A priori knowledge on large-scale sub-surface conductivity structure is required in many applications investigating electrical properties of the lithosphere. A map on crustal conductivity for the Fennoscandian Shield and its surrounding oceans, sea basins and continental areas is presented. The map is based on a new database on crustal conductance, i.e. depth integrated conductivity, where all available information on the conductivity of the bedrock, sedimentary cover and seawater are compiled together for the first time for the Fennoscandian Shield. The final model consists of eight separate layers to allow a 3D description of conductivity structures. The first three layers, viz. water, sediments and the first bedrock layer, describe the combined conductance of the uppermost 10 km. The other five bedrock layers contain the data of the crustal conductance from the depth of 10 km to the depth of 60 km. The database covers an area from 0• E to 50• E and 50• N to 85• N. Water conductances are estimated from bathymetric data by converting depths to conductances and taking into account the salinity variations in the Baltic Sea. Conductance of the sedimentary cover includes estimates on the conductance of both marine and continental sediments. Bedrock conductances are extrapolated from 1D-and 2D-models. Extrapolations are based on data from magnetometer array studies, airborne electromagnetic surveys and other electromagnetic investigations as well as on other geophysical and geological data. The crustal conductivity structure appears to be very heterogeneous. Upper crust, in particular, has a very complex structure reflecting a complex geological history. Lower crust seems to be slightly more homogeneous although large regional contrasts are found in both the Archaean and Palaeoproterozoic areas.

SUMMARY A new robust magnetotelluric (MT) data processing algorithm is described, involving Siegel estimation on the basis of a repeated median (RM) algorithm for maximum protection against the influence of outliers and large errors. The spectral transformation is performed by means of a fast Fourier transformation followed by segment coherence sorting. To remove outliers and gaps in the time domain, an algorithm of forward autoregression prediction is applied. The processing technique is tested using two 7 day long synthetic MT time‐series prepared within the framework of the COMDAT processing software comparison project. The first test contains pure MT signals, whereas in the second test the same signal is superimposed on different types of noise. To show the efficiency of the algorithm some examples of real MT data processing are also presented.

Abstract. Geomagnetically induced currents (GICs) are directly described by ground electric fields, but estimating them is time-consuming and requires knowledge of the ionospheric currents and the three-dimensional (3D) distribution of the electrical conductivity of the Earth. The time derivative of the horizontal component of the ground magnetic field (dH∕dt) is closely related to the electric field via Faraday's law and provides a convenient proxy for the GIC risk. However, forecasting dH∕dt still remains a challenge. We use 25 years of 10 s data from the northern European International Monitor for Auroral Geomagnetic Effects (IMAGE) magnetometer network to show that part of this problem stems from the fact that, instead of the primary ionospheric currents, the measured dH∕dt is dominated by the signature from the secondary induced telluric currents at nearly all IMAGE stations. The largest effects due to telluric currents occur at coastal sites close to high-conducting ocean water and close to near-surface conductivity anomalies. The secondary magnetic field contribution to the total field is a few tens of percent, in accordance with earlier studies. Our results have been derived using IMAGE data and are thus only valid for the stations involved. However, it is likely that the main principle also applies to other areas. Consequently, it is recommended that the field separation into internal (telluric) and external (ionospheric and magnetospheric) parts is performed whenever feasible (i.e., a dense observation network is available).

SUMMARY We describe a new algorithm for robust principal component analysis (PCA) of electromagnetic (EM) array data, extending previously developed multivariate methods to include arrays with large data gaps, and only partial overlap between site occupations. Our approach is based on a criss‐cross regression scheme in which polarization parameters and spatial modes are alternately estimated with robust regression procedures. The basic scheme can be viewed as an expectation robust (ER) algorithm, of the sort that has been widely discussed in the statistical literature in the context of robust PCA, but with details of the scheme tailored to the physical specifics of EM array observations. We have tested our algorithm with synthetic and real data, including data denial experiments where we have created artificial gaps, and compared results obtained with full and incomplete data arrays. These tests reveal that for modest amounts of missing data (up to 20 per cent or so) the algorithm performs well, reproducing essentially the same dominant spatial modes that would be obtained from analysis of the complete array. The algorithm thus makes multivariate analysis practical for the first time for large heterogeneous arrays, as we illustrate by application to two different EM arrays.