Spatiotemporal slip distributions associated with the 2018–2019 Bungo Channel long-term slow slip event inverted from GNSS data

Long-term slow slip events (L-SSEs) have repeatedly occurred beneath the Bungo Channel in southwestern Japan with durations of several months to a couple of years, with a recurrence interval of approximately 6 years. We estimated the spatiotemporal slip distributions of the 2018–2019 Bungo Channel L-SSE by inverting processed GNSS time series data. This event was divided into two subevents, with the first on the southwest side of the Bungo Channel from 2018.3 to 2018.7 and the second beneath the Bungo Channel from 2018.8 to 2019.4. Tectonic tremors became active on the downdip side of the L-SSE occurrence region when large slow slips took place beneath the Bungo Channel. Compared with the previous Bungo Channel L-SSEs, this spatiotemporal slip pattern and amount were similar to those of the 2002–2004 L-SSE. However, the slip expanded in the northeast and southwest directions in the latter half of the second subevent. The maximum amount of slip, the maximum slip velocity, the total released seismic moment, and the moment magnitude of the 2018–2019 L-SSE were estimated to be 28 cm, 54 cm/year, $$4.4 \times 10^{19}$$ 4.4 × 10 19 Nm, and 7.0, respectively, all of which were the largest among the 1996–1998, 2002–2004, 2009–2011, and 2018–2019 L-SSEs.

A New Multi-Scale Sliding Window LSTM Framework (MSSW-LSTM): A Case Study for GNSS Time-Series Prediction

GNSS time-series prediction plays an important role in the monitoring of crustal plate movement, and dam or bridge deformation, and the maintenance of global or regional coordinate frames. Deep learning is a state-of-the-art approach for extracting high-level abstract features from big data without any prior knowledge. Moreover, long short-term memory (LSTM) networks are a form of recurrent neural networks that have significant potential for processing time series. In this study, a novel prediction framework was proposed by combining a multi-scale sliding window (MSSW) with LSTM. Specifically, MSSW was applied for data preprocessing to effectively extract the feature relationship at different scales and simultaneously mine the deep characteristics of the dataset. Then, multiple LSTM neural networks were used to predict and obtain the final result by weighting. To verify the performance of MSSW-LSTM, 1000 daily solutions of the XJSS station in the Up component were selected for prediction experiments. Compared with the traditional LSTM method, our results of three groups of controlled experiments showed that the RMSE value was reduced by 2.1%, 23.7%, and 20.1%, and MAE was decreased by 1.6%, 21.1%, and 22.2%, respectively. Our results showed that the MSSW-LSTM algorithm can achieve higher prediction accuracy and smaller error, and can be applied to GNSS time-series prediction.

Spatiotemporal Slip Distributions Associated with the 2018-2019 Bungo Channel Long-Term Slow Slip Event Inverted from GNSS Data

Long-term slow slip events (L-SSEs) have occurred beneath the Bungo Channel with durations of several months to a couple of years repeatedly with a recurrence interval of approximately six years. We estimated the spatiotemporal slip distributions of the 2018–2019 Bungo Channel L-SSE inverted from processed GNSS time series data. This event was divided into two subevents, with the first on the southwest side of the Bungo Channel from 2018.3 to 2018.7 and the second beneath the Bungo Channel from 2018.8 to 2019.4. Tectonic tremors became active on the downdip side of the L-SSE occurrence region when large slow slips took place beneath the Bungo Channel. Compared with the previous Bungo Channel L-SSEs, this spatiotemporal slip pattern and amount were similar to those of the 2003 L-SSE. However, the slip expanded in the northeast-southwest direction in the latter half of the second subevent. We also found that the total duration of the two subevents was 1.0 year, which was the shortest among the four recent L-SSEs beneath the Bungo Channel identified using GNSS time series data. The maximum amount of slip, the maximum slip velocity, the total released seismic moment, and the moment magnitude of the 2018–2019 L-SSE were estimated to be 27 cm, 53 cm/year, 4.1×1019 Nm, and 7.0, respectively, all of which were the largest among the four L-SSEs.

