基于缪子成像的三维密度与空气-岩石界面重建：天琴隧道应用
Three-dimensional density and air-rock interface reconstruction with muography: Application to the TianQin tunnel
AI总结：
针对有限测量位置和稀疏数据导致的模糊伪影问题，提出优化的Metropolis-Hastings算法和反距离加权方法，实现高精度三维密度分布和空气-岩石界面重建，并通过天琴隧道实验验证。
摘要：
缪子成像是一种利用宇宙射线缪子的非侵入性成像技术，通常分为透射（吸收）和散射缪子成像。对于透射缪子成像，反演算法对重建质量至关重要。然而，当测量位置有限且数据稀疏时，广泛使用的方案可能会产生模糊伪影。我们开发了一种优化的Metropolis-Hastings（M-H）算法，该算法在不依赖辅助数据的情况下减轻模糊并恢复更清晰、更准确的密度分布。此外，我们实现了一种反距离加权（IDW）方法，从缪子测量中重建空气-岩石界面。将优化的M-H算法应用于蒙特卡洛模拟，并利用MuGrid-v2探测器对天琴隧道实验的现场数据进行处理。通过光探测与测距（LiDAR）测量验证了IDW重建的空气-岩石界面。在模拟中，优化的M-H算法在阈值5.1 g/cm³下将高密度异常检测精度从42%提高到100%，在其他阈值和低密度场景下提高了6%到42%，结合天琴隧道重建结果，证明了所提方法的有效性。
英文摘要：
Muography is a non-invasive imaging technique that uses cosmic-ray muons, commonly divided into transmission (absorption) and scattering muography. For transmission muography, the inversion algorithm critically determines reconstruction quality. However, widely used schemes may produce smearing artifacts when measurement locations are limited and data are sparse. We develop an optimized Metropolis--Hastings (M--H) algorithm that mitigates smearing and retrieves sharper, more accurate density distributions without auxiliary data. Additionally, we implement an inverse distance weighting (IDW) approach to reconstruct the air--rock interface from muon measurements. The optimized M--H algorithm is applied in Monte Carlo simulations and applied to field data from the TianQin Tunnel experiment using the MuGrid-v2 detector. The IDW-reconstructed air--rock interface is validated against Light Detection and Ranging (LiDAR) measurements. In simulations, the optimized M--H algorithm improves high-density anomaly detection precision from $42\%$ to $100\%$ at threshold $5.1\,\mathrm{g/cm^3}$, with gains of $6\%$ to $42\%$ across other threshold and low-density scenarios, together with the TianQin Tunnel reconstructions, these results demonstrate the effectiveness of the proposed approach.
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