@misc{HetenyiMolinariClintonetal.2018, author = {Hetenyi, Gyorgy and Molinari, Irene and Clinton, John and Bokelmann, Gotz and Bondar, Istvan and Crawford, Wayne C. and Dessa, Jean-Xavier and Doubre, Cecile and Friederich, Wolfgang and Fuchs, Florian and Giardini, Domenico and Graczer, Zoltan and Handy, Mark R. and Herak, Marijan and Jia, Yan and Kissling, Edi and Kopp, Heidrun and Korn, Michael and Margheriti, Lucia and Meier, Thomas and Mucciarelli, Marco and Paul, Anne and Pesaresi, Damiano and Piromallo, Claudia and Plenefisch, Thomas and Plomerova, Jaroslava and Ritter, Joachim and Rumpker, Georg and Sipka, Vesna and Spallarossa, Daniele and Thomas, Christine and Tilmann, Frederik and Wassermann, Joachim and Weber, Michael and Weber, Zoltan and Wesztergom, Viktor and Zivcic, Mladen and Abreu, Rafael and Allegretti, Ivo and Apoloner, Maria-Theresia and Aubert, Coralie and Besancon, Simon and de Berc, Maxime Bes and Brunel, Didier and Capello, Marco and Carman, Martina and Cavaliere, Adriano and Cheze, Jerome and Chiarabba, Claudio and Cougoulat, Glenn and Cristiano, Luigia and Czifra, Tibor and Danesi, Stefania and Daniel, Romuald and Dannowski, Anke and Dasovic, Iva and Deschamps, Anne and Egdorf, Sven and Fiket, Tomislav and Fischer, Kasper and Funke, Sigward and Govoni, Aladino and Groschl, Gidera and Heimers, Stefan and Heit, Ben and Herak, Davorka and Huber, Johann and Jaric, Dejan and Jedlicka, Petr and Jund, Helene and Klingen, Stefan and Klotz, Bernhard and Kolinsky, Petr and Kotek, Josef and Kuhne, Lothar and Kuk, Kreso and Lange, Dietrich and Loos, Jurgen and Lovati, Sara and Malengros, Deny and Maron, Christophe and Martin, Xavier and Massa, Marco and Mazzarini, Francesco and Metral, Laurent and Moretti, Milena and Munzarova, Helena and Nardi, Anna and Pahor, Jurij and Pequegnat, Catherine and Petersen, Florian and Piccinini, Davide and Pondrelli, Silvia and Prevolnik, Snjezan and Racine, Roman and Regnier, Marc and Reiss, Miriam and Salimbeni, Simone and Santulin, Marco and Scherer, Werner and Schippkus, Sven and Schulte-Kortnack, Detlef and Solarino, Stefano and Spieker, Kathrin and Stipcevic, Josip and Strollo, Angelo and Sule, Balint and Szanyi, Gyongyver and Szucs, Eszter and Thorwart, Martin and Ueding, Stefan and Vallocchia, Massimiliano and Vecsey, Ludek and Voigt, Rene and Weidle, Christian and Weyland, Gauthier and Wiemer, Stefan and Wolf, Felix and Wolyniec, David and Zieke, Thomas}, title = {The AlpArray seismic network}, series = {Surveys in Geophysics}, volume = {39}, journal = {Surveys in Geophysics}, number = {5}, publisher = {Springer}, address = {Dordrecht}, organization = {ETHZ SED Elect Lab AlpArray Seismic Network Team AlpArray OBS Cruise Crew AlpArray Working Grp}, issn = {0169-3298}, doi = {10.1007/s10712-018-9472-4}, pages = {1009 -- 1033}, year = {2018}, abstract = {The AlpArray programme is a multinational, European consortium to advance our understanding of orogenesis and its relationship to mantle dynamics, plate reorganizations, surface processes and seismic hazard in the Alps-Apennines-Carpathians-Dinarides orogenic system. The AlpArray Seismic Network has been deployed with contributions from 36 institutions from 11 countries to map physical properties of the lithosphere and asthenosphere in 3D and thus to obtain new, high-resolution geophysical images of structures from the surface down to the base of the mantle transition zone. With over 600 broadband stations operated for 2 years, this seismic experiment is one of the largest simultaneously operated seismological networks in the academic domain, employing hexagonal coverage with station spacing at less than 52 km. This dense and regularly spaced experiment is made possible by the coordinated coeval deployment of temporary stations from numerous national pools, including ocean-bottom seismometers, which were funded by different national agencies. They combine with permanent networks, which also required the cooperation of many different operators. Together these stations ultimately fill coverage gaps. Following a short overview of previous large-scale seismological experiments in the Alpine region, we here present the goals, construction, deployment, characteristics and data management of the AlpArray Seismic Network, which will provide data that is expected to be unprecedented in quality to image the complex Alpine mountains at depth.