Three-dimensional time-lapse inversion of transient electromagnetic data, with application at an Icelandic geothermal site

Abstract

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 3-D TEM time-lapse (TL) data using a generalized minimum support (MS) norm and its application to monitoring conductivity changes over time. In particular, two challenges for TL TEM applications are addressed: (i) the survey repetition with slightly different acquisition position, that is, because systems are not installed and (ii) non-optimal data coverage above the TL 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 TL inversion scheme with the following features: (1) a multimesh approach for model definition and forward computations, which allows for seamless integration of data sets with different acquisition layouts; (2) 3-D sensitivity calculation during the inversion, which allows retrieving conductivity changes in-between TEM soundings and (3) simultaneous inversion of two data sets at once, imposing TL constraints defined in terms of a generalized MS norm, which ensures compact TL 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 TL changes. Results from the synthetic tests are used for interpreting field data, which consists of two TEM data sets 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 TL constraints in the inversion objective function, using the tuning settings of the generalized MS norm. Based on the results from both the synthetic and field cases, we show that our implementation of 3-D TL inversion has a robust performance for TEM monitoring.