Applying Compression Distance Metrics to Seismic Data in Support of Global Nuclear Explosion Monitoring.
Abstract not provided.
Matzen, Laura E.↗
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Results for “seismic data”
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Assessing the Accuracy of a Borehole-Controlled Velocity Model of Yucca Flat Nevada Using Large-N Seismic Data.
Abstract not provided.
Vieceli, Rhiannon↗
Deep learning denoising applied to regional distance seismic data in Utah.
Abstract not provided.
Tibi, Rigobert↗
Can Synthetic Seismic Data Make Up For Observational Sparsity? .
Abstract not provided.
Linville, Lisa↗
Deep Learning Denoising of Seismic Data Recorded at the IMS Station MKAR.
Abstract not provided.
Tibi, Rigobert↗
Machine Learning Applications for Induced Seismic Data Analysis at Illinois Basin Decatur Project Site.
Abstract not provided.
Yoon, Hongkyu↗
Applying Compression Metrics to Seismic Data to Assist Analysts
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Zhou, Angela Ellison↗
Seismic Data Denoising Using Multi-Scale Mathematical Morphological Filtering
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Tibi, Rigobert↗
Mathematical Morphological Filtering with a Self-adaptive Reconstruction Technique and Its Application to Local Seismic Data
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Tibi, Rigobert [Sandia National Laboratories (SNL-↗
Time-variable moment tensor inversion applied to seismic data collected at the PE1-A experiment
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Darrh, Andrea Nicole [Sandia National Laboratories↗
Exploring deep learning methods for characterizing near-source characteristics of buried explosions from seismic data
Item purpose: SnT virtual poster showing LYNM work Internal peer review status: This poster has been peer reviewed HQ review status: this poster has been approved by HQ
Harding, Jennifer Logan [Sandia National Laborator↗
Monte Carlo inversion of seismic data
The analytic solution to the linear inverse problem provides estimates of the uncertainty of the solution in terms of standard deviations of corrections to a particular solution, resolution of parameter adjustments, and information distribution among the observations. It is shown that Monte Carlo inversion, when properly executed, can provide all the same kinds of information for nonlinear problems. Proper execution requires a relatively uniform sampling of all possible models. The expense of performing Monte Carlo inversion generally requires strategies to improve the probability of finding passing models. Such strategies can lead to a very strong bias in the distribution of models examined unless great care is taken in their application.
Wiggins, R. A.↗
Modeling and Pattern Informatics of Remotely Sensed Crustal Deformation Data, Seismicity, and Geologic Observations for Improved Earthquake Forecasting
No abstract available
earthquake forcasting↗
Recent Re-Analyses of the Apollo Lunar Seismic Data: Insight into the Moons Deep Interior
No abstract available
Moon↗
Imaging Coseismic Deformation from Large Thrust Earthquakes on the Edges of the Tibetan Plateau with Geodetic And Seismic Data
No abstract available
Fielding, Eric↗
Imaging Coseismic Deformation from Large Thrust Earthquakes on the Edges of the Tibetan Plateau with Geodetic and Seismic Data
No abstract available
Fielding, Eric↗