Magnetotellurics (MT) is a geophysical method that investigates the relationships among the different components of the natural electromagnetic field related to the geoelectric structure of the subsurface. Data can be contaminated by anthropic noise sources and suffer from transient noise to signal variations. Since the 80s, robust processing methods have been introduced to minimize the impact of noise on sounding quality. This paper presents Razorback, an open source Python library, implemented to handle, manipulate, and combine time series of synchronous data. This modular library allows users to plug in data prefilters and includes both M-estimator and bounded influence techniques, as well as a two-stage multiple remote reference. Validation of this library is performed on a real data set by comparing the results with those of an existing code. In contrast to standalone codes, the developed library allows for the design of complex and specific processing procedures. As examples, Razorback is used to perform (i) continuous time lapse processing and (ii) processing of one site in a peri-urban context. In the latter case, we have tested all possible combinations of remote reference stations in an MT array. Our phase tensor analysis shows that the bounded influence outperforms the M-estimator in reducing the impacts of man-made electromagnetic noise on magnetotelluric soundings. The Razorback library is available at https://github.com/BRGM/razorback. Jupyter notebooks for data handling and MT robust processing are available at https://github.com/BRGM/razorback/blob/doc/docs/source/tutorials/.

大地电磁学（MT）是一种地球物理方法，用于研究与地下电磁结构有关的自然电磁场的不同组成部分之间的关​​系。数据可能会受到人为噪声源的污染，并且会受到瞬态噪声到信号变化的影响。从80年代开始，就引入了鲁棒的处理方法，以最大程度地减少噪声对声音质量的影响。本文介绍了Razorback，这是一个开放源代码Python库，已实现，用于处理，操纵和组合同步数据的时间序列。该模块化库允许用户插入数据预过滤器，并且包括M估计器和有限影响技术，以及两阶段的多个远程参考。通过将结果与现有代码的结果进行比较，可以对真实数据集执行此库的验证。与独立代码相反，开发的库允许设计复杂和特定的处理程序。作为示例，Razorback用于执行（i）连续时间推移处理和（ii）在城市周边环境中处理一个站点。在后一种情况下，我们已经测试了MT阵列中远程参考站的所有可能组合。我们的相量张量分析表明，在减少人为电磁噪声对大地电磁测深的影响方面，有限影响优于M估计器。Razorback库位于以下位置：我们已经测试了MT阵列中远程参考站的所有可能组合。我们的相量张量分析表明，在减少人为电磁噪声对大地电磁测深的影响方面，有限影响优于M估计器。Razorback库位于以下位置：我们已经测试了MT阵列中远程参考站的所有可能组合。我们的相量张量分析表明，在减少人为电磁噪声对大地电磁测深的影响方面，有限影响优于M估计器。Razorback库位于以下位置：https://github.com/BRGM/razorback。Jupyter笔记本可用于数据处理和MT稳健处理，网址为https://github.com/BRGM/razorback/blob/doc/docs/source/tutorials/。