A Novel Statistical Technique to Distinguish Lunar Impacts From Shallow Moonquakes
Description:
Between 1967 and 1977, The Apollo seismic network recorded thousands of signals, such as impacts and shallow moonquakes (Nakamura et al., 1981). Correctly classifying seismic signals is crucial for assessing lunar impact and seismicity rates. However, 60% of the original events remain unclassified due to low-quality data and the time required to analyse the data. We introduce a semi-automated and objective method for discriminating shallow moonquakes from impacts. First, we convert short- and long-period spectrograms to smoothed probability density functions. Then, we calculate the K-L divergence for pairs of events. The K-L divergence is a nonparametric measure of the differences between the two probability distributions; a K-L divergence of 0 indicates an identical pair of signals. We test this new statistical method on classified events in the catalogue. Preliminary results show a K-L divergence of > 1 between previously identified shallow moonquakes and impacts but a divergence of < 0.5 between pairs of shallow moonquakes; shallow moonquakes are more similar to each other than to impacts. Thus, the K-L divergence can discriminate between shallow moonquakes and impacts. Additionally, we analyse the short period time series of the seismic signals recorded at station S15 using the Python package tsfresh, which automatically calculates over 1200 time-series features. We again test this method on classified events in the catalogue. Spectral features, such as Fourier entropy and autocorrelation, vary systematically between shallow moonquakes and impacts. A recent study (Onodera et al., 2023) identified ~40 new shallow moonquakes, and we apply the same tsfresh algorithm to these newly identified events. The newly identified moonquakes have autocorrelation and Fourier entropy values similar to the previously catalogued moonquakes. Along with supporting reanalysis of the Apollo seismic signals, we suggest these methods to distinguish the lunar seismic signals could apply to future lunar seismic data.
Session: Planetary Seismology - I
Type: Oral
Date: 5/1/2024
Presentation Time: 05:00 PM (local time)
Presenting Author: Alice
Student Presenter: No
Invited Presentation:
Authors
Alice Turner Presenting Author Corresponding Author alice.turner@jsg.utexas.edu University of Texas at Austin |
Sean Gulick sean@ig.utexas.edu University of Texas at Austin |
Daniel Trugman dtrugman@unr.edu Nevada Seismological Laboratory |
Francesco Civilini francesco.civilini@nasa.gov NASA Goddard Space Flight Center |
Keisuke Onodera onodera@ipgp.fr Institut de Physique du Globe Paris |
|
|
|
|
A Novel Statistical Technique to Distinguish Lunar Impacts From Shallow Moonquakes
Category
Planetary Seismology