@article{HamannTronicke2014, author = {Hamann, G{\"o}ran and Tronicke, Jens}, title = {Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models}, series = {Near surface geophysics}, volume = {12}, journal = {Near surface geophysics}, number = {4}, publisher = {European Association of Geoscientists \& Engineers}, address = {Houten}, issn = {1569-4445}, doi = {10.3997/1873-0604.2014005}, pages = {505 -- 514}, year = {2014}, abstract = {Velocity models are essential to process two-and three-dimensional ground-penetrating radar (GPR) data. Furthermore, velocity information aids the interpretation of such data sets because velocity variations reflect important material properties such as water content. In many GPR applications, common midpoint (CMP) surveys are routinely collected to determine one-dimensional velocity models at selected locations. To analyse CMP data gathers, spectral velocity analyses relying on the normal-moveout (NMO) model are commonly employed. Using Dix's formula, the derived NMO velocities can be further converted to interval velocities which are needed for processing and interpretation. Because of the inherent assumptions and limitations of such approaches, we investigate and propose an alternative procedure based on the global inversion of reflection travel-times. We use a finite-difference solver of the Eikonal equation to accurately solve the forward problem in combination with particle swarm optimization (PSO) to find one-dimensional GPR velocity models explaining our data. Because PSO is a robust and efficient global optimization tool, our inversion approach includes generating an ensemble of representative solutions that allows us to analyse uncertainties in the model space. Using synthetic data examples, we test and evaluate our inversion approach to analyse CMP data collected across typical near-surface environments. Application to a field data set recorded at a well-constrained test site including a comparison to independent borehole and direct-push data, further illustrates the potential of the proposed approach, which includes a straightforward and understandable appraisal of non-uniqueness and uncertainty issues, respectively. We conclude that our methodology is a feasible and powerful tool to analyse GPR CMP data and allows practitioners and researchers to evaluate the reliability of CMP derived velocity models.}, language = {en} } @article{ZeheEhretPfisteretal.2014, author = {Zehe, E. and Ehret, U. and Pfister, L. and Blume, Theresa and Schroeder, Boris and Westhoff, M. and Jackisch, C. and Schymanski, Stanislauv J. and Weiler, M. and Schulz, K. and Allroggen, Niklas and Tronicke, Jens and van Schaik, Loes and Dietrich, Peter and Scherer, U. and Eccard, Jana and Wulfmeyer, Volker and Kleidon, Axel}, title = {HESS Opinions: From response units to functional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments}, series = {Hydrology and earth system sciences : HESS}, volume = {18}, journal = {Hydrology and earth system sciences : HESS}, number = {11}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-18-4635-2014}, pages = {4635 -- 4655}, year = {2014}, abstract = {According to Dooge (1986) intermediate-scale catchments are systems of organized complexity, being too organized and yet too small to be characterized on a statistical/conceptual basis, but too large and too heterogeneous to be characterized in a deterministic manner. A key requirement for building structurally adequate models precisely for this intermediate scale is a better understanding of how different forms of spatial organization affect storage and release of water and energy. Here, we propose that a combination of the concept of hydrological response units (HRUs) and thermodynamics offers several helpful and partly novel perspectives for gaining this improved understanding. Our key idea is to define functional similarity based on similarity of the terrestrial controls of gradients and resistance terms controlling the land surface energy balance, rainfall runoff transformation, and groundwater storage and release. This might imply that functional similarity with respect to these specific forms of water release emerges at different scales, namely the small field scale, the hillslope, and the catchment scale. We thus propose three different types of "functional units" - specialized HRUs, so to speak - which behave similarly with respect to one specific form of water release and with a characteristic extent equal to one of those three scale levels. We furthermore discuss an experimental strategy based on exemplary learning and replicate experiments to identify and delineate these functional units, and as a promising strategy for characterizing the interplay and organization of water and energy fluxes across scales. We believe the thermodynamic perspective to be well suited to unmask equifinality as inherent in the equations governing water, momentum, and energy fluxes: this is because several combinations of gradients and resistance terms yield the same mass or energy flux and the terrestrial controls of gradients and resistance terms are largely independent. We propose that structurally adequate models at this scale should consequently disentangle driving gradients and resistance terms, because this optionally allow sequifinality to be partly reduced by including available observations, e. g., on driving gradients. Most importantly, the thermodynamic perspective yields an energy-centered perspective on rainfall-runoff transformation and evapotranspiration, including fundamental limits for energy fluxes associated with these processes. This might additionally reduce equifinality and opens up opportunities for testing thermodynamic optimality principles within independent predictions of rainfall-runoff or land surface energy exchange. This is pivotal to finding out whether or not spatial organization in catchments is in accordance with a fundamental organizing principle.