Exploiting Spark for HPC Simulation Data: Taming the Ephemeral Data Explosion
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We have evaluated the concept of practicing S-wave reflection seismology with legacy 3D seismic data generated by a P-wave source and recorded with only vertical geophones. This type of S-wave imaging is based on the principle that seismic P-wave sources not only produce a downgoing illuminating P wavefield, but they also simultaneously produce a downgoing illuminating SV wavefield that, in almost all cases, is suitable for S-wave reflection imaging. The S-mode used in this study is the SV-P, or converted-P, mode. This mode involves a downgoing illuminating SV wavefield and an upgoing reflected P-mode that is recorded by vertical geophones. In flat-layered stratigraphy, the lengths of the SV and P raypaths in SV-P imaging are identical to the lengths of the SV and P raypaths in P-SV imaging with P-sources and 3C geophones. P-SV imaging of deep rocks has been practiced for more than two decades; SV-P imaging is a new concept. SV-P data should provide the same options for investigating deep rocks as do P-SV data. We have determined one of the equivalences between SV-P data extracted from vertical-geophone data and P-SV data extracted from horizontal geophones: that both modes react to azimuth-dependent variations in the S velocity in anisotropic rocks. Azimuthal variations in the SV-P traveltime can be used to define the polarization direction of the fast-S-wave mode, which is also the azimuth of the maximum horizontal stress (SHmax). Our investigation demonstrates a noninvasive method for monitoring changes in the SHmax azimuth across a CO 2 storage reservoir, or any targeted porous rock, as fluids are cycled into, and then out of, that rock’s pore space.
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With a brilliant hot flash and a loud boom, the giant mushroom cloud with its fiery core filled the predawn sky above the New Mexico desert. The nuclear age had begun. It was 75 years ago on July 16, 1945, that Los Alamos scientists changed the world with the successful detonation of “the Gadget” – the device created to test the Fat Man implosion-type plutonium weapon before it was taken into combat. However, the road to Trinity, as the test was named by J. Robert Oppenheimer, who is thought to have been inspired by a line in a John Donne poem, was a difficult one.
Advanced reactive burn models enable phenomena to be modeled that aren’t possible with programmed burn. Programmed burn propagates a burn front at a prescribed speed from a prescribed initiation point. Reactive burn uses the hydro variables (pressure, temperature, etc.) to determine when, how rapidly, and to what extent a zone detonates. This enables a completely new set of problems to be simulated. Our goal in this milestone has been to improve the usability of reactive burn modes. Thus, the closure criteria are: 1. Perform code and solution verification of the reactive burn models using appropriate solutions and simplified HE setups. 2. Perform validation assessments using small-scale experiments. 3. Evaluate sensitivities and quantify uncertainties due to model form variations and mesh dependencies.
On June 30, 2020, a 0.87 gram PETN charge being pressed in the Rapid Prototyping Facility (RPF), unexpectedly initiated, resulting in destruction of the pressing fixture but no injuries or facility damage. In response, the Safety Review Board (SRB) met on Aug. 13, 2020 and Oct. 1, 2020 to review information collected following the incident, consider likely direct causes, and form recommendations.
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These lightning strikes can be more dangerous than “normal” lightning associated with a regular thunderstorm in that the “bolts from the blue” often occur well away from where the precipitation is occurring at the surface. “Bolts from the blue” occurred at a measured distance of 33 miles and an estimated 35 to 50 miles away from where the precipitation was occurring with this storm.
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In order to fit models to data, we need a way to simulate the data given a model. Our projects involved simulators for the Manganin gauge experiment and for cylinder tests, which we use to calibrate models to experiments. We call the parameters we seek to optimize the degrees of freedom (DOF). We use a Bayesian approach with its associated UQ benefits.
The data sources tend to have the highest quality data and metadata, particularly for more recent data sets. Early data from sources such as IRIS tend to have some metadata issues.
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