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Using GNSS Phase Observation Residuals and Wavelet Analysis to Detect Earthquakes Cover

Using GNSS Phase Observation Residuals and Wavelet Analysis to Detect Earthquakes

Artificial Satellites
Volume 58 (2023): Issue 4 (December 2023)
By: Maciej Lackowski,  Kamil Kaźmierski and  Iwona Kudłacik  
Open Access
|Jan 2024

Figures & Tables

Figure 1.

Distribution of the earthquake epicenter and GNSS stations
Distribution of the earthquake epicenter and GNSS stations

Figure 2.

Flow chart of the proposed method
Flow chart of the proposed method

Figure 3.

Coordinate differences between a priori coordinates from the RINEX file header and estimated coordinates for each epoch obtained from the kinematic PPP. The vertical dashes define the earthquake ranges obtained from the seismograph for the ACCU station
Coordinate differences between a priori coordinates from the RINEX file header and estimated coordinates for each epoch obtained from the kinematic PPP. The vertical dashes define the earthquake ranges obtained from the seismograph for the ACCU station

Figure 4.

Fast wavelet transform performed for a series of resistances for all satellites at frequency L1 for the ACCU station
Fast wavelet transform performed for a series of resistances for all satellites at frequency L1 for the ACCU station

Figure 5.

GNSS observation residuals (red) and filtered residuals (blue, top panel), continuous wavelet transform scalar for scales 1–64 (bottom panel) for filtered observation residuals for the satellite G05 at frequency L1W for the ACCU station
GNSS observation residuals (red) and filtered residuals (blue, top panel), continuous wavelet transform scalar for scales 1–64 (bottom panel) for filtered observation residuals for the satellite G05 at frequency L1W for the ACCU station

Figure 6.

Detected outliers derived from the scalogram coefficients of the filtered GNSS observation residuals for satellite G05 at frequency L1W (top panel) and the percentage of scalogram coefficients classified as outliers (bottom panel)
Detected outliers derived from the scalogram coefficients of the filtered GNSS observation residuals for satellite G05 at frequency L1W (top panel) and the percentage of scalogram coefficients classified as outliers (bottom panel)

Figure 7.

Detected outliers obtained from summed scalogram coefficients for all satellites (top panel) and percentage of scalogram coefficients classified as outliers (bottom panel)
Detected outliers obtained from summed scalogram coefficients for all satellites (top panel) and percentage of scalogram coefficients classified as outliers (bottom panel)

Figure 8.

Color combinations of outliers determined by a certain number of satellites on the L1W frequency for ACCU stations. Colors used; green: one satellite, yellow: two, orange: three, red: four, purple: five, black: six satellites.
Color combinations of outliers determined by a certain number of satellites on the L1W frequency for ACCU stations. Colors used; green: one satellite, yellow: two, orange: three, red: four, purple: five, black: six satellites.

Figure 9.

Accelerations for the N component of the seismograph (black) and the designated earthquake range for the ACCU station detected using GNSS residuals
Accelerations for the N component of the seismograph (black) and the designated earthquake range for the ACCU station detected using GNSS residuals

Summary of names, azimuths, and distances for GNSS stations and seismographs (Kudlacik et al_ 2019)

Sensor nameAzimuth of epicenterDistance [km]
GNSS stationSeismographTo Epic. epicenterBetween Sens. sensors
ACCUIT.ACCU158.9°25.40.08
AMATIT.AMT157.0°33.70.77
GUMAIV.GUMA45.6°24.00.49
MTERIV.RM33171.5°44.90.39

Computational strategy in CSRS-PPP

SectionDescription
Mask elevation7.5°
Interval0.1 s
ObservationsGPS + GLONASS
Tropospheric modelingVMF1 (Boehm, Werl, and Schuh 2006) + horizontal and zenith gradient estimation
Ionosphere modelingEstimated
Use of the precise productsProducts from IGS + CSRS-PPP clock combination (Banville 2020)

Parameters of the analyzed earthquake (after Kudlacik et al_ 2019)

ParameterValue
Date10.26.2016 19:18:08 UTC
Location3 km NNW of Visso, Italy
Epicenter42.862°N 13.096°E
Depth8 km
Magnitude6.1
Focal mechanismnormal, NP1: 333°/40°/−92°, NP2: 155°/50°/−89°
DOI: https://doi.org/10.2478/arsa-2023-0014 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
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Language: English
Page range: 341 - 354
Submitted on: Jul 6, 2023
Accepted on: Dec 22, 2023
Published on: Jan 19, 2024
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
short-term ground displacements,
GNSS observation residuals,
earthquake vibrations,
PPP,
continuous wavelet transform
Related subjects:
Geosciences,
Geosciences, other

© 2024 Maciej Lackowski, Kamil Kaźmierski, Iwona Kudłacik, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.

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