ALBERT

Description: 〈span〉〈div〉SUMMARY〈/div〉The localization of passive seismic sources in form of microseismic tremors as well as large-scale earthquakes is a key issue in seismology. While most previous studies are assuming fairly good knowledge of the underlying velocity model, we propose an automatic spatial localization and joint velocity model building scheme that is independent of detailed 〈span〉a priori〈/span〉 information. The first step is a coherence analysis, estimating so-called wavefront attributes to locally describe the wavefield in terms of slopes and curvatures. In a similar fashion, we also obtain an initial guess of the source excitation times of the recorded events. The wavefront attributes constitute the input for wavefront tomography which represents the next step of the workflow and allows for a refinement of the previously evaluated source excitation times while simultaneously approximating the velocity distribution. In a last step, we use the final estimate of the velocity distribution and compute the respective image function by reverse time modelling to gain the source locations. This paper introduces the theoretical concept of our proposed approach for the general 3-D case. We analyse the feasibility of our strategy and the influences of different acquisition settings by means of a synthetic 2-D data example. In a final 3-D field data example we use the workflow to localize a deep earthquake without relying on a given velocity model. The approach can deal with high levels of noise and low signal amplitudes, respectively, as well as sparse geophone sampling. The workflow generally delivers good approximations of the long-wavelength velocity variations along with accurate source locations.〈/span〉

Description: Author(s): Hao Wang, B. Bauer, M. Troyer, and V. W. Scarola We theoretically examine the use of a statistical distance measure, the indistinguishability, as a generic tool for the identification of topological order. We apply this measure to the toric code and two fractional quantum Hall models. We find that topologically ordered states can be identified wit... [Phys. Rev. B 83, 115119] Published Mon Mar 14, 2011

Description: Author(s): B. Bauer, P. Corboz, R. Orús, and M. Troyer Due to the unfavorable scaling of tensor-network methods with the refinement parameter M, new approaches are necessary to improve the efficiency of numerical simulations based on such states, in particular for gapless, strongly entangled systems. In one-dimensional density matrix renormalization gro... [Phys. Rev. B 83, 125106] Published Fri Mar 18, 2011

Description: The southern Appalachians represent a landscape characterized by locally high topographic relief, steep slopes, and frequent mass movement in the absence of significant tectonic forcing for at least the last 200 Ma. The fundamental processes responsible for landscape evolution in a post-orogenic landscape remain enigmatic. The non-glaciated Cullasaja River basin of southwestern North Carolina, with uniform lithology, frequent debris flows, and the availability of high-resolution airborne lidar DEMs, is an ideal natural setting to study landscape evolution in a post-orogenic landscape through the lens of hillslope-channel coupling. We limit our investigation to channels with upslope contributing areas 〉2.7 km2, a conservative estimate of the transition from fluvial to debris-flow dominated channel processes. We utilize values of normalized hypsometry, hypsometric integral, and mean slope vs. elevation for 14 tributary basins and the Cullasaja basin as a whole to characterize landscape evolution following upstream knickpoint migration. Our results highlight the existence of a transient spatial relationship between knickpoints present along the fluvial network of the Cullasaja basin and adjacent hillslopes. Metrics of topography (relief, slope gradient) and hillslope activity (landslide frequency) exhibit significant downstream increases below the current position of major knickpoints. We capture the transient effect of knickpoint-driven channel incision on basin hillslopes by measuring the relief, mean slope steepness, and mass movement frequency of tributary basins and comparing these results to the distance from major knickpoints along the Cullasaja River. We present a conceptual model of area-elevation and slope distributions that may be representative of post-orogenic landscape evolution in analogous geologic settings. Importantly, our model explains how knickpoint migration and channel-hillslope coupling is an important factor in tectonically-inactive (i.e. post-orogenic) orogens for the maintenance of significant relief, steep slopes, and weathering-limited hillslopes. Copyright © 2011 John Wiley & Sons, Ltd.

Description: 〈span〉〈div〉Summary〈/div〉The localisation of passive seismic sources in form of microseismic tremors as well as large-scale earthquakes is a key issue in seismology. While most previous studies are assuming fairly good knowledge of the underlying velocity model, we propose an automatic spatial localisation and joint velocity model building scheme that is independent of detailed a priori information. The first step is a coherence analysis, estimating so-called wavefront attributes to locally describe the wavefield in terms of slopes and curvatures. In a similar fashion, we also obtain an initial guess of the source excitation times of the recorded events. The wavefront attributes constitute the input for wavefront tomography which represents the next step of the workflow and allows for a refinement of the previously evaluated source excitation times while simultaneously approximating the velocity distribution. In a last step, we use the final estimate of the velocity distribution and compute the respective image function by reverse time modelling to gain the source locations. This paper introduces the theoretical concept of our proposed approach for the general 3D case. We analyse the feasibility of our strategy and the influences of different acquisition settings by means of a synthetic 2D data example. In a final 3D field data example we use the workflow to localise a deep earthquake without relying on a given velocity model. The approach can deal with high levels of noise and low signal amplitudes, respectively, as well as sparse geophone sampling. The workflow generally delivers good approximations of the long-wavelength velocity variations along with accurate source locations.〈/span〉

