Search

We report on a study to explore the deep electrical conductivity structure of the Dead Sea Basin (DSB) using magnetotelluric (MT) data collected along a transect across the DSB where the left lateral strike-slip Dead Sea transform (DST) fault splits into two fault strands forming one of the largest pull-apart basins of the world. A very pronounced feature of our 2-D inversion model is a deep, subvertical conductive zone beneath the DSB. The conductor extends through the entire crust and is sandwiched between highly resistive structures associated with Precambrian rocks of the basin flanks. The high electrical conductivity could be attributed to fluids released by dehydration of the uppermost mantle beneath the DSB, possibly in combination with fluids released by mid- to low-grade metamorphism in the lower crust and generation of hydrous minerals in the middle crust through retrograde metamorphism. Similar high conductivity zones associated with fluids have been reported from other large fault systems. The presence of fluids and hydrous minerals in the middle and lower crust could explain the required low friction coefficient of the DST along the eastern boundary of the DSB and the high subsidence rate of basin sediments. 3-D inversion models confirm the existence of a subvertical high conductivity structure underneath the DSB but its expression is far less pronounced. Instead, the 3-D inversion model suggests a deepening of the conductive DSB sediments off-profile towards the south, reaching a maximum depth of approximately 12 km, which is consistent with other geophysical observations. At shallower levels, the 3-D inversion model reveals salt diapirism as an upwelling of highly resistive structures, localized underneath the Al-Lisan Peninsula. The 3-D model furthermore contains an E–W elongated conductive structure to the northeast of the DSB. More MT data with better spatial coverage are required, however, to fully constrain the robustness of the above-mentioned off-profile features.

SUMMARY Electrical conductivity is a key parameter for the exploration and characterization of geothermal reservoirs as hot mineralized formation water of active geothermal areas usually exhibits significantly higher conductivity than the surrounding host rock. Here we present results of a magnetotelluric (MT) exploration experiment carried out in the vicinity of the Gros Sch¨ onebeck geothermal test site in Northern Germany, where a doublet system of two 4.3-km deep boreholes was drilled to establish an in situ laboratory to investigate the potential for geothermal energy production. Classical 2-D smooth inversion of the MT data, recorded along two profiles, reveals a shallow conductive structure in good agreement with information from regional geology and seismic images. However, at the northernmost part of the profiles, the conductivity models reveal deep-reaching conductive structures, which appear uncorrelated with existing (geophysical or geological) data. Incorporating information from seismics as independent constraints for MT inversions allows us to examine the model space rigorously but target oriented. Employing so-called tear-zone inversions we can effectively derive an alternative class of models, which are consistent with the MT observations but also with the other data sets. We speculate that the zones of high conductivity imaged at reservoir depth are related areas of reduced thickness of the overlaying evapourite layer. The enhanced conductivity can be explained by a higher fracture density in anhydritic layers and/or generally lower resistivity of the pore fluid.

SUMMARY Magnetotelluric (MT) response function estimates can be severely disturbed by the effects of cultural noise. Methods to isolate and remove these disturbances are typically based on time-series editing, robust statistics, remote reference processing, or some combination of the above. Robust remote reference processing can improve the data quality at a local site, but only if synchronous recordings of at least one additional site are available and if electromagnetic noise between these sites is uncorrelated. If these prerequisites are not met, we suggest an alternative approach for noise removal, based on a combination of frequency domain editing with subsequent single site robust processing. The data pre-selection relies on a thorough visual inspection of a variety of statistical parameters such as spectral power densities, coherences, the distribution of response functions and their errors, etc. Extreme outliers and particularly noisy data segments are excluded from further data processing by setting threshold values for individual parameters. Examples from Namibia and Jordan illustrate that this scheme can improve data quality significantly. However, the examples also suggest that it is not possible to establish generally valid rules for selection as they depend strongly on the local noise conditions. High coherence, for example, can indicate a good signal-to-noise ratio or strongly correlated noise. However, we found that strong polarization of the magnetic field channels and the distribution of response function errors are two important parameters for noise detection.

Robust estimates of magnetotelluric and geomagnetic response functions are determined using the coherency and expected uniformity of the magnetic source field as quality criteria. The method is applied on data sets of three simultaneously recording sites. For the data acquisition we used a new generation of geophysical equipment (S.P.A.M. MkIII), which comprises novel concepts of parallel computing and networked, digital data transmission. The data-processing results show that the amount of noise on the horizontal components of the magnetic field varies considerably in time, between sites and over the frequency range. The removal of such contaminated data beforehand is essential for most data-processing schemes, as the magnetic channels are usually assumed to be free of noise. The standard remote reference method is aimed at reducing bias in response function estimates. However, this does not necessarily improve their precision as our results clearly show. With our method, on the other hand, we can filter out source field irregularities, thereby providing suitable working conditions for the robust algorithm, and eventually obtain considerably improved results. Contrary to previous concepts, we suggest rejecting as much data as feasible in order to concentrate on the remaining parts of high-quality observations.