Razorback, an Open Source Python Library for Robust Processing of Magnetotelluric Data
Résumé
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/.
Origine : Fichiers éditeurs autorisés sur une archive ouverte