S U M M A R YRobust estimates of the magnetotelluric (MT) transfer function are found using an iterative reweighted method on time series data corrected for outliers and gaps. The MT transfer function, composed of several analytic functions smoothly varying in frequency, is used to represent the frequency-domain relationship between electric and magnetic time series. The smoothly varying transfer function facilitates identification and removal of electric and magnetic outliers (spikes), construction of the frequencyand time-domain weights used for obtaining robust smooth and band-averaged estimates, and separation of the time series into MT and correlated noise signals if a remote site exists that is free of the correlated noise. Errors in the transfer function are calculated using jackknife estimates of the solution covariance. The method is tested on: time series from a relatively clean MT site in central California; a test time series based on Tucson magnetic time series plus synthetic noise for a given transfer function; and time series from the Larderello geothermal region in central Italy where there are strong signals from d.c. electrified railways.

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Abstract. In this study the resource base for EGS (enhanced geothermal systems) in Europe was quantified and economically constrained, applying a discounted cash-flow model to different techno-economic scenarios for future EGS in 2020EGS in , 2030EGS in , and 2050. Temperature is a critical parameter that controls the amount of thermal energy available in the subsurface. Therefore, the first step in assessing the European resource base for EGS is the construction of a subsurface temperature model of onshore Europe. Subsurface temperatures were computed to a depth of 10 km below ground level for a regular 3-D hexahedral grid with a horizontal resolution of 10 km and a vertical resolution of 250 m. Vertical conductive heat transport was considered as the main heat transfer mechanism. Surface temperature and basal heat flow were used as boundary conditions for the top and bottom of the model, respectively. If publicly available, the most recent and comprehensive regional temperature models, based on data from wells, were incorporated.With the modeled subsurface temperatures and future technical and economic scenarios, the technical potential and minimum levelized cost of energy (LCOE)

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