Open-Source ANSS Quake Monitoring System Software

ANSS stands for the Advanced National Seismic System of the U.S.A., and ANSS Quake Monitoring System (AQMS) is the earthquake management system (EMS) that most of its member regional seismic networks (RSNs) use. AQMS is based on Earthworm, but instead of storing files on disk, it uses a relational database with replication capability to store pick, amplitude, waveform, and event parameters. The replicated database and other features of AQMS make it a fully redundant system. A graphical user interface written in Java, Jiggle, is used to review automatically generated picks and event solutions, relocate events, and recalculate magnitudes. Add‐on mechanisms to produce various postearthquake products such as ShakeMaps and focal mechanisms are available as well. It provides a configurable automatic alarming and notification system. The Pacific Northwest Seismic Network, one of the Tier 1 ANSS RSNs, has modified AQMS to be compatible with a freely available, capable, open‐source database system, PostgreSQL, and is running this version successfully in production. The AQMS Software Working Group has moved the software from a subversion repository server hosted at the California Institute of Technology to a public repository at gitlab.com. The drawback of AQMS as a whole is that it is complex to fully configure and comprehend. Nevertheless, the fact that it is very capable, documented, and now free to use, might make it an attractive EMS choice for many seismic networks

Assessing and reducing stratigraphic uncertainty in the subsurface: where are we standing?

Sedimentary strata are essential archives of the past conditions of the earth, and host significant natural resources in the subsurface. However, inferring the features of strata at depth (e.g., geometry, connectivity, physical or geological properties), remains a challenge prone to many uncertainties. Classically, the layers and their geometry are first interpreted from boreholes, geological outcrops and geophysical images, then layer properties can be addressed with geostatistical techniques and inverse methods. Theoretical models considering horizon depth uncertainty have been proposed decades ago, and geostatistical simulation can sample petrophysical uncertainties, but these approaches leave the number of layers fixed and are rely on conformable layering assumptions which are seldom met. We review some recent developments in well correlation in the frame of relative chronostratigraphy, which addresses the problem of locating potential gaps in the stratigraphic record. We also present some first results of the integration of the number of layers in inverse problems using a reversible jump Monte Carlo method. These two elements open interesting perspectives to jointly address topological, geometrical and petrophysical uncertainties at multiple scales in sedimentary basins. Although such uncertainties can have significant impact on quantitative geological and geophysical model forecasts, many computational challenges still lie ahead to appropriately sample uncertainties. Harnessing these challenges should open the way to finding, on a case-by-case basis, the suitable level of detail between detailed stratigraphic architectures and effective medium representations

Open-Source ANSS Quake Monitoring System Software

ANSS stands for the Advanced National Seismic System of the U.S.A., and ANSS Quake Monitoring System (AQMS) is the earthquake management system (EMS) that most of its member regional seismic networks (RSNs) use. AQMS is based on Earthworm, but instead of storing files on disk, it uses a relational database with replication capability to store pick, amplitude, waveform, and event parameters. The replicated database and other features of AQMS make it a fully redundant system. A graphical user interface written in Java, Jiggle, is used to review automatically generated picks and event solutions, relocate events, and recalculate magnitudes. Add‐on mechanisms to produce various postearthquake products such as ShakeMaps and focal mechanisms are available as well. It provides a configurable automatic alarming and notification system. The Pacific Northwest Seismic Network, one of the Tier 1 ANSS RSNs, has modified AQMS to be compatible with a freely available, capable, open‐source database system, PostgreSQL, and is running this version successfully in production. The AQMS Software Working Group has moved the software from a subversion repository server hosted at the California Institute of Technology to a public repository at gitlab.com. The drawback of AQMS as a whole is that it is complex to fully configure and comprehend. Nevertheless, the fact that it is very capable, documented, and now free to use, might make it an attractive EMS choice for many seismic networks

Slip Triggered on Southern California Faults by the 1992 Joshua Tree, Landers, and Big Bear Earthquakes

Five out of six functioning creepmeters on southern California faults recorded slip triggered at the time of some or all of the three largest events of the 1992 Landers earthquake sequence. Digital creep data indicate that dextral slip was triggered within 1 min of each mainshock and that maximum slip velocities occurred 2 to 3 min later. The duration of triggered slip events ranged from a few hours to several weeks. We note that triggered slip occurs commonly on faults that exhibit fault creep. To account for the observation that slip can be triggered repeatedly on a fault, we propose that the amplitude of triggered slip may be proportional to the depth of slip in the creep event and to the available near-surface tectonic strain that would otherwise eventually be released as fault creep. We advance the notion that seismic surface waves, perhaps amplified by sediments, generate transient local conditions that favor the release of tectonic strain to varying depths. Synthetic strain seismograms are presented that suggest increased pore pressure during periods of fault-normal contraction may be responsible for triggered slip, since maximum dextral shear strain transients correspond to times of maximum fault-normal contraction

