Steady Crustal Deformation Around Cape Omaezaki Revealed by InSAR Time Series Analysis Method

This study investigates the kinematic behavior and deformation patterns of the Psathopyrgos normal fault in the Western Gulf of Corinth (GoC) using space geodetic techniques such as InSAR and GNSS time-series analysis. The Psathopyrgos fault is the main onshore tectonic structure of the north-dipping fault system and is located near the western tip of GoC (Tsimi et al. 2007). The crustal extension across the Corinth rift increases from east to west and reaches its maximum value in the western GoC where the Psathopyrgos fault is located.  Our analysis covers the period from 2016 to 2022 and leverages LiCSBAS, an open-source package, for InSAR time series analysis with the N-SBAS method. We combine our InSAR results with GNSS velocities in order to obtain a more accurate estimation of the deformation field. Through the InSAR time-series analysis, the E-W fault trace of the Psathopyrgos fault was mapped in detail as the ground motion pattern is affected by the long-term displacement of the fault. An offset across the fault trace was detected in the LOS position time series. The Up-Down component of InSAR confirms the LOS findings thus indicating a mainly vertical component of motion and shows an average velocity offset of 4.5 mm/yr between the two blocks across the fault, i.e., the footwall and the hanging-wall. This geodetic evidence confirms the creeping behavior of the fault. The E-W cross-sections of the InSAR velocity data also show contrasting patterns of motion. The E-W component of InSAR reveals a right-lateral slip along the western segment of the fault. An additional finding was provided by the examination of the time-series of the pixels that are located on the hanging wall of the Psathopyrgos fault. These pixels include offsets related to possible co-seismic or passive slip of Psathopyrgos fault because of the 17 February 2021 M5.3 offshore earthquake (Zahradnik et al. 2022). The offset in the time-series was about 0.01 m. The geodetic data indicate a possible surface rupture or passive slip along the Psathopyrgos fault plane, together with continuous motion that could relate to migration of fluids and aseismic creep. These new findings suggest a combination of slip history including fault rupture, aseismic creep, and fluid migration, thus, contributing to a better understanding of the interseismic and co-seismic dynamics of the Psathopyrgos active fault. Tsimi, Ch., Ganas, A., Soulakellis, N., Kairis, O., and Valmis, S., 2007. Morphotectonics of the Psathopyrgos active fault, western Corinth rift, central Greece. Bulletin of the Geological Society of Greece, vol. 40, 500-511 http://dx.doi.org/10.12681/bgsg.16657  .Zahradník, J., Aissaoui, E. M., Bernard, P., Briole, P., Bufféral, S., De Barros, L., et al. (2022). An atypical shallow Mw 5.3, 2021 earthquake in the western Corinth rift (Greece). Journal of Geophysical Research: Solid Earth, 127, e2022JB024221. https://doi.org/10.1029/2022JB02422

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS‐based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short‐spatial scales (<10 km). Overall, MODIS‐inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS‐based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

Oblique rift zones and bookshelf structures in the Reykjanes Peninsula are part of the intricate boundary that separates the North American from the Eurasian plates. Studying their evolution over several years provides a deeper understanding of plate tectonic processes and also aim to better understand the tectonic conditions that precede volcanic eruptions. After a period of dormancy lasting 800 years, the Reykjanes Peninsula has experienced strong episodic plate boundary motions since at least 2017 and four eruptions in 2021, 2022, and 2023 (twice). These events have been accompanied by significant seismic activity and ground displacement related to strain release at the plate boundary and around dike intrusions. Here we employed D-InSAR, stacking-InSAR, and PSI time series to investigate the deformation occurring in the Reykjanes Peninsula from June 2016 to December 2023, covering the period before and during the onset of the eruption phases. Due to the fast deformation during large earthquakes and eruptions, we separately analyze dike intrusions before the four eruptions, five earthquake swarms, and seven time series between the above-mentioned events. The discretization of the observation period allows us to improve the InSAR process for different events and to avoid confusing the deformations originating from different events. Using InSAR and seismicity, we identifiund displacement and earthquake swarms in the Fagradalsfjall area in July 2017, July 2020, August 2020 and October 2020, highlighting an activation of localized portions of the plate boundary at least four years before the first eruption. The overall earthquake distribution aligns with the plate boundary (N070), but suggests a bookshelf structure composed of north-south fault ruptures. We use the vbmethod to decompose the InSAR time-series into independent components of deformation such as intrusions, earthquakes, and tectonic plate motion. From the InSAR time series analysis, we observed that before each dike intrusion, the Southern Fagradalsfjall-Krysuvik () area exhibits an overall southeastward movement. The results of vbICA also suggest that this area has been accelerating since 2020.   The comprehensive observations of tectonic and volcanic activity in the Reykjanes Peninsula, using both InSAR time series and seismicity over seven and a half years provide valuable insights to better understand the onset of oblique rifting events at divergent plate boundaries.