Description: Article Chiral spin liquids, a topological phase in frustrated quantum spin systems, have been recently very sought-after. Here, Bauer et al. present a model for a Mott insulator on the Kagome lattice with broken time-reversal symmetry exhibiting such a topological phase. Nature Communications doi: 10.1038/ncomms6137 Authors: B. Bauer, L. Cincio, B.P. Keller, M. Dolfi, G. Vidal, S. Trebst, A.W.W. Ludwig

Description: The vadose zone plays an important role in the hydrologic cycle. Various geophysical methods can determine soil water content variations in time and space in volumes ranging from a few cubic centimeters to several cubic meters. In contrast to the established methods, time-lapse gravity measurements of changes in soil water content do not rely on a petrophysical relationship between the measured quantity and the water content but give a direct measure of the mass change in the soil. Only recently has the vadose zone been systematically incorporated when ground-based gravity data are used to infer hydrologic information. In this study, changes in the soil water content gave rise to a measurable signal in a forced infiltration experiment on a 107-m2 grassland area. Time-lapse gravity data were able to constrain the van Genuchten soil hydraulic parameters in both a synthetic example and a field experiment with forced infiltration. The most significant reduction in parameter uncertainty was achieved for the saturated water content, while gravity data had some ability to constrain the saturated hydraulic conductivity and the van Genuchten n. Cross-borehole ground penetrating radar data were used to support the interpretation and control of the gravity data.

