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In many near-surface geophysical studies it is now common practice to collect co-located disparate geophysical data sets to explore subsurface structures. Reconstruction of physical parameter distributions underlying the available geophysical data sets usually requires the use of tomographic reconstruction techniques. To improve the quality of the obtained models, the information content of all data sets should be considered during the model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the zonal cooperative inversion methodology based on fuzzy c-means cluster analysis and conventional single-input data set inversion algorithms for the cooperative inversion of data sets with partially co-located model areas. This is done by considering recent developments in fuzzy c-means cluster analysis. Additionally, we show how supplementary a priori information can be incorporated in an automated fashion into the zonal cooperative inversion approach to further constrain the inversion. The only requirement is that this a priori information can be expressed numerically; e.g., by physical parameters or indicator variables. We demonstrate the applicability of the modified zonal cooperative inversion approach using synthetic and field data examples. In these examples, we cooperatively invert S- and P-wave traveltime data sets with partially co-located model areas using water saturation information expressed by indicator variables as additional a priori information. The approach results in a zoned multi-parameter model, which is consistent with all available information given to the zonal cooperative inversion and outlines the major subsurface units. In our field example, we further compare the obtained zonal model to sparsely available borehole and direct-push logs. This comparison provides further confidence in our zonal cooperative inversion model because the borehole and direct-push logs indicate a similar zonation.
We strive to assess soil water content on a well-studied slow-moving hillslope in Austria. In doing so, we employ time lapse mapping of bulk electrical conductivity using a geophysical electromagnetic induction system operated at low induction numbers. This information is complemented by the acquisition of soil samples for gravimetric water content analysis during one survey campaign. Simple visual soil sample analysis reveals that the upper material in the survey area is a spatially highly variable mixture of predominately sandy, silty, clayey and organic materials. Due to this heterogeneity, classical approaches of mapping soil moisture on the basis of stationary mapping of electrical conductivity variations are not successful. Also the time-lapse approach does not allow ruling out some of the ambiguity inherent to the linkage of bulk electrical conductivity to soil water content. However, indication is found that time-lapse measurements may have supportive capabilities to identify regions of low precipitation infiltration due to high soil saturation. Furthermore, the relationship between the mean electrical conductivity averaged over a full vegetation period and an already available ecological moisture map produced by vegetation analysis is found to resemble closely the relationship observed between gravimetric soil water content and electrical conductivity during the time of sample collection except for highly organic soils. This leads us to the assumption that the relative soil moisture distribution is temporarily stable except for those areas characterized by highly organic soils.
Near-surface seismic traveltime tomography using a direct-push source and surface-planted geophones
(2009)
Information about seismic velocity distribution in heterogeneous near-surface sedimentary deposits is essential for a variety of environmental and engineering geophysical applications. We have evaluated the suitability of the minimally invasive direct-push technology for near-surface seismic traveltime tomography. Geophones placed at the surface and a seismic source installed temporarily in the subsurface by direct-push technology quickly acquire reversed multioffset vertical seismic profiles (VSPs). The first-arrival traveltimes of these data were used to reconstruct the 2D seismic velocity distribution tomographically. After testing this approach on synthetic data, we applied it to field data collected over alluvial deposits in a former river floodplain. The resulting velocity model contains information about high- and low-velocity anomalies and offers a significantly deeper penetration depth than conventional refraction tomography using surface-planted sources and receivers at the investigated site. A combination of refraction seismic and direct-push data increases resolution capabilities in the unsaturated zone and enables reliable reconstruction of velocity variations in near-surface unconsolidated sediments. The final velocity model structurally matches the results of cone-penetration tests and natural gamma-radiation data acquired along the profile. The suitability of multiple rapidly acquired reverse VSP surveys for 2D tomographic velocity imaging of near-surface unconsolidated sediments was explored.
Jugendgewalt und Fremdenfeindlichkeit : eine Herausforderung an Schule und politische Bildung
(1993)
Jugend in Brandenburg 93
(1994)
Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment
(2018)
Sensor-to-sensor variability is a source of error common to all geoscientific instruments that needs to be assessed before comparative and applied research can be performed with multiple sensors. Consistency among sensor systems is especially critical when subtle features of the surrounding terrain are to be identified. Cosmic-ray neutron sensors (CRNSs) are a recent technology used to monitor hectometre-scale environmental water storages, for which a rigorous comparison study of numerous co-located sensors has not yet been performed. In this work, nine stationary CRNS probes of type "CRS1000" were installed in relative proximity on a grass patch surrounded by trees, buildings, and sealed areas. While the dynamics of the neutron count rates were found to be similar, offsets of a few percent from the absolute average neutron count rates were found. Technical adjustments of the individual detection parameters brought all instruments into good agreement. Furthermore, we found a critical integration time of 6 h above which all sensors showed consistent dynamics in the data and their RMSE fell below 1% of gravimetric water content. The residual differences between the nine signals indicated local effects of the complex urban terrain on the scale of several metres. Mobile CRNS measurements and spatial simulations with the URANOS neutron transport code in the surrounding area (25 ha) have revealed substantial sub-footprint heterogeneity to which CRNS detectors are sensitive despite their large averaging volume. The sealed and constantly dry structures in the footprint furthermore damped the dynamics of the CRNS-derived soil moisture. We developed strategies to correct for the sealed-area effect based on theoretical insights about the spatial sensitivity of the sensor. This procedure not only led to reliable soil moisture estimation during dry-out periods, it further revealed a strong signal of intercepted water that emerged over the sealed surfaces during rain events. The presented arrangement offered a unique opportunity to demonstrate the CRNS performance in complex terrain, and the results indicated great potential for further applications in urban climate research.
We present cross-hole P- and S-wave seismic experiments that have been performed along a similar to 100 m long transect for the detailed characterization of a contaminated sedimentary site (Bitterfeld research test site, Germany). We invert the corresponding first break arrival times for the P- and S-wave velocity structure and compare two different strategies to interpret these models in terms of pertinent lithological and geotechnical parameter variations. The first (common) approach is based on directly translating the tomographic velocity models into the parameters of interest (e.g., elastic moduli). The second (zonal) approach first reduces the tomographic parameter information to a limited number of characteristic velocity combinations via k-means cluster analysis. Then, for each zone (cluster) further parameters including uncertainties can be estimated. In the presented case study, Our results indicate that the zonal approach provides an effective means for the integrated interpretation of different co-located data.
In die Freiheit entlassen
(1993)
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.