DARHT Multi-intelligence Seismic and Acoustic Data Analysis

doi:10.2172/1291257

GSTDTAP > 地球科学

DOI	10.2172/1291257
报告编号	LA-UR--16-25378
来源ID	OSTI ID: 1291257
	DARHT Multi-intelligence Seismic and Acoustic Data Analysis
	Stevens, Garrison Nicole; Van Buren, Kendra Lu; Hemez, Francois M.
	2016-07-21
出版年	2016
页数	48
语种	英语
国家	美国
领域	地球科学
英文摘要	The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, and finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.
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来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/7191
专题	地球科学
推荐引用方式 GB/T 7714	Stevens, Garrison Nicole,Van Buren, Kendra Lu,Hemez, Francois M.. DARHT Multi-intelligence Seismic and Acoustic Data Analysis,2016.