Summary Transient electromagnetic (TEM) is an efficient non-invasive method to map electrical conductivity distribution in the subsurface. This paper presents an inversion scheme for three-dimensional (3D) TEM time-lapse data using a generalized minimum support norm and its application to monitoring conductivity changes over time. In particular, two challenges for time-lapse TEM applications are addressed: i) the survey repetition with slightly different acquisition position, i.e. because systems are not installed; ii) non-optimal data coverage above the time-lapse anomalies, for instance, due to the presence of infrastructure that limits the acquisition layout because of coupling. To address these issues, we developed a new TEM time-lapse inversion scheme with the following features: (1) a multi-mesh approach for model definition and forward computations, which allows for seamless integration of datasets with different acquisition layouts; (2) 3D sensitivity calculation during the inversion, which allows retrieving conductivity changes in-between TEM soundings; (3) simultaneous inversion of two datasets at once, imposing time-lapse constraints defined in terms of a generalized minimum support norm, which ensures compact time-lapse changes. We assess the relevance of our implementations through a synthetic example and a field example. In the synthetic example, we study the capability of the inversion scheme to retrieve compact time-lapse changes despite slight changes in the acquisition layout and the effect of data coverage on the retrieval of time-lapse changes. Results from the synthetic tests are used for interpreting field data, which consists of two TEM datasets collected in 2019 and 2020 at the Nesjavellir high-temperature geothermal site (Iceland) within a monitoring project of H2S sequestration. Furthermore, the field example illustrates the effect of the trade-off between data misfit and time-lapse constraints in the inversion objective function, using the tuning settings of the generalized minimum support norm. Based on the results from both the synthetic and field cases, we show that our implementation of 3D time-lapse inversion has a robust performance for TEM monitoring.

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