We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus, a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.
In this paper we will demonstrate that the computational effort of FWI can be reduced significantly by applying it to data formed by encoding and summing source gathers, if the encoding of the sources is changed between iterations. Changing the encoding between iterations changes the crosstalk noise caused by the summation of the sources. Thus, the source crosstalk-noise stacks out of the inverted earth model, allowing summation of a large number of encoded sources. We call this method encoded simultaneous-source FWI (ESSFWI).
The U.S. Department of Energy (DOE) provides scientific infrastructure and data archives to the international Arctic research community through a national user facility, the ARM Climate Research Facility, located on the North Slope of Alaska. The ARM sites at Barrow and Atqasuk, Alaska have been collecting and archiving atmospheric data for more than 10 years. These data have been used for scientific investigation as well as remote sensing validations. Funding from the Recovery Act (American Recovery and Reinvestment Act of 2009) will be used to install new instruments and upgrade existing instruments at the North Slope sites. These instruments include: scanning precipitation radar; scanning cloud radar; automatic balloon launcher; high spectral resolution lidar; eddy correlation flux systems; and upgraded ceilometer, AERI, micropulse lidar, and millimeter cloud radar. Information on these planned additions and upgrades will be provided in our poster. An update on activities planned at Oliktok Point will also be provided.
The objective of this Standard is the specification of a verification and validation approach that quantifies the degree of accuracy inferred from the comparison of solution and data for a specified variable at a specified validation point. The approach uses the concepts from experimental uncertainty analysis to consider the errors and uncertainties in both the solution and the data. The scope of this Standard is the quantification of the degree of accuracy of simulation of specified validation variables at a specified validation point for cases in which the conditions of the actual experiment are simulated. Consideration of solution accuracy at points within a domain other than the validation points, for example interpolation/extrapolation in a domain of validation, is a matter of engineering judgment specific to each family of problems and is beyond the scope of this Standard.