Corporal punishment and child adjustment

Abstract The association between corporal punishment and children's emotional and behavioral functioning was studied in a sample of 98 non-referred children with a mean age of 12.35 (SD = 1.72) recruited from two school systems in the southeastern United States. Children were divided into those who had experienced no corporal punishment over approximately a two-week period, those who had experienced mild levels of corporal punishment (i.e., 1 or 2 instances), and those who had experienced high levels of corporal punishment (i.e., 3 or more instances). Results indicated that use of corporal punishment was associated with problems in both emotional and behavioral adjustment. However, these associations were strongest for children who experienced high levels of corporal punishment, for children who were impulsive, and for children who did not experience a warm and supportive family climate

SLAMBench 3.0:Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding

As the SLAM research area matures and the number of SLAM systems available increases, the need for frameworks that can objectively evaluate them against prior work grows. This new version of SLAMBench moves beyond traditional visual SLAM, and provides new support for scene understanding and non-rigid environments (dynamic SLAM). More concretely for dynamic SLAM, SLAMBench 3.0 includes the first publicly available implementation of DynamicFusion, along with an evaluation infrastructure. In addition, we include two SLAM systems (one dense, one sparse) augmented with convolutional neural networks for scene understanding, together with datasets and appropriate metrics. Through a series of use-cases, we demonstrate the newly incorporated algorithms, visulation aids and metrics (6 new metrics, 4 new datasets and 5 new algorithms)

Diplomat: Mapping of multi-kernel applications using a static dataflow abstraction

In this paper we propose a novel approach to heterogeneous embedded systems programmability using a taskgraph based framework called Diplomat. Diplomat is a taskgraph framework that exploits the potential of static dataflow modeling and analysis to deliver performance estimation and CPU/GPU mapping. An application has to be specified once, and then the framework can automatically propose good mappings. We evaluate Diplomat with a computer vision application on two embedded platforms. Using the Diplomat generation we observed a 16% performance improvement on average and up to a 30% improvement over the best existing hand-coded implementation

Earthquake Early Warning ShakeAlert System: West Coast Wide Production Prototype

Earthquake early warning (EEW) is an application of seismological science that can give people, as well as mechanical and electrical systems, up to tens of seconds to take protective actions before peak earthquake shaking arrives at a location. Since 2006, the U.S. Geological Survey has been working in collaboration with several partners to develop EEW for the United States. The goal is to create and operate an EEW system, called ShakeAlert, for the highest risk areas of the United States, starting with the West Coast states of California, Oregon, and Washington. In early 2016, the Production Prototype v.1.0 was established for California; then, in early 2017, v.1.2 was established for the West Coast, with earthquake notifications being distributed to a group of beta users in California, Oregon, and Washington. The new ShakeAlert Production Prototype was an outgrowth from an earlier demonstration EEW system that began sending test notifications to selected users in California in January 2012. ShakeAlert leverages the considerable physical, technical, and organizational earthquake monitoring infrastructure of the Advanced National Seismic System, a nationwide federation of cooperating seismic networks. When fully implemented, the ShakeAlert system may reduce damage and injury caused by large earthquakes, improve the nation’s resilience, and speed recovery

Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper

In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to reduce execution time while meeting our quality of result objectives. In previous work we showed for the first time that it is possible to map this application to power constrained embedded systems, highlighting that decision choices made at the algorithmic design-level have the most impact. As the algorithmic design space is too large to be exhaustively evaluated, we use a previously introduced multi-objective Random Forest Active Learning prediction framework dubbed HyperMapper, to find good algorithmic designs. We show that HyperMapper generalizes on a recent cutting edge 3D scene understanding algorithm and on a modern GPU-based computer architecture. HyperMapper is able to beat an expert human hand-tuning the algorithmic parameters of the class of Computer Vision applications taken under consideration in this paper automatically. In addition, we use crowd-sourcing using a 3D scene understanding Android app to show that the Pareto front obtained on an embedded system can be used to accelerate the same application on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from 2 to over 12.Comment: 10 pages, Keywords: design space exploration, machine learning, computer vision, SLAM, embedded systems, GPU, crowd-sourcin