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Title: Systems, methods and computer program products for self-tuning sensor data processing

Abstract

Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.

Inventors:
; ; ;
Issue Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1771638
Patent Number(s):
10837811
Application Number:
15/828,188
Assignee:
National Technology & Engineering Solutions of Sandia, LLC (Albuquerque, NM)
Patent Classifications (CPCs):
G - PHYSICS G01 - MEASURING G01D - MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
DOE Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Patent
Resource Relation:
Patent File Date: 11/30/2017
Country of Publication:
United States
Language:
English

Citation Formats

Draelos, Timothy J., Faust, Aleksandra, Knox, Hunter A., and Peterson, Matthew Gregor. Systems, methods and computer program products for self-tuning sensor data processing. United States: N. p., 2020. Web.
Draelos, Timothy J., Faust, Aleksandra, Knox, Hunter A., & Peterson, Matthew Gregor. Systems, methods and computer program products for self-tuning sensor data processing. United States.
Draelos, Timothy J., Faust, Aleksandra, Knox, Hunter A., and Peterson, Matthew Gregor. Tue . "Systems, methods and computer program products for self-tuning sensor data processing". United States. https://www.osti.gov/servlets/purl/1771638.
@article{osti_1771638,
title = {Systems, methods and computer program products for self-tuning sensor data processing},
author = {Draelos, Timothy J. and Faust, Aleksandra and Knox, Hunter A. and Peterson, Matthew Gregor},
abstractNote = {Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Nov 17 00:00:00 EST 2020},
month = {Tue Nov 17 00:00:00 EST 2020}
}

Works referenced in this record:

Automatic picking of seismic arrivals in local earthquake data using an artificial neural network
journal, March 1995


Automatic P-Wave Arrival Detection and Picking with Multiscale Wavelet Analysis for Single-Component Recordings
journal, October 2003


Multi-component autoregressive techniques for the analysis of seismograms
journal, June 1999


Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes
journal, December 1974


PhasePApy: A Robust Pure Python Package for Automatic Identification of Seismic Phases
journal, August 2016


Automated identification, location, and volume estimation of rockfalls at Piton de la Fournaise volcano
journal, May 2014


A procedure for the modeling of non-stationary time series
journal, December 1978


Identification and picking of S phase using an artificial neural network
journal, October 1997


Automatic earthquake recognition and timing from single traces
journal, October 1978


Automatic Detection and Assessment of Chemical, Biological, Radiological, and Nuclear Threats
patent-application, January 2004


Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings
journal, June 1999


An Automatic Kurtosis-Based P- and S-Phase Picker Designed for Local Seismic Networks
journal, November 2013


A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations
journal, September 2012


An automatic phase picker for local and teleseismic events
journal, August 1987


Automatic $P$-Phase Picking Based on Local-Maxima Distribution
journal, August 2008


Systems and Methods for CMOS-Compatible Silicon Nano-Wire Sensors with Biochemical and Cellular Interfaces
patent-application, November 2010


Wavelet transform methods for phase identification in three-component seismograms
journal, December 1997


Automatic Picker Developments and Optimization: A Strategy for Improving the Performances of Automatic Phase Pickers
journal, May 2012


Automatic picking of P and S phases using a neural tree
journal, January 2006


Emerging applications of wavelets: A review
journal, March 2010


Continuous Wavelet Decomposition Algorithms for Automatic Detection of Compressional‐ and Shear‐Wave Arrival Times
journal, May 2015