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Radiation Mapping: A Gaussian Multi-Kernel Weighting Method for Source Investigation in Disaster Scenarios.

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A new multi-kernel Gaussian process regression (MK-GPR) model accurately maps radiation fields in complex, obstructed environments. This advanced radiation mapping improves source localization for emergency response after disasters.

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Area of Science:

  • Nuclear Engineering
  • Environmental Science
  • Robotics

Background:

  • Accidents and disasters can cause structural collapses, creating radiation shielding and complex radiation fields.
  • Traditional radiation mapping methods struggle with sharp gradients and accurate source localization in obstructed environments.
  • Rapid and precise radiation source localization is critical for effective emergency response.

Purpose of the Study:

  • To develop a novel multi-kernel Gaussian process regression (MK-GPR) model for high-fidelity radiation mapping in environments with physical obstructions.
  • To improve the accuracy and efficiency of radiation source localization in post-disaster scenarios.
  • To validate the MK-GPR model's performance against conventional methods using simulations and field experiments.

Main Methods:

  • Proposed a multi-kernel Gaussian process regression (MK-GPR) model integrating kernels for inverse-square law, material attenuation, and energy-dependent penetration.
  • Simulated gamma-ray distributions in complex, shielded environments using GEometry ANd Tracking 4 (Geant4).
  • Deployed the optimized MK-GPR model on an RK-3588 edge computing platform integrated with a mobile robot and NaI(Tl) detector.

Main Results:

  • MK-GPR demonstrated significant improvements in mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute error (MAE) compared to standard methods.
  • Achieved a coefficient of determination (R²) of 0.937, indicating high model accuracy.
  • Field experiments with SLAM achieved localization errors of 10 cm for single sources and 15 cm for dual sources.

Conclusions:

  • The proposed MK-GPR model offers a robust solution for high-fidelity radiation mapping in challenging, obstructed environments.
  • The system's successful deployment on an edge computing platform and mobile robot demonstrates practical applicability for radiation source investigation.
  • This approach provides an effective and deployable tool for radiation source localization in post-disaster emergency response scenarios.