Identifying Seismic Anomalies via Wavelet Maxima Analysis of Satellite Microwave Brightness Temperature Observations

This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismogenic activity. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five surface categories of...

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Published inRemote sensing (Basel, Switzerland) Vol. 16; no. 2; p. 303
Main Authors Wu, Haochen, Xiong, Pan, Chen, Jianghe, Zhang, Xuemin, Yang, Xing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2024
Subjects
2008 AD
Aftershocks
Algorithms
Analysis
Brightness
Brightness temperature
China
Continuous wavelet transform
Dielectric properties
Earth
earthquake
Earthquakes
Empirical analysis
Horizontal polarization
Machine learning
Measurement
microwave brightness temperature (MBT)
Microwave radiometers
Parameterization
Radiation
Remission
Remote sensing
Research methodology
Satellite observation
Satellites
seismic anomalies
Sensors
Temperature
Topography
Vegetation
Wavelet analysis
wavelet maxima
Wavelet transforms
Weather
China
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Summary:This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismogenic activity. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five surface categories of the epicenter area were defined contingent on position (oceanic/terrestrial) and ambient traits (soil hydration, vegetal covering). Continuous wavelet transform was applied to preprocess annualized MBT readings preceding and succeeding prototypical events of each grouping, utilizing optimized wavelet functions and orders tailored to individualized contexts. Wavelet maxima graphs visually portraying signal intensity variations facilitated the identification of aberrant phenomena, including pre-seismic accrual, co-seismic perturbation, and postseismic remission signatures. The casework found 10 GHz horizontal-polarized MBT optimally detected signals for aquatic and predominantly humid/vegetative settings, whereas 36 GHz horizontal-polarized performed best for arid, vegetated landmasses. Quantitative machine learning methods are warranted to statistically define selection standards and augment empirical forecasting leveraging lithospheric stress state inferences from sensitive MBT parametrization.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16020303