GEODYNAMICS

The goal. Identify the relationship between seasonal temperature changes and vertical and horizontal displacements of GNSS control points based on data obtained by the automated monitoring system of the Dnipro HPP dam in the period from 2016 to 2020. Input data. The research used data of uninterrupted GNSS measurements obtained at 16 points of the Dnipro HPP dam from mid-2016 to mid-2020. Method. A specially developed software product analyzes the GNSS time series of measurements pre-processed by the GeoMoS system to determine the parameters of seasonal displacements and their relationship with seasonal changes in air temperature. The GNSS time series analysis. Based on the conducted research, the influence of environmental temperature has a decisive effect on the cyclicity of dam deformations. This applies to both horizontal and vertical displacements but in the absence of significant changes in the water level in the upper reservoir. Values of extreme displacements increase closer to the middle of the dam and decrease at the edges. This tendency is observed every year in the study period. According to the three-year GNSS dam monitoring, the amplitude of semi-annual horizontal oscillations of the control points relative to the dam axis is in the range of 15-18 mm. Almost all vectors of horizontal displacements are perpendicular to the axis of the arcuate dam. In the first half of the year, the vectors of horizontal displacements aim to widen the dam, and in the second half of the year - at compressing the dam. The analysis of the data represents that almost every year, extreme deviations, both horizontal and vertical, occur in February and August. Temperature extremes occur faster than excessive GNSS displacements. For the dam of the Dnipro HPP, the extreme horizontal displacements lag on average by 37 days, and the vertical ones - by 32 days from the extreme temperatures. The deformations of the dam are related to the concrete structure temperature, which changes with a certain delay relative to the air temperature. The magnitudes of extreme displacements and the epoch of their manifestation depend on the dam's design and its technical parameters. For each dam, these extreme displacements and the periods of their representation will be different. Accordingly, monitoring these displacements and their changes over time is one of the criteria for assessing the general condition of the dam. Scientific novelty and practical significance. The regularities of the connection between the change of temperature and the displacements of the GNSS points, revealed during the research, can be used for the further study of data processing and analysis of the hydraulic structures monitoring.

Comparative Analysis of Statistical and Analytical Techniques for the Study of GNSS Geodetic Time Series

GNSS systems allow precise resolution of the geodetic positioning problem through advanced techniques of GNSS observation processing (PPP or relative positioning). Current instrumentation and communications capabilities allow obtaining geocentric and topocentric geodetic high frequencies time series, whose analysis provides knowledge of the tectonic or volcanic geodynamic activity of a region. In this work, the GNSS time series study was carried out through the use and adaptation of R packets to determine their behavior, obtaining displacement velocities, noise levels, precursors in the time series, anomalous episodes, and their temporal forecast. Statistical and analytical methods were studied, for example, ARMA, ARIMA models, least-squares methods, wavelet functions, and Kalman techniques. To carry out a comparative analysis of these techniques and methods, significant GNSS time series obtained in geodynamically active regions (tectonic and/or volcanic) were considered.

Comparative Analysis of Non-Linear GNSS Geodetic Time Series Filtering Techniques: El Hierro Volcanic Process (2010–2014)

GNSS geodetic time series analysis allows the study of the geodynamic behavior of a specific terrestrial area. These time series define the temporal evolution of the geocentric or topocentric coordinates obtained from geodetic stations, which are linear or non-linear depending, respectively, on the tectonic or volcanic–tectonic character of a region. Linear series are easily modeled but, for the study of nonlinear series, it is necessary to apply filtering techniques that provide a more detailed analysis of their behavior. In this work, a comparative analysis is carried out between different filtering techniques and non–linear GNSS time series analysis: 1sigma–2sigma filter, outlier filter, wavelet analysis, Kalman filter and CATS analysis (Create and Analyze Time Series). This comparative methodology is applied to the time series that describe the volcanic process of El Hierro island (2010–2014). Among them, the time series of the slope distance variation between FRON (El Hierro island) and LPAL (La Palma island) stations is studied, detecting and analyzing the different phases involved in the process.

Non-tidal loading of the Baltic Sea in Latvian GNSS time series

The objective of this study is to investigate the effect of the Baltic Sea non-tidal loading in the territory of Latvia using observations of the GNSS continuously operating reference stations (CORS) of LatPos, EUPOS®-Riga, EPN and EstPos networks. The GNSS station daily coordinate time series obtained in a double-difference (DD) mode were used. For representation of the sea level dynamics, the Latvian tide gauge records were used. Performed correlation analysis is based on yearly data sets of these observations for the period from 2012 up to 2020. The approach discloses how the non-tidal loading can induce variations in the time series of the regional GNSS station network. This paper increases understanding of the Earth’s surface displacements occurring due to the non-tidal loading effect in Latvia, and is intended to raise the importance and necessity of improved Latvian GNSS time series by removing loading effects.

An Analysis of Vertical Crustal Movements along the European Coast from Satellite Altimetry, Tide Gauge, GNSS and Radar Interferometry

The main aim of the article was to analyse the actual accuracy of determining the vertical movements of the Earth’s crust (VMEC) based on time series made of four measurement techniques: satellite altimetry (SA), tide gauges (TG), fixed GNSS stations and radar interferometry. A relatively new issue is the use of the persistent scatterer InSAR (PSInSAR) time series to determine VMEC. To compare the PSInSAR results with GNSS, an innovative procedure was developed: the workflow of determining the value of VMEC velocities in GNSS stations based on InSAR data. In our article, we have compiled 110 interferograms for ascending satellites and 111 interferograms for descending satellites along the European coast for each of the selected 27 GNSS stations, which is over 5000 interferograms. This allowed us to create time series of unprecedented time, very similar to the time resolution of time series from GNSS stations. As a result, we found that the obtained accuracies of the VMEC determined from the PSInSAR are similar to those obtained from the GNSS time series. We have shown that the VMEC around GNSS stations determined by other techniques are not the same.