@article{ZakrevskyyCywinskiCywinskaetal.2014, author = {Zakrevskyy, Yuriy and Cywinski, Piotr and Cywinska, Magdalena and Paasche, Jens and Lomadze, Nino and Reich, Oliver and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Santer, Svetlana}, title = {Interaction of photosensitive surfactant with DNA and poly acrylic acid}, series = {The journal of chemical physics : bridges a gap between journals of physics and journals of chemistr}, volume = {140}, journal = {The journal of chemical physics : bridges a gap between journals of physics and journals of chemistr}, number = {4}, publisher = {American Institute of Physics}, address = {Melville}, issn = {0021-9606}, doi = {10.1063/1.4862679}, pages = {8}, year = {2014}, language = {en} } @article{PaascheTronickeDietrich2012, author = {Paasche, Hendrik and Tronicke, Jens and Dietrich, Peter}, title = {Zonal cooperative inversion of partially co-located data sets constrained by structural a priori information}, series = {Near surface geophysics}, volume = {10}, journal = {Near surface geophysics}, number = {2}, publisher = {European Association of Geoscientists \& Engineers}, address = {Houten}, issn = {1569-4445}, doi = {10.3997/1873-0604.2011033}, pages = {103 -- 116}, year = {2012}, abstract = {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.}, language = {en} } @article{TronickePaascheBoeniger2012, author = {Tronicke, Jens and Paasche, Hendrik and B{\"o}niger, Urs}, title = {Crosshole traveltime tomography using particle swarm optimization a near-surface field example}, series = {Geophysics}, volume = {77}, journal = {Geophysics}, number = {1}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2010-0411.1}, pages = {R19 -- R32}, year = {2012}, abstract = {Particle swarm optimization (PSO) is a relatively new global optimization approach inspired by the social behavior of bird flocking and fish schooling. Although this approach has proven to provide excellent convergence rates in different optimization problems, it has seldom been applied to inverse geophysical problems. Until today, published geophysical applications mainly focus on finding an optimum solution for simple, 1D inverse problems. We have applied PSO-based optimization strategies to reconstruct 2D P-wave velocity fields from crosshole traveltime data sets. Our inversion strategy also includes generating and analyzing a representative ensemble of acceptable models, which allows us to appraise uncertainty and nonuniqueness issues. The potential of our strategy was tested on field data collected at a well-constrained test site in Horstwalde, Germany. At this field site, the shallow subsurface mainly consists of sand- and gravel-dominated glaciofluvial sediments, which, as known from several boreholes and other geophysical experiments, exhibit some well-defined layering at the scale of our crosshole seismic data. Thus, we have implemented a flexible, layer-based model parameterization, which, compared with standard cell-based parameterizations, allows for significantly reducing the number of unknown model parameters and for efficiently implementing a priori model constraints. Comparing the 2D velocity fields resulting from our PSO strategy to independent borehole and direct-push data illustrated the benefits of choosing an efficient global optimization approach. These include a straightforward and understandable appraisal of nonuniqueness issues as well as the possibility of an improved and also more objective interpretation.}, 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{PaascheTronickeHolligeretal.2006, author = {Paasche, Hendrik and Tronicke, Jens and Holliger, Klaus and Green, Alan G. and Maurer, Hansruedi}, title = {Integration of diverse physical-property models : subsurface zonation and petrophysical parameter estimation based on fuzzy c-means cluster analyses}, doi = {10.1190/1.2192927}, year = {2006}, abstract = {Inversions of an individual geophysical data set can be highly nonunique, and it is generally difficult to determine petrophysical parameters from geophysical data. We show that both issues can be addressed by adopting a statistical multiparameter approach that requires the acquisition, processing, and separate inversion of two or more types of geophysical data. To combine information contained in the physical-property models that result from inverting the individual data sets and to estimate the spatial distribution of petrophysical parameters in regions where they are known at only a few locations. we demonstrate the potential of the fuzzy c-means (FCM) clustering technique. After testing this new approach on synthetic data, we apply it to limited crosshole georadar, crosshole seismic, gamma-log, and slug-test data acquired within a shallow alluvial aquifer. The derived multiparameter model effectively outlines the major sedimentary units observed in numerous boreholes and provides plausible estimates for the spatial distributions of gamma-ray emitters and hydraulic conductivity}, language = {en} } @article{TronickePaasche2017, author = {Tronicke, Jens and Paasche, Hendrik}, title = {Integrated interpretation of 2D ground-penetrating radar, P-, and S-wave velocity models in terms of petrophysical properties}, series = {Interpretation : a journal of subsurface characterization}, volume = {5}, journal = {Interpretation : a journal of subsurface characterization}, number = {1}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {2324-8858}, doi = {10.1190/INT-2016-0081.1}, pages = {T121 -- T130}, year = {2017}, abstract = {Near-surface geophysical techniques are extensively used in a variety of engineering, environmental, geologic, and hydrologic applications. While many of these applications ask for detailed, quantitative models of selected material properties, geophysical data are increasingly used to estimate such target properties. Typically, this estimation procedure relies on a two-step workflow including (1) the inversion of geophysical data and (2) the petrophysical translation of the inverted parameter models into the target properties. Standard deterministic implementations of such a quantitative interpretation result in a single best-estimate model, often without considering and propagating the uncertainties related to the two steps. We address this problem by using a rather novel, particle-swarm-based global joint strategy for data inversion and by implementing Monte Carlo procedures for petrophysical property estimation. We apply our proposed workflow to crosshole ground-penetrating radar, P-, and S-wave data sets collected at a well-constrained test site for a detailed geotechnical characterization of unconsolidated sands. For joint traveltime inversion, the chosen global approach results in ensembles of acceptable velocity models, which are analyzed to appraise inversion-related uncertainties. Subsequently, the entire ensembles of inverted velocity models are considered to estimate selected petrophysical properties including porosity, bulk density, and elastic moduli via well-established petrophysical relations implemented in a Monte Carlo framework. Our results illustrate the potential benefit of such an advanced interpretation strategy; i.e., the proposed workflow allows to study how uncertainties propagate into the finally estimated property models, while concurrently we are able to study the impact of uncertainties in the used petrophysical relations (e.g., the influence of uncertain, user-specified parameters). We conclude that such statistical approaches for the quantitative interpretation of geophysical data can be easily extended and adapted to other applications and geophysical methods and might be an important step toward increasing the popularity and acceptance of geophysical tools in engineering practice.}, language = {en} }