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Knox, Hunter Anne

Publications and source records attributed to Knox, Hunter Anne.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Near-Field Imaging of Shallow Chemical Detonations in Granite using Change Detection Methods of Borehole Seismic Data

As part of the Source Physics Experiment (SPE) Phase I shallow chemical detonation series, multiple surface and borehole active-source seismic campaigns were executed to perform high-resolution imaging of seismic velocity changes in the granitic substrate. Cross-correlation data processing methods were implemented to efficiently and robustly perform semi-automated change detection of first-arrival times between campaigns. The change detection algorithm updates the arrival times, and consequently the velocity model, of each campaign. The resulting tomographic imagery reveals the evolution of the subsurface velocity structure as the detonations progressed.

42 ENGINEERING↗
DOE OSTI2018

Near-Field Imaging of Shallow Chemical Detonations in Granite using Change Detection Methods of Borehole Seismic Data

As part of the Source Physics Experiment (SPE) Phase I shallow chemical detonation series, multiple surface and borehole active-source seismic campaigns were executed to perform high resolution imaging of seismic velocity changes in the granitic substrate. Cross-correlation data processing methods were implemented to efficiently and robustly perform semi-automated change detection of first-arrival times between campaigns. The change detection algorithm updates the arrival times, and consequently the velocity model, of each campaign. The resulting tomographic imagery reveals the evolution of the subsurface velocity structure as the detonations progressed.

42 ENGINEERING↗
DOE OSTI2018

EGS Collab Testbed 1: Baseline Cross-well Seismic

As part of the geophysical characterization suite for the first EGS Collab tesbed, here are the baseline cross-well seismic data and resultant models. The campaign seismic data have been organized, concatenated with geometry and compressional (P-) & and shear (S-) wave picks, and submitted as SGY files. P-wave data were collected and analyzed in both 2D and 3D, while S-wave data were collected and analyzed in 2D only. Inversion models are provided as point volumes; the volumes have been culled to include only the points within source/receiver array coverage. The full models space volumes are also included, if relevant. An AGU 2018 poster by Linneman et al. is included that provides visualizations/descriptions of the cross-well seismic characterization method, elastic moduli calculations, and images of model inversion results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Adaptive Self-Tuning of Signal Detection Parameters

The quality of automatic detections from sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings, yet achieving superior automatic detection of events is closely related to these parameters. We present an automated sensor tuning (AST) system that tunes effective parameter settings for each sensor detector to the current state of the environment by leveraging cooperation within a neighborhood of sensors. After a stabilization period, the AST algorithm can adapt in near real-time to changing conditions and automatically self-tune a signal detector to identify (detect) only signals from events of interest. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections from a seismic sensor network monitoring the Mount Erebus Volcano in Antarctica, we show that AST increases the probability of detection while decreasing false alarms.

58 GEOSCIENCES↗