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Classification of Unlabeled Deep Moonquakes Using Machine Learning

Authors:
Shiori Kikuchi
Ryuhei Yamada
Yukio Yamamoto
Masaharu Hirota
Shohei Yokoyama
Hiroshi Ishikawa

Keywords: Waveform analysis; Neural Network

Abstract:
This paper investigates classification of deep moonquakes. Because some waveforms in deep moonquake contain much noise and small amplitude, estimating the source using conventional means is difficult.Therefore, we use machine learning based on waveform similarity to estimate the seismic sources of moonquakes.However, when the source of moonquake is unknown, the arrival time to the observation points is not determined. Therefore, cutting the S wave of a moonquake based on the arrival time is difficult. To classify waveforms for which the arrival time is not determined, we use long waveform from the start time of event, which might contain the arrival time.Moreover, we classify 43 unlabeled moonquakes observed by Apollo 12.As a result, labels were given with high classification probability for many moonquakes.

Pages: 44 to 50

Copyright: Copyright (c) IARIA, 2017

Publication date: April 23, 2017

Published in: conference

ISSN: 2308-4448

ISBN: 978-1-61208-548-7

Location: Venice, Italy

Dates: from April 23, 2017 to April 27, 2017