Deriving thermophysical properties such as thermal inertia from thermal infrared observations provides useful insights into the structure of the surface material on planetary bodies. The estimation of these properties is usually done by fitting temperature variations calculated by thermophysical models to infrared observations. For multiple free model parameters, traditional methods such as Least-Squares fitting or Markov-Chain Monte-Carlo methods become computationally too expensive. Consequently, the simultaneous estimation of several thermophysical parameters together with their corresponding uncertainties and correlations is often not computationally feasible and the analysis is usually reduced to fitting one or two parameters. Data assimilation methods have been shown to be robust while sufficiently accurate and computationally affordable even for a large number of parameters. This paper will introduce a standard sequential data assimilation method, the Ensemble Square Root Filter, to thermophysical modelling of asteroid surfaces. This method is used to re-analyse infrared observations of the MARA instrument, which measured the diurnal temperature variation of a single boulder on the surface of near-Earth asteroid (162173) Ryugu. The thermal inertia is estimated to be $295 \pm 18$ $\mathrm{J\,m^{-2}\,K^{-1}\,s^{-1/2}}$, while all five free parameters of the initial analysis are varied and estimated simultaneously. Based on this thermal inertia estimate the thermal conductivity of the boulder is estimated to be between 0.07 and 0.12 $\mathrm{W\,m^{-1}\,K^{-1}}$ and the porosity to be between 0.30 and 0.52. For the first time in thermophysical parameter derivation, correlations and uncertainties of all free model parameters are incorporated in the estimation procedure which is more than 5000 times more efficient than a comparable parameter sweep.

中文翻译：

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基于数据同化的太阳系小天体热物理建模与参数估计

从热红外观测中推导出热物理特性，例如热惯性，为了解行星体表面材料的结构提供了有用的见解。这些特性的估计通常是通过将热物理模型计算出的温度变化与红外观测值进行拟合来完成的。对于多个自由模型参数，最小二乘拟合或马尔可夫链蒙特卡洛方法等传统方法在计算上变得过于昂贵。因此，同时估计几个热物理参数及其相应的不确定性和相关性在计算上通常是不可行的，分析通常会简化为拟合一两个参数。数据同化方法已被证明是稳健的，同时足够准确并且即使对于大量参数也是计算上负担得起的。本文将介绍一种标准的顺序数据同化方法，即集合平方根滤波器，用于小行星表面的热物理建模。该方法用于重新分析 MARA 仪器的红外观测，该仪器测量了近地小行星 (162173) Ryugu 表面上单个巨石的昼夜温度变化。热惯性估计为 $295 \pm 18$ $\mathrm{J\,m^{-2}\,K^{-1}\,s^{-1/2}}$，而所有五个免费初始分析的参数是同时变化和估计的。基于这种热惯性估计，巨石的热导率估计在 0.07 到 0.12 $\mathrm{W\,m^{-1}\ 之间，K^{-1}}$ 和孔隙率在 0.30 和 0.52 之间。在热物理参数推导中，所有自由模型参数的相关性和不确定性首次被纳入估计程序，其效率是可比参数扫描的 5000 多倍。