Description: The Pluto system was recently explored by NASA's New Horizons spacecraft, making closest approach on 14 July 2015. Pluto's surface displays diverse landforms, terrain ages, albedos, colors, and composition gradients. Evidence is found for a water-ice crust, geologically young surface units, surface ice convection, wind streaks, volatile transport, and glacial flow. Pluto's atmosphere is highly extended, with trace hydrocarbons, a global haze layer, and a surface pressure near 10 microbars. Pluto's diverse surface geology and long-term activity raise fundamental questions about how small planets remain active many billions of years after formation. Pluto's large moon Charon displays tectonics and evidence for a heterogeneous crustal composition; its north pole displays puzzling dark terrain. Small satellites Hydra and Nix have higher albedos than expected.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Stern, S A -- Bagenal, F -- Ennico, K -- Gladstone, G R -- Grundy, W M -- McKinnon, W B -- Moore, J M -- Olkin, C B -- Spencer, J R -- Weaver, H A -- Young, L A -- Andert, T -- Andrews, J -- Banks, M -- Bauer, B -- Bauman, J -- Barnouin, O S -- Bedini, P -- Beisser, K -- Beyer, R A -- Bhaskaran, S -- Binzel, R P -- Birath, E -- Bird, M -- Bogan, D J -- Bowman, A -- Bray, V J -- Brozovic, M -- Bryan, C -- Buckley, M R -- Buie, M W -- Buratti, B J -- Bushman, S S -- Calloway, A -- Carcich, B -- Cheng, A F -- Conard, S -- Conrad, C A -- Cook, J C -- Cruikshank, D P -- Custodio, O S -- Dalle Ore, C M -- Deboy, C -- Dischner, Z J B -- Dumont, P -- Earle, A M -- Elliott, H A -- Ercol, J -- Ernst, C M -- Finley, T -- Flanigan, S H -- Fountain, G -- Freeze, M J -- Greathouse, T -- Green, J L -- Guo, Y -- Hahn, M -- Hamilton, D P -- Hamilton, S A -- Hanley, J -- Harch, A -- Hart, H M -- Hersman, C B -- Hill, A -- Hill, M E -- Hinson, D P -- Holdridge, M E -- Horanyi, M -- Howard, A D -- Howett, C J A -- Jackman, C -- Jacobson, R A -- Jennings, D E -- Kammer, J A -- Kang, H K -- Kaufmann, D E -- Kollmann, P -- Krimigis, S M -- Kusnierkiewicz, D -- Lauer, T R -- Lee, J E -- Lindstrom, K L -- Linscott, I R -- Lisse, C M -- Lunsford, A W -- Mallder, V A -- Martin, N -- McComas, D J -- McNutt, R L Jr -- Mehoke, D -- Mehoke, T -- Melin, E D -- Mutchler, M -- Nelson, D -- Nimmo, F -- Nunez, J I -- Ocampo, A -- Owen, W M -- Paetzold, M -- Page, B -- Parker, A H -- Parker, J W -- Pelletier, F -- Peterson, J -- Pinkine, N -- Piquette, M -- Porter, S B -- Protopapa, S -- Redfern, J -- Reitsema, H J -- Reuter, D C -- Roberts, J H -- Robbins, S J -- Rogers, G -- Rose, D -- Runyon, K -- Retherford, K D -- Ryschkewitsch, M G -- Schenk, P -- Schindhelm, E -- Sepan, B -- Showalter, M R -- Singer, K N -- Soluri, M -- Stanbridge, D -- Steffl, A J -- Strobel, D F -- Stryk, T -- Summers, M E -- Szalay, J R -- Tapley, M -- Taylor, A -- Taylor, H -- Throop, H B -- Tsang, C C C -- Tyler, G L -- Umurhan, O M -- Verbiscer, A J -- Versteeg, M H -- Vincent, M -- Webbert, R -- Weidner, S -- Weigle, G E 2nd -- White, O L -- Whittenburg, K -- Williams, B G -- Williams, K -- Williams, S -- Woods, W W -- Zangari, A M -- Zirnstein, E -- New York, N.Y. -- Science. 2015 Oct 16;350(6258):aad1815. doi: 10.1126/science.aad1815.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Southwest Research Institute, Boulder, CO 80302, USA. astern@boulder.swri.edu. ; Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303, USA. ; National Aeronautics and Space Administration (NASA) Ames Research Center, Space Science Division, Moffett Field, CA 94035, USA. ; Southwest Research Institute, San Antonio, TX 28510, USA. ; Lowell Observatory, Flagstaff, AZ 86001, USA. ; Department of Earth and Planetary Sciences, Washington University, St. Louis, MO 63130, USA. ; Southwest Research Institute, Boulder, CO 80302, USA. ; Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA. ; Universitat der Bundeswehr Munchen, Neubiberg 85577, Germany. ; Planetary Science Institute, Tucson, AZ 85719, USA. ; KinetX Aerospace, Tempe, AZ 85284, USA. ; NASA Jet Propulsion Laboratory, La Canada Flintridge, CA 91011, USA. ; Massachusetts Institute of Technology, Cambridge, MA 02139, USA. ; University of Bonn, Bonn D-53113, Germany. ; NASA Headquarters (retired), Washington, DC 20546, USA. ; University of Arizona, Tucson, AZ 85721, USA. ; Cornell University, Ithaca, NY 14853, USA. ; NASA Headquarters, Washington, DC 20546, USA. ; Rheinisches Institut fur Umweltforschung an der Universitat zu Koln, Cologne 50931, Germany. ; Department of Astronomy, University of Maryland, College Park, MD 20742, USA. ; Search for Extraterrestrial Intelligence Institute, Mountain View, CA 94043, USA. ; Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA. ; NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA. ; National Optical Astronomy Observatory, Tucson, AZ 26732, USA. ; NASA Marshall Space Flight Center, Huntsville, AL 35812, USA. ; Stanford University, Stanford, CA 94305, USA. ; Space Telescope Science Institute, Baltimore, MD 21218, USA. ; University of California, Santa Cruz, CA 95064, USA. ; Lunar and Planetary Institute, Houston, TX 77058, USA. ; Michael Soluri Photography, New York, NY 10014, USA. ; Johns Hopkins University, Baltimore, MD 21218, USA. ; Roane State Community College, Jamestown, TN 38556, USA. ; George Mason University, Fairfax, VA 22030, USA. ; Department of Astronomy, University of Virginia, Charlottesville, VA 22904, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26472913" target="_blank"〉PubMed〈/a〉

Description: Compressed air energy storage (CAES) in porous formations is considered as one option for large-scale energy storage to compensate for fluctuations from renewable energy production. To analyse the feasibility of such a CAES application and the deliverability of an underground porous formation, a hypothetical CAES scenario using an anticline structure is investigated. Two daily extraction cycles of 6 h each are assumed, complementing high solar energy production around noon. A gas turbine producing 321 MW of power with a minimum inlet pressure of 43 bar at 417 kg s –1 air is assumed. Simulation results show that using six wells the 20 m-thick storage formation with a permeability of 1000 mD can support the required 6 h continuous power output of 321 MW, even reaching 8 h maximally. For the first 30 min, maximum power output is higher, at 458 MW, continuously dropping afterwards. A sensitivity analysis shows that the number of wells required does not linearly decrease with increasing permeability of the storage formation due to well inference during air extraction. For each additional well, the continuous power output increases by 4.8 h and the maximum power output within the first 30 min by 76 MW.