Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms

To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake’s magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada’s model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada’s model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake’s displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake’s magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy.

Analysis of Tectonic Plate Velocity Variations in the Sunda Strait Based on GPS Time-series Data

Indonesia is an earthquake-prone country located in the junction of four tectonic plates, namely the Indo-Australian, Eurasian, Philippine, and Pacific. The convergent boundary between tectonic plates is also called a subduction zone that can produce great earthquakes in the future. One of the subduction zones in Indonesia is the Sunda Strait subduction zone which predicted can release a M7.8 earthquake. Previous research stated that there is a change in tectonic plate velocity after an earthquake ruptured. It is likely that this could happen in the Sunda Strait area which has experienced several large earthquakes. In this study, we conducted research to find out the information on the tectonic plate velocity changes in the Sunda Strait. We used Global Positioning System (GPS) time-series data provided by Indonesia Geospatial Information Agency (BIG). The time series data is used to calculate the earthquake displacement, the changes in GPS velocity of before and after earthquake, and the changes in velocity of each time interval. Our results show that the horizontal displacement due to the earthquake at all GPS stations ranged from 3.34 mm to 7.36 mm in the north-south direction and -27.45 mm to 0.18 mm in the east-west direction. Furthermore, the result of the changes in GPS velocity before and after an earthquake ranged from 2.25 mm/year to 12.60 mm/year and 1.80 mm/year to 13.35 mm/year. The pattern of change in velocity is likely due to post-seismic deformation from the 2012 Indian Ocean earthquake, the 2016 Sumatra earthquake, and also other tectonic factors.

Uncertainty Assessments of Load Deformation from Different GPS Time Series Products, GRACE Estimates and Model Predictions: A Case Study over Europe

A good understanding of the accuracy of the Global Positioning System (GPS) surface displacements provided by different processing centers plays an important role in load deformation analysis. We estimate the noise level in both vertical and horizontal directions for four representative GPS time series products, and compare GPS results with load deformation derived from the Gravity Recovery and Climate Experiment (GRACE) gravity measurements and climate models in Europe. For the extracted linear trend signals, the differences among different GPS series are small in all the three (east, north, and up) directions, while for the annual signals the differences are large. The mean standard deviations of annual amplitudes retrieved from the four GPS series are 3.54 mm in the vertical component (69% of the signal itself) and ~ 0.3 mm in the horizontal component (30% of the signal itself). The Scripps Orbit and Permanent Array Center (SOPAC) and MEaSUREs series have the lowest noise level in vertical and horizontal directions, respectively. Through consistency/discrepancy analysis among GPS, GRACE, and model vertical series, we find that the Jet Propulsion Laboratory (JPL) and Nevada Geodetic Laboratory (NGL) series show good consistency, the SOPAC series show good agreements in annual signal with the GRACE and model, and the MEaSUREs series show substantially large annual amplitude. We discuss the possible reasons for the notable differences among GPS time series products.

The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019)

The Neapolitan volcanic area includes three active and high-risk volcanoes: Campi Flegrei caldera, Somma–Vesuvius, and Ischia island. The Campi Flegrei volcanic area is a typical example of a resurgent caldera, characterized by intense uplift periods followed by subsidence phases (bradyseism). After about 21 years of subsidence following the 1982–1984 unrest, a new inflation period started in 2005 and, with increasing rates over time, is ongoing. The overall uplift from 2005 to December 2019 is about 65 cm. This paper provides the history of the recent Campi Flegrei caldera unrest and an overview of the ground deformation patterns of the Somma–Vesuvius and Ischia volcanoes from continuous GPS observations. In the 2000–2019 time span, the GPS time series allowed the continuous and accurate tracking of ground and seafloor deformation of the whole volcanic area. With the aim of improving the research on volcano dynamics and hazard assessment, the full dataset of the GPS time series from the Neapolitan volcanic area from January 2000 to December 2019 is presented and made available to the scientific community.

Imputation of GPS Coordinate Time Series Using MissForest

The global positioning system (GPS) can provide the daily coordinate time series to help geodesy and geophysical studies. However, due to logistics and malfunctioning, missing values are often “seen” in GPS time series, especially in polar regions. Acquiring a consistent and complete time series is the prerequisite for accurate and reliable statical analysis. Previous imputation studies focused on the temporal relationship of time series, and only a few studies used spatial relationships and/or were based on machine learning methods. In this study, we impute 20 Greenland GPS time series using missForest, which is a new machine learning method for data imputation. The imputation performance of missForest and that of four traditional methods are assessed, and the methods’ impacts on principal component analysis (PCA) are investigated. Results show that missForest can impute more than a 30-day gap, and its imputed time series has the least influence on PCA. When the gap size is 30 days, the mean absolute value of the imputed and true values for missForest is 2.71 mm. The normalized root mean squared error is 0.065, and the distance of the first principal component is 0.013. MissForest outperforms the other compared methods. MissForest can effectively restore the information of GPS time series and improve the results of related statistical processes, such as PCA analysis.

The quality of velocities from GNSS campaign measurements when gaps in data exist

<p>In this study, we investigate the effect of gaps in data on the accuracy of deformation rates produced from GNSS campaign measurements. Our motivation in investigating gaps in data is that campaign GNSS time series might not be collected regularly due to various constraints in real life conditions. We used the baseline components produced from continuous GPS time series of JPL, NASA from a global network of the IGS to generate data gaps. The solutions of the IGS continuous GNSS time series were decimated to the solutions of the campaign data sampled one measurement per each month or three measurements per year. Furthermore, the effect of antenna set-up errors, which show Gaussian distribution, in campaign measurements was taken into account following the suggestions from the literature. The number of gaps in campaign GNSS time series was incremented plus one for each different trial until only one month is left within the specific year. Eventually, we tested whether the velocities obtained from GNSS campaign series containing data gaps differ significantly from the velocities derived from continuous data which is taken as to be the “truth”. The initial efforts using the samples from a restricted amount of data reveal that the deformation rate produced from the east component is more sensitive to the gaps in data than that of the components north and vertical.</p><p><strong>Keywords: </strong>GPS time series; GPS campaigns; Velocity estimation; Gaps in data; Deformation.</p>