}, language = {en} } @article{FuchsFoersterBrauneetal.2018, author = {Fuchs, Sven and F{\"o}rster, Hans-J{\"u}rgen and Braune, K. and F{\"o}rster, A.}, title = {Calculation of Thermal Conductivity of Low-Porous, Isotropic Plutonic Rocks of the Crust at Ambient Conditions From Modal Mineralogy and Porosity}, series = {Journal of geophysical research : Solid earth}, volume = {123}, journal = {Journal of geophysical research : Solid earth}, number = {10}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2169-9313}, doi = {10.1029/2018JB016287}, pages = {8602 -- 8614}, year = {2018}, abstract = {Thermal conductivity (lambda) is an essential physical property of minerals and rocks and fundamental in constraining the thermal field of the lithosphere. In case that adequate samples to measure lambda are not available, it could be indirectly inferred from calculation. One of the most widely applied indirect methods for rocks involve modal mineralogy and porosity as parameters that are incorporated into mathematical mean or mixing models. Robust inferences from these approaches for crystalline rocks were impeded by a small number of studied samples or restriction to certain rock types. We employ this method and examine its applicability to low-porosity plutonic rocks by calculating bulk thermal conductivity lambda(b) for 45 samples covering the entire range from gabbro/diorite to granite. We show that the use of the harmonic-mean model for both rock matrix and porosity provided a good match between lambda(b.meas) and lambda(b.calc) of <10\% deviation (2 sigma), with relative and absolute errors amounting to 1.49.7\% and 4.44.9\%, respectively. The results of our study constitute a big step forward to a robust conclusion on the overall applicability of the harmonic-mean model for inferring lambda(b) of isotropic, low-porosity, mafic to silicic plutonic and metamorphic rocks with an acceptable magnitude of error. Drill cuttings and enclaves form particularly interesting objects for application of this method, as they are poorly suited for direct measurement. Well-derived lambda values for those rocks would permit to calculate heat flow and to model more profoundly the thermal state of the deeper lithosphere.}, language = {en} } @article{WittigMirandaHoelzeretal.2022, author = {Wittig, Alice and Miranda, Fabio Malcher and H{\"o}lzer, Martin and Altenburg, Tom and Bartoszewicz, Jakub Maciej and Beyvers, Sebastian and Dieckmann, Marius Alfred and Genske, Ulrich and Giese, Sven Hans-Joachim and Nowicka, Melania and Richard, Hugues and Schiebenhoefer, Henning and Schmachtenberg, Anna-Juliane and Sieben, Paul and Tang, Ming and Tembrockhaus, Julius and Renard, Bernhard Y. and Fuchs, Stephan}, title = {CovRadar}, series = {Bioinformatics}, volume = {38}, journal = {Bioinformatics}, number = {17}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btac411}, pages = {4223 -- 4225}, year = {2022}, abstract = {The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast.}, language = {en} } @phdthesis{Fuchs2013, author = {Fuchs, Sven}, title = {Well-log based determination of rock thermal conductivity in the North German Basin}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-67801}, school = {Universit{\"a}t Potsdam}, year = {2013}, abstract = {In sedimentary basins, rock thermal conductivity can vary both laterally and vertically, thus altering the basin's thermal structure locally and regionally. Knowledge of the thermal conductivity of geological formations and its spatial variations is essential, not only for quantifying basin evolution and hydrocarbon maturation processes, but also for understanding geothermal conditions in a geological setting. In conjunction with the temperature gradient, thermal conductivity represents the basic input parameter for the determination of the heat-flow density; which, in turn, is applied as a major input parameter in thermal modeling at different scales. Drill-core samples, which are necessary to determine thermal properties by laboratory