Study of induced magnetic field is a powerful way to sound Earth internal structure. This work presents a full analysis and interpretation in terms of electrical conductivity and temperature of vector magnetic field measurements from Swarm Level1b data product from 26/11/2013 to 31/12/2019. Time series of the Gauss coefficients associated with the induced and inducing magnetic field are obtained from the data after removal of the core and lithospheric fields models and data selection. A Bayesian inversion of the induced field Gauss coefficients is then performed to obtain a new estimate of Earth's 1D mantle electrical conductivity down to 2000 km depth. This profile is fully compatible with the profiles derived from satellite and ground magnetic observatories data but does not present in the lower mantle the increase predicted by laboratory-based conductivity profile associated to classical mantle composition and temperature profile. Using the most recent database to model the electrical conductivity of all mineral mantle phases, two different methods are used to interpret Swarm data in terms of temperature for a given composition and water content. The first one is based on an interpretation of the conductivity estimates in terms of temperature by classical numerical root search. The second one consists in inferring a temperature probability density function from a Bayesian inversion of the Gauss coefficients associated to the induced magnetic field. Our results show that the later provide more reliable estimates of mantle temperatures, in relation to more physically grounded prior values. This second method provides also tighter constraints on the electrical conductivity estimates of the lower mantle.

对感应磁场的研究是一种有效的听起来地球内部结构的方法。这项工作对电导率和Swarm矢量磁场测量的温度进行了全面的分析和解释从2013年11月26日到2019年12月31日的Level1b数据产品。在去除磁心和岩石圈场模型并选择数据之后，从数据中获得与感应磁场和感应磁场相关的高斯系数的时间序列。然后，对感应场高斯系数进行贝叶斯反演，以获得一个新的地球一维地幔电导率的新估计值，该深度一直到2000 km深度。该剖面与从卫星和地面电磁观测数据得出的剖面完全兼容，但在下地幔中不存在与经典地幔成分和温度廓线相关的基于实验室的电导率剖面所预测的增加。使用最新的数据库对所有矿物地幔相的电导率进行建模，可以使用两种不同的方法来解释根据给定成分和水含量的温度来聚集数据。第一个基于通过经典数值根搜索对温度下电导率估算值的解释。第二个在于从与感应磁场相关的高斯系数的贝叶斯反演中推断出温度概率密度函数。我们的结果表明，相对于实际接地的先验值，后者提供了更可靠的地幔温度估算。第二种方法对下地幔的电导率估计值也提供了更严格的约束。