We present a nonlinear Bayesian tomographic framework for Doppler spectral imaging that enables simultaneous reconstruction of emissivity, ion temperature, and flow velocity from line-integrated spectra. The method employs nonlinear Gaussian process tomography (GPT) with a Laplace approximation while retaining the full Doppler forward model. A log-Gaussian process prior stabilizes the velocity reconstruction in low-emissivity regions where Doppler information becomes weak, preventing the unphysical divergence of velocity estimates commonly encountered in conventional spectral tomography. The reconstruction method is verified using synthetic phantom data and applied to coherence imaging spectroscopy (CIS) measurements in the RT-1 device, resolving spatial structures of ion temperature and toroidal ion flow characteristic of magnetospheric plasma in the RT-1 device. The framework extends existing CIS tomography to regimes with strong flows and large temperature variations and provides a general Bayesian approach for Doppler spectral tomography that can be integrated with complementary spectroscopic diagnostics.

翻译：本文提出了一种用于多普勒光谱成像的非线性贝叶斯层析框架，能够从线积分光谱中同步重建发射率、离子温度和流速。该方法采用带有拉普拉斯近似的非线性高斯过程层析（GPT），同时保留了完整的多普勒前向模型。对数高斯过程先验在发射率较低、多普勒信息变弱的区域稳定了速度重建，防止了传统光谱层析中常见的速度估计值出现非物理发散。该重建方法通过合成仿体数据进行了验证，并应用于RT-1装置中的相干成像光谱（CIS）测量，成功解析了RT-1装置中磁层等离子体特征的离子温度和环形离子流动的空间结构。该框架将现有的CIS层析扩展到具有强流和大温度变化的区域，并为多普勒光谱层析提供了一种通用的贝叶斯方法，可与互补的光谱诊断技术相结合。