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The possibility of solving the three‐dimensional (3‐D) inverse problem of geoelectrics using the artificial neural network (ANN) approach is investigated. The properties of a supervised ANN based on the back‐propagation scheme with three layers of neurons are studied, and the ANN architecture is adjusted. A model class consisting of a dipping dyke in the basement of a two‐layer earth with the dyke in contact with the overburden is used for numerical experiments. Six macroparameters of the 3‐D model, namely the thickness of the top layer, which coincides with the depth of the dyke (D), the conductivity ratio between the first and second layers (C1 /C2 ), the conductivity contrast of the dyke (C/C2 ), and the width (W ), length (L ) and dip angle of the dyke (A), are used. Various groups of magnetotelluric field components and their transformations are studied in order to estimate the effect of the data type used on the ANN recognition ability. It is found that use of only the xy‐ and yx‐components of impedance phases results in reasonable recognition errors for all unknown parameters (D: 0.02 per cent, C1/C2: 8.4 per cent, C/C2: 26.8 per cent, W : 0.02 per cent, L : 0.02 per cent, A: 0.24 per cent). The influence of the size and shape of the training data pool (including the ‘gaps in education’ and ‘no target’ effects) on the recognition properties is studied. Results from numerous ANN tests demonstrate that the ANN possesses good enough interpolation and extrapolation abilities if the training data pool contains a sufficient number of representative data sets. The effect of noise is estimated by means of mixing the synthetic data with 30, 50 and 100 per cent Gaussian noise. The unusual behaviour of the recognition errors for some of the model parameters when the data become more noisy (in particular, the fact that an increase in error is followed by a decrease) indicates that the use of standard techniques of noise reduction may give an opposite result, so the development of a special noise treatment methodology is required. Thus, it is shown that ANN‐based recognition can be successfully used for inversion if the data correspond to the model class familiar to the ANN. No initial guess regarding the parameters of the 3‐D target or 1‐D layering is required. The ability of the ANN to teach itself using real geophysical (not only electromagnetic) data measured at a given location over a sufficiently long period means that there is the potential to use this approach for interpreting monitoring data.

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This paper deals with the further development of finite-difference methods for electromagnetic field modelling in two-and three-dimensional cases. The main feature of the approach suggested here is the application of generalized asymptotic boundary conditions valid with the accuracy o(l/pN), where p is the distance from the heterogeneities. The finite-difference approximation of problems under solution is made using the balance method, which results in 5-point difference schemes in the 2-D case and 7-point difference schemes in the 3-D case. To solve the linear system of difference equations the successive over-relaxation (SOR) method is used, the relaxation factor being chosen during the iteration procedure. In view of the vectorial character of the problem for the 3-D case, a successive blocked over-relaxation method (SBOR) is applied.The model's validity is based on the comparison of the fields accounted at the ground surface with those computed by the integral transformation of excessive currents, determined in the heterogeneity region using the finitedifference scheme.M. S. Zhdanov et al.

An indirect electromagnetic geothermometer is used for deep temperature estimations in the Soultz‐sous‐Forêts geothermal area (France) using magnetotelluric sounding data. Validation of temperature assessment carried out by comparison of the forecast temperature profile with temperature log from the deepest borehole has resulted in the relative extrapolation accuracy of less than 2%. It is found that the resistivity’s uncertainty caused by magnetotelluric inversion errors and by possible effects of external factors very weakly affects the resulting temperature, with the latter being influenced mainly by the ratio between the borehole length and the extrapolation depth. The temperature cross‐section constructed up to the depth 5000 m manifests local temperature maxima at large depths beneath the wells GPK2 and RT1/RT3. The analysis of the temperature profile in GPK2 location beneath 5000 m indicates that its behaviour continues to be of the conductive type (as in the depth range of 3700 m–5000 m) up to the depth 6000 m, while manifesting convective type below this depth. Finally, application of the indirect electromagnetic geothermometer for the deep temperature forecasting in the Rittershoffen site enabled us to constrain the location for future drilling.

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High-density array MT soundings of the crust in the seismically active northern Tien Shan were performed using Phoenix MTU-5 stations in the Bishkek Geodynamic Polygon, at the junction of the Chu basin and the Kyrgyz Range. The MT transfer functions were determined to an accuracy of 1–2% (amplitude) and about 0.5–0.8 deg (phase) in most of 145 soundings. Preliminary analysis of the collected data aimed at estimating the geoelectrical dimensionality. The Bahr decomposition analysis indicated the presence of local 3D structures in the crust of the area superposed on the regional 2D structure.

This paper provides a review of geological, geophysical, and geochemical studies for three geo thermal zones of Hengill Volcano, Iceland: Nesjavellir, Hellisheidi, and Hveragerdi. We discuss the relation ships between global tectonics and high temperature geothermal systems in Iceland. The bulk of this review is devoted to studies of the physical, geochemical, and mineralogic parameters for the three areas. A separate discussion concerns surface phenomena, as well as the origin of thermal water. This review covers studies of the main aquifer complexes: Miocene/Lower Pliocene plateau basalts, Upper Pliocene/Pleistocene lavas and volcanoclastics involving tillite horizons, an aquifer complex of Holocene lava sheets as thick as 1 km, and an aquifer complex of Upper Pleistocene/Holocene alluvial eolian deposits and formations of bottom moraines. We consider a conceptual model of geothermal reservoirs characteristic for the Hengill geothermal fields.