measurements, are rarely available and often limited to previously explored reservoir formations. Thus, thermal conductivities of Mesozoic rocks in the North German Basin (NGB) are largely unknown. In contrast, geophysical borehole measurements are often available for the entire drilled sequence. Therefore, prediction equations to determine thermal conductivity based on well-log data are desirable. In this study rock thermal conductivity was investigated on different scales by (1) providing thermal-conductivity measurements on Mesozoic rocks, (2) evaluating and improving commonly applied mixing models which were used to estimate matrix and pore-filled rock thermal conductivities, and (3) developing new well-log based equations to predict thermal conductivity in boreholes without core control. Laboratory measurements are performed on sedimentary rock of major geothermal reservoirs in the Northeast German Basin (NEGB) (Aalenian, Rhaethian-Liassic, Stuttgart Fm., and Middle Buntsandstein). Samples are obtained from eight deep geothermal wells that approach depths of up to 2,500 m. Bulk thermal conductivities of Mesozoic sandstones range between 2.1 and 3.9 W/(m∙K), while matrix thermal conductivity ranges between 3.4 and 7.4 W/(m∙K). Local heat flow for the Stralsund location averages 76 mW/m², which is in good agreement to values reported previously for the NEGB. For the first time, in-situ bulk thermal conductivity is indirectly calculated for entire borehole profiles in the NEGB using the determined surface heat flow and measured temperature data. Average bulk thermal conductivity, derived for geological formations within the Mesozoic section, ranges between 1.5 and 3.1 W/(m∙K). The measurement of both dry- and water-saturated thermal conductivities allow further evaluation of different two-component mixing models which are often applied in geothermal calculations (e.g., arithmetic mean, geometric mean, harmonic mean, Hashin-Shtrikman mean, and effective-medium theory mean). It is found that the geometric-mean model shows the best correlation between calculated and measured bulk thermal conductivity. However, by applying new model-dependent correction, equations the quality of fit could be significantly improved and the error diffusion of each model reduced. The 'corrected' geometric mean provides the most satisfying results and constitutes a universally applicable model for sedimentary rocks. Furthermore, lithotype-specific and model-independent conversion equations are developed permitting a calculation of water-saturated thermal conductivity from dry-measured thermal conductivity and porosity within an error range of 5 to 10\%. The limited availability of core samples and the expensive core-based laboratory measurements make it worthwhile to use petrophysical well logs to determine thermal conductivity for sedimentary rocks. The approach followed in this study is based on the detailed analyses of the relationships between thermal conductivity of rock-forming minerals, which are most abundant in sedimentary rocks, and the properties measured by standard logging tools. By using multivariate statistics separately for clastic, carbonate and evaporite rocks, the findings from these analyses allow the development of prediction equations from large artificial data sets that predict matrix thermal conductivity within an error of 4 to 11\%. These equations are validated successfully on a comprehensive subsurface data set from the NGB. In comparison to the application of earlier published approaches formation-dependent developed for certain areas, the new developed equations show a significant error reduction of up to 50\%. These results are used to infer rock thermal conductivity for entire borehole profiles. By inversion of corrected in-situ thermal-conductivity profiles, temperature profiles are calculated and compared to measured high-precision temperature logs. The resulting uncertainty in temperature prediction averages < 5\%, which reveals the excellent temperature prediction capabilities using the presented approach. In conclusion, data and methods are provided to achieve a much more detailed parameterization of thermal models.}, language = {en} }