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Title: Seismic classification through sparse filter dictionaries

Abstract

We tackle a multi-label classi cation problem involving the relation between acoustic- pro le features and the measured seismogram. To isolate components of the seismo- grams unique to each class of acoustic pro le we build dictionaries of convolutional lters. The convolutional- lter dictionaries for the individual classes are then combined into a large dictionary for the entire seismogram set. A given seismogram is classi ed by computing its representation in the large dictionary and then comparing reconstruction accuracy with this representation using each of the sub-dictionaries. The sub-dictionary with the minimal reconstruction error identi es the seismogram class.

Authors:
 [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1392830
Report Number(s):
LA-UR-17-28229
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; sparse dictionary learning

Citation Formats

Hickmann, Kyle Scott, and Srinivasan, Gowri. Seismic classification through sparse filter dictionaries. United States: N. p., 2017. Web. doi:10.2172/1392830.
Hickmann, Kyle Scott, & Srinivasan, Gowri. Seismic classification through sparse filter dictionaries. United States. doi:10.2172/1392830.
Hickmann, Kyle Scott, and Srinivasan, Gowri. Wed . "Seismic classification through sparse filter dictionaries". United States. doi:10.2172/1392830. https://www.osti.gov/servlets/purl/1392830.
@article{osti_1392830,
title = {Seismic classification through sparse filter dictionaries},
author = {Hickmann, Kyle Scott and Srinivasan, Gowri},
abstractNote = {We tackle a multi-label classi cation problem involving the relation between acoustic- pro le features and the measured seismogram. To isolate components of the seismo- grams unique to each class of acoustic pro le we build dictionaries of convolutional lters. The convolutional- lter dictionaries for the individual classes are then combined into a large dictionary for the entire seismogram set. A given seismogram is classi ed by computing its representation in the large dictionary and then comparing reconstruction accuracy with this representation using each of the sub-dictionaries. The sub-dictionary with the minimal reconstruction error identi es the seismogram class.},
doi = {10.2172/1392830},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Sep 13 00:00:00 EDT 2017},
month = {Wed Sep 13 00:00:00 EDT 2017}
}

Technical Report:

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