}, language = {en} } @article{PaascheTronicke2014, author = {Paasche, Hendrik and Tronicke, Jens}, title = {Nonlinear joint inversion of tomographic data using swarm intelligence}, series = {Geophysics}, volume = {79}, journal = {Geophysics}, number = {4}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2013-0423.1}, pages = {R133 -- R149}, year = {2014}, abstract = {Geophysical techniques offer the potential to tomographically image physical parameter variations in the ground in two or three dimensions. Due to the limited number and accuracy of the recorded data, geophysical model generation by inversion suffers ambiguity. Linking the model generation process of disparate data by jointly inverting two or more data sets allows for improved model reconstruction. Fully nonlinear inversion using optimization techniques searching the solution space of the inverse problem globally enables quantitative assessment of the ambiguity inherent to the model reconstruction. We used two different multiobjective particle swarm optimization approaches to jointly invert synthetic crosshole tomographic data sets comprising radar and P-wave traveltimes, respectively. Beginning with a nonlinear joint inversion founded on the principle of Pareto optimality and game theoretic concepts, we obtained a set of Pareto-optimal solutions comprising commonly structured radar and P-wave velocity models for low computational costs. However, the efficiency of the approach goes along with some risk of achieving a final model ensemble not adequately illustrating the ambiguity inherent to the model reconstruction process. Taking advantage of the results of the first approach, we inverted the database using a different nonlinear joint-inversion approach reducing the multiobjective optimization problem to a single-objective one. Computational costs were significantly higher, but the final models were obtained mutually independently allowing for objective appraisal of model parameter determination. Despite the high computational effort, the approach was found to be an efficient nonlinear joint-inversion formulation compared to what could be extracted from individual nonlinear inversions of both data sets.}, language = {en} } @article{RumpfTronicke2014, author = {Rumpf, Michael and Tronicke, Jens}, title = {Predicting 2D geotechnical parameter fields in near-surface sedimentary environments}, series = {Journal of applied geophysics}, volume = {101}, journal = {Journal of applied geophysics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2013.12.002}, pages = {95 -- 107}, year = {2014}, abstract = {For a detailed characterization of near-surface environments, geophysical techniques are increasingly used to support more conventional point-based techniques such as borehole and direct-push logging. Because the underlying parameter relations are often complex, site-specific, or even poorly understood, a remaining challenging task is to link the geophysical parameter models to the actual geotechnical target parameters measured only at selected points. We propose a workflow based on nonparametric regression to establish functional relationships between jointly inverted geophysical parameters and selected geotechnical parameters as measured, for example, by different borehole and direct-push tools. To illustrate our workflow, we present field data collected to characterize a near-surface sedimentary environment Our field data base includes crosshole ground penetrating radar (GPR), seismic P-, and S-wave data sets collected between 25 m deep boreholes penetrating sand- and gravel dominated sediments. Furthermore, different typical borehole and direct-push logs are available. We perform a global joint inversion of traveltimes extracted from the crosshole geophysical data using a recently proposed approach based on particle swarm optimization. Our inversion strategy allows for generating consistent models of GPR, P-wave, and S-wave velocities including an appraisal of uncertainties. We analyze the observed complex relationships between geophysical velocities and target parameter logs using the alternating conditional expectation (ACE) algorithm. This nonparametric statistical tool allows us to perform multivariate regression analysis without assuming a specific functional relation between the variables. We are able to explain selected target parameters such as characteristic grain size values or natural gamma activity by our inverted geophysical data and to extrapolate these parameters to the inter-borehole plane covered by our crosshole experiments. We conclude that the ACE algorithm is a powerful tool to analyze a multivariate petrophysical data base and to develop an understanding of how a multi-parameter geophysical model can be linked and translated to selected geotechnical parameters.}, language = {en} } @article{TronickeHamann2014, author = {Tronicke, Jens and Hamann, G{\"o}ran}, title = {Vertical radar profiling: Combined analysis of traveltimes, amplitudes, and reflections}, series = {Geophysics}, volume = {79}, journal = {Geophysics}, number = {4}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2013-0428.1}, pages = {H23 -- H35}, year = {2014}, abstract = {Vertical radar profiling (VRP) is a single-borehole geophysical technique, in which the receiver antenna is located within a borehole and the transmitter antenna is placed at one or various offsets from the borehole. Today, VRP surveying is primarily used to derive 1D velocity models by inverting the arrival times of direct waves. Using field data collected at a well-constrained test site in Germany, we evaluated a VRP workflow relying on the analysis of direct-arrival traveltimes and amplitudes as well as on imaging reflection events. To invert our VRP traveltime data, we used a global inversion strategy resulting in an ensemble of acceptable velocity models, and thus, it allowed us to appraise uncertainty issues in the estimated velocities as well as in porosity models derived via petrophysical translations. In addition to traveltime inversion, the analysis of direct-wave amplitudes and reflection events provided further valuable information regarding subsurface properties and architecture. The used VRP amplitude preprocessing and inversion procedures were adapted from raybased crosshole ground-penetrating radar (GPR) attenuation tomography and resulted in an attenuation model, which can be used to estimate variations in electrical resistivity. Our VRP reflection imaging approach relied on corridor stacking, which is a well-established processing sequence in vertical seismic profiling. The resulting reflection image outlines bounding layers and can be directly compared to surface-based GPR reflection profiling. Our results of the combined analysis of VRP, traveltimes, amplitudes, and reflections were consistent with independent core and borehole logs as well as GPR reflection profiles, which enabled us to derive a detailed hydro-stratigraphic model as needed, for example, to understand and model groundwater flow and transport.}, language = {en} }