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The Lower Rhine Graben (Central Europe) is a prime example of a seismically active low-strain rift zone characterized by pronounced anthropogenic and climatic overprint of structures, and long recurrence intervals of large earthquakes. These factors render the identification of active faults and surface ruptures difficult. We investigated two fault scarps in the Lower Rhine Graben, to decipher their structural character, offset and potential seismogenic origin. Both scarps were modified by anthropogenic activity. The Hemmerich site lies c. 20 km SW of Cologne, along the Erft Fault. The Untermaubach site lies SW of Duren, where the Schafberg Fault projects into the Rur River valley. At the Hemmerich site, geomorphic and geophysical data, as well as exploratory coring reveal evidence of repeated normal faulting. Geophysical analysis and palaeoseismological excavation at the Untermaubach site reveal a complex fault zone in Holocene gravels characterized by subtle gravel deformation. Differentiation of tectonic and fluvial features was only possible with trenching, because fault structures and grain sizes of the sediments were below the resolution of the geophysical data. Despite these issues, our investigation demonstrates that valuable insight into past earthquakes and seismogenic deformation in a low-strain environment can be revealed using a multidisciplinary approach.
Information regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering.
Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes.
Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture.
Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany.
Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points.
In addition, soil analysis and classification results are available for 132 regularly distributed positions up to depths of 1.2 m.
We compare and link three-dimensional resistivity models obtained via data inversion to soil composition and soil moisture as provided by our point data.
Both the soil samples and the resistivity models indicate a two-layer medium characterized by a sandy top layer with varying thickness and a loamy bottom soil.
For all six field campaigns, we observe similar resistivity patterns reflecting the temporally stable influence of soil texture.
While the overall patterns are stable, the range of resistivity values changes with soil moisture. Finally, to estimate spatial models of soil moisture, we link our soil moisture and resistivity data using empirical petrophysical models relying on a second order polynomial function.
We observe a mean prediction error for soil moisture of +/-0.034 m3 & BULL; m? 3 using all observation points while we notice that point-specific models further reduce the error.
Thus, we conclude that our experimental and data analysis strategies represent a reliable approach to establish site-specific models and to estimate three-dimensional moisture distribution including temporal variations.
Direct current systems employing a kinematic surveying strategy allow to analyze the electrical resistivity of the subsurface for large areas (i.e., several hectares). Typical applications are found in precision agriculture, archaeological prospecting and soil sciences. With the typical survey setting, the collected data sets are often characterized by a rather high level of noise and a rather coarse lateral sampling compared to data acquired with fixed electrodes. We therefore present an efficient one-dimensional inversion approach in which we put special attention on modeling the effects of noise. We apply this method to data recorded with a five-offset equatorial dipole-dipole system employing rolling electrodes. By performing several synthetic tests with realistic noise levels, we found that the considered five-configuration soundings allow for a reliable imaging of two-layer cases in the uppermost two meters of the subsurface, where the subsurface can be assumed to follow a horizontally layered geometry within 3 m around the system. By analyzing the corresponding sensitivity functions, we also show that the equatorial dipole-dipole array is relatively well suited for a 1D inversion approach compared to standard in-line electrode arrays. To illustrate this aspect, we show that our method can provide results similar to those obtained with a 2D Wenner imaging procedure for data recorded across a well-constrained 2D target. We finally apply our method to a large five-offset data set acquired in an agricultural study. The final pseudo-3D model of electrical resistivity is in accordance with borehole data available for the surveyed area. Our results demonstrate the applicability and the versatility of the presented inversion approach for large-scale data sets as they are typically collected with such rolling electrode systems. (C) 2017 Elsevier B.V. All rights reserved.
We present an algorithm that performs sequentially one-dimensional inversion of subsurface magnetic permeability and electrical conductivity by using multi-configuration electromagnetic induction sensor data. The presented method is based on the conversion of the in-phase and out-of-phase data into effective magnetic permeability and electrical conductivity of the equivalent homogeneous half-space. In the case of small-offset systems, such as portable electromagnetic induction sensors, for which in-phase and out-of-phase data are moderately coupled, the effective half-space magnetic permeability and electrical conductivity can be inverted sequentially within an iterative scheme. We test and evaluate the proposed inversion strategy using synthetic and field examples. First, we apply it to synthetic data for some highly magnetic environments. Then, the method is tested on real field data acquired in a basaltic environment to image a formation of archaeological interest. These examples demonstrate that a joint interpretation of in-phase and out-of-phase data leads to a better characterisation of the subsurface in magnetic environments such as volcanic areas.
Inland salt meadows are particularly valuable ecosystems, because they support a variety of salt-adapted species (halophytes). They can be found throughout Europe; including the peatlands of the glacial lowlands in northeast Germany. These German ecosystems have been seriously damaged through drainage. To assess and ultimately limit the damages, temporal monitoring of soil salinity is essential, which can be conducted by geoelectrical techniques that measure the soil electrical conductivity. However, there is limited knowledge on how to interpret electrical conductivity surveys of peaty salt meadows. In this study, temporal and spatial monitoring of dissolved salts was conducted in saline peatland soils using different geoelectrical techniques at different scales (1D: conductivity probe, 2D: conductivity cross-sections). Cores and soil samples were taken to validate the geoelectrical surveys. Although the influence of peat on bulk conductivity is large, the seasonal dynamics of dissolved salts within the soil profile could be monitored by repeated geoelectrical measurements. A close correlation is observed between conductivity (similar to salinity) at different depths and temperature, precipitation and corresponding groundwater level. The conductivity distribution between top- and subsoil during the growing season reflected the leaching of dissolved salts by precipitation and the capillary rise of dissolved salts by increasing temperature (similar to evaporation). Groundwater levels below 0.38 cm resulted in very low conductivities in the topsoil, which is presumably due to limited soil moisture and thus precipitation of salts. Therefore, to prevent the disappearance of dissolved salts from the rooting zone, which are essential for the halophytes, groundwater levels should be adjusted to maintain depths of between 20 and 35 cm. Lower groundwater levels will lead to the loss of dissolved salts from the rooting zone and higher levels to increasing dilution with fresh rainwater. The easy-to-handle conductivity probe is an appropriate tool for salinity monitoring. Using this probe with regressions adjusted for sandy and organic substrates (peat and organic gyttja) additional influences on bulk conductivity (e.g. cation exchange capacity, water content) can be compensated for and the correlation between salinity and electrical conductivity is high.
In near- surface geophysics, ground-based mapping surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we have developed a multiscale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the a trous algorithm, which allows to efficiently compute a multiscale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, whereas our archaeogeophysical case study from northeast Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, whereas our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multiscale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.