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Chapter 4 - Bayesian nonparametric methods for financial and macroeconomic time series analysis

Analysis of temporal trends in medicine is needed to understand normal physiology and to study the evolution of disease processes. It is also useful for monitoring response to drugs and interventions, and for accountability and tracking of health care resources. In this review, we discuss what makes time series analysis unique for the purposes of renal research and its limitations. We also introduce nonlinear time series analysis methods and provide examples where these have advantages over linear methods. We review areas where these computational methods have found applications in nephrology ranging from basic physiology to health services research. Some examples include noninvasive assessment of autonomic function in patients with chronic kidney disease, dialysis-dependent renal failure and renal transplantation. Time series models and analysis methods have been utilized in the characterization of mechanisms of renal autoregulation and to identify the interaction between different rhythms of nephron pressure flow regulation. They have also been used in the study of trends in health care delivery. Time series are everywhere in nephrology and analyzing them can lead to valuable knowledge discovery. The study of time trends of vital signs, laboratory parameters and the health status of patients is inherent to our everyday clinical practice, yet formal models and methods for time series analysis are not fully utilized. With this review, we hope to familiarize the reader with these techniques in order to assist in their proper use where appropriate.

Identification and monitoring of unstable slopes across wide regions using Synthetic Aperture Radar Interferometry (InSAR) can further help to prevent and mitigate geological hazards. However, the low spatial density of measurement points (MPs) extracted using the traditional time-series InSAR method in topographically complex mountains and vegetation-covered slopes makes the final result unreliable. In this study, a method of time-series InSAR analysis using single- and multi-look phases were adopted to solve this problem, which exploited single- and multi-look phases to increase the number of MPs in the natural environment. Archived ascending and descending Sentinel-1 datasets covering Zhouqu County were processed. The results revealed that nine landslides could be quickly identified from the average phase rate maps using the Stacking method. Then, the time-series InSAR analysis with single- and multi-look phases could be used to effectively monitor the deformation of these landslides and to quantitatively analyze the magnitude and dynamic evolution of the deformation in various parts of the landslides. The reliability of the InSAR results was further verified by field investigations and Unmanned Aerial Vehicle (UAV) surveys. In addition, the precursory movements and causative factors of the recent Yahuokou landslide were analyzed in detail, and the application of the time-series InSAR method in landslide investigations was discussed and summarized. Therefore, this study has practical significance for early warning of landslides and risk mitigation.

The freezing and thawing processes of the active permafrost layer, driven by temperature variations between summer and winter, lead to surface seasonal uplift and subsidence, which can be captured by time-series InSAR techniques and associated permafrost modeling. As climate change introduces variations in factors like soil moisture and temperature, the seasonal surface deformation experiences interannual and fluctuating variations. However, these variations have often eluded capture due to either the spatiotemporal filtering processes to mitigate atmospheric delay of temporal InSAR and the approximate assumptions in permafrost deformation models. To better capture the dynamic changes in surface deformation caused by permafrost freeze-thaw processes, we develop a seasonally varying deformation method based on Autoregressive Integrated Moving Average Model (ARIMA) time series analysis. Through both synthetic data and real data experiments, we validate that the proposed method can provide more accurate deformation results while capturing the interannual variations in permafrost deformation. The real-data experiment, utilizing Sentinel-1 data, reveals that the maximum seasonal deformations in the continuous permafrost region of northern Alaska exhibit an increasing-decreasing trend from 2017 to 2021, with 2019 showing a relative maximum, correlating with the number of thawing days and air temperature in that year. This study contributes to a deeper understanding of freeze-thaw processes in permafrost regions, providing robust support for analyzing the impact of climate change on surface deformations in permafrost areas.

