one attributed literature photograph
  10. 10Automated tests, repository validation and Docker reproduction

Project record

No information is collected on this page.

Permanent project ID
GP-PH-08YF9SP
Catalogued
21 Aug 2026
Completed
04 Sept 2026
Verified
04 Sept 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.