A detector-adjacent sampling and reverse mapping method for efficient muography simulation

Muography is a non-invasive imaging technique that uses cosmic-ray muons to investigate the internal structure of objects. Simulation serves as an important tool for assessing the feasibility of muography. However, when imaging large-scale objects, the process can be very time-consuming because many muons do not reach the detector. In this paper, a detector-adjacent sampling and reverse mapping method is introduced to enable efficient muography simulations. The detector-adjacent sampling approach generates muons that both match the expected flux distribution and can reach the detector. The reverse mapping approach then uses this muon information to project back onto the generating surface within the simulation scenario. This combined method improves computational efficiency by more than two orders of magnitude in simulations involving large-scale objects exceeding 10 m, while maintaining consistency with results from direct muon generation. The method is applicable to common muon-generating surfaces, such as those with planar, hemispherical, and cylindrical geometries. Evaluation metrics, including the mean square error, structural similarity index, and Kullback–Leibler divergence, are used to assess the consistency between this method and existing approaches. The results show that the proposed method achieves excellent agreement with direct generation. This framework is further applied to two imaging scenarios: a hollow iron sphere and a mountain. The findings demonstrate that the detector-adjacent sampling and reverse mapping method greatly reduces the simulation time, making it suitable for both small-scale and large-scale muography simulations.