The article will present the methodology for holistic time series analysis, based on time series transformation model REFII (REFII is an acronym for Raise-Equal-Fall and the model version is II or 2) Patel et al. (Mining motifs in massive time series databases, 2002) [1], Perng and Parker (SQL/LPP: a time series extension of SQL based on limited patience patterns, 1999) [2], Popivanov and Miller (Similarity search over time series data using wavelets, 2002) [3]. The main purpose of REFII model is to automate time series analysis through a unique transformation model of time series. The advantage of this approach to a time series analysis is the linkage of different methods for time series analysis linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. REFII model is not a closed system, which means that there is a finite set of methods. This is primarily a model used for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in the domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analyses. In combination with elements of other methods, such as self-organizing maps or frequent-pattern trees, REFII models can make new hybrid methods for efficient time temporal data mining. Similar principle of hybridization could be used as a tool for time series temporal pattern recognition. The article describes real case study illustrating practical application of described methodology.

Abstract. Current multi-epoch InSAR techniques heavily rely on the assumption of linear deformation. This can sometimes overlook crucial deformation signals when using velocities for evaluation. The process of interpreting InSAR time series is not only time-consuming and labor-intensive but also requires a certain level of expertise. This study refines existing InSAR deformation categories, such as stable, linear, step, piecewise linear, power, and undefined, to define 'canonical deformation time series patterns.' We propose an innovative approach for InSAR post-processing using Temporal Convolutional Networks (TCN) and transfer learning. Due to the limited availability of real data, we use simulated data to train a pre-existing model. We then assess the effectiveness of our method in identifying urban deformation patterns. This research could significantly improve our understanding of large-scale InSAR time series deformation and reveal the underlying patterns.

Understanding the inner structure of the crust and upper mantle is essential to evaluate those mechanisms driving Earth’s dynamics. Usually, surface deformation provides valuable constraints on viscoelastic parameters.  Postseismic deformation following large megathrust earthquakes, offers a unique opportunity to explore the viscoelastic properties of the shallower earth structure since it is strongly influenced by viscoelastic relaxation processes. This postseismic deformation is often recorded by GNSS stations, which offer high temporal resolution and therefore are useful to constrain the relaxation time along convergent margins. However, the spatial coverage of GNSS networks is often sparse,  inhibiting our ability to study the large scale variations in viscoelastic properties of the medium. To solve these issues, we rely on InSAR time series which provide continuous spatial resolution of surface deformation. In this work, we exploit the FLATSIM project (Thollard et al., 2021) initiative considering Sentinel-1 data  over Central Chile that has been processed using the NSBAS processing chain (Doin et al., 2013). Particularly, we focus on Central Chile, with special emphasis on the 2015 8.3 Mw Illapel earthquake. The InSAR data spans 8 years and has been corrected using the global atmospheric models ERA-5. Complementary, we use GNSS time series from 25 stations deployed over the Illapel rupture area, combining stations from Centro Sismologico Nacional and the DeepTrigger project.Since both data sets contain the contribution from multiple tectonic and non-tectonic processes, we employ different techniques to isolate the postseismic deformation of the 2015 Illapel earthquake. Actually,  for GNSS, we apply Independent Component Analysis while for InSAR time series, we perform  a parametric decomposition pixel by pixel. Our findings reveal a very strong postseismic signal with a typical logarithmic decay, lasting at least 8 years.In this work, in order to investigate the underlying rheological properties of the medium, we exploit the PyLith software,  a finite-element model that can take into account the complex rheological structure of the system. To do so, we impose the co-seismic slip model coming from averaged slip solutions, thereby initiating the model to distinguish between viscoelastic and afterslip contributions. By reproducing the surface deformation patterns given jointly by GNSS and InSAR data, we aim to determine the geometrical and rheological variations beneath the Illapel rupture area, particularly those viscoelastic parameters characterizing the crust and upper mantle regions. Our analysis provide insights to better understand how these properties affect both the seismic cycle and long-term deformation patterns at local and regional scales.

Time series analysis aims to understand underlying patterns and relationships in data to inform decision-making. As time series data are becoming more widely available across a variety of academic disciplines, time series analysis has become a rapidly growing field. In particular, Bayesian nonparametric (BNP) methods are gaining traction for their power and flexibility in modeling, predicting, and extracting meaningful information from time series data. The utility of BNP methods lies in their ability to encode prior information and represent complex patterns in the data without imposing strong assumptions about the underlying distribution or functional form. BNP methods for time series analysis can be applied to a breadth of problems, including anomaly detection, noise density estimation, and time series clustering. This work presents a comprehensive survey of the existing literature on BNP methods for time series analysis. Various temporal BNP models are discussed along with notable applications and possible approaches for inference. This work also highlights current research trends in the field and potential avenues for further development and exploration.

This book [1] aims to present the best application for managing and clearly representing the massive amount of Flight Data (FD) that exists. [...]

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.