GPS Time Research Articles

Analysis of deterministic and stochastic models of GPS stations in the crustal movement observation network of China

The Crustal Movement Observation Network of China (CMONOC) has been accumulating GPS observation data for nearly 20 years since its trial operation in March 1999. It is an important task to comprehensively utilize the existing data from different research organizations and institutes, which will be beneficial to the research field of geosciences. Focusing on GPS coordinate time series of 27 CMONOC stations, this paper analyses the change of its characteristics before and after land surface environmental loading corrections (ELCs) and further studies the effect of surface displacements derived from environmental loadings on deterministic and stochastic models in this long-term GPS coordinate time series. Results show that the annual periodic signal in environmental loading displacement series may reasonably explain the seasonal signals in the vertical components of CMONOC stations but fails to account for the annual variation in the horizontal components. After ELCs, velocity estimations of 63.0% of station components decrease, while velocity uncertainties have an increasing trend. Non-integer spectral indices of GPS coordinate time series have also changed, especially in the vertical component. The optimal noise models in 14.8% of station components have changed, and the two principal models are WN + FN and WN + PL. The amplitudes of white noise and random walk noise have not been weakened, while the amplitude of flicker noise in the vertical component has reduced by 17.8% on average. Velocity uncertainties have a certain linear correlation with annual average rainfall and latitudes of these CMONOC stations, and the correlation with latitudes is stronger than with rainfall. Through a comprehensive analysis of the deterministic and stochastic models contained in the GPS time series, we may gain a further understanding of the features of crustal deformation in China.

Local‐area nanosecond‐accuracy time synchronisation based on GPS L1 observations

To date, high-precision time synchronisation has been applied in many fields, such as high-precision indoor positioning, multistatic Synthetic Aperture Radar (SAR) and etc. This study focuses on the use of only global positioning system (GPS) L1 civilian signals to achieve regional nanosecond time synchronisation. GPS errors are analysed in this study to determine their effect on GPS timing. This study also examines the specific features of the error in fixed-position timing technology and related error reduction techniques to illustrate the feasibility of nanosecond high-precision time synchronisation with GPS L1 signals in a fixed-position timing mode. Then, the results of a specific GPS timing receiver working in a fixed-position mode and using GPS L1 signals are shown to indicate that the use of GPS L1 signals to achieve nanosecond-accuracy time synchronisation in a local area is feasible. Finally, this study also analyses the impacts of the imprecise position in a fixed-position timing mode.

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

AbstractFor numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validation measurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R2)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.

Slow Slip Event Detection in Cascadia Using Vertical Derivatives of Horizontal Stress Rates

AbstractWe present a new approach assuming elastic behavior at the Earth's surface to invert continuous GPS time series for Vertical Derivatives of Horizontal Stress rates. This approach enables detection of transient slow slip events at the Cascadia subduction zone. Vertical Derivatives of Horizontal Stress rates are the most spatially compact expression of surface deformation due to subsurface deformation sources (compared to GPS displacement/velocity fields, and strain rates), and enable identification of transient slip events and temporal evolution of locking on the interface throughout the slow slip cycle. Slow slip events (SSEs) identified by our method coincide spatially and temporally with tremor episodes, and agree well with SSEs identified by previous geodetic studies. We present daily slip models that fit the Vertical Derivatives of Horizontal Stress rates, showing phases of locking, unlocking, and SSEs on the subduction interface for the 2009–2015 period. Similar to previous studies, our results highlight along‐strike variations in SSEs and locking behavior from northern Cascadia to southern Cascadia. Between events, the SSE source zone in northern Cascadia appears to be completely locked. In contrast, we suggest that some fraction of plate motion in the SSE zone in central and southern Cascadia is accommodated by steady creep (or very small, frequent SSEs) between large SSE events. We observe rapid relocking of the interface (within weeks) following the end of most SSEs, with implications for the time scales of fault healing following deformation events. Our results present a promising approach to transient deformation detection, with a fine temporal and spatial resolution of the surface expression of deformation.

Solar panel monitoring: real-time system using video watermarking and Mosaicing

In this paper, we propose a new approach to monitor a solar plant in real-time using an embedded vision system, comprised mainly of raspberry Pi3 card, GPS and thermometer sensors, and an HD camera module. This approach consists on using image processing techniques on solar energy fields. It is implemented on two steps, the first apply the digital watermarking technique based on discrete cosine transform (DCT) to indexing a captured video. We start by capturing all information from the solar plant (GPS coordinates, temperature, date, and time), then we embed on each video frame their related data. This method depends on transforming the video frames from the spatial to the frequency domain using DCT and embedding data in 8 × 8 block coefficients. The second step consists on applying a mosaicing techniques based on the scale invariant features transform (SIFT), in order to generate a panoramic image of the solar plant from video frames. Remotely, the supervisor can visualize the image of the whole, part, or panel of the solar plant, and exploits the embedded data to ensure an efficient real-time monitoring. The proposed method allows a high efficiency in terms of accuracy, data security, and retrieving data quickly, also it offers a high capacity, less imperceptibility and good robustness.

An Approach to a Clock Offsets Model for Real-Time PPP Time and Frequency Transfer During Data Discontinuity

To resolve the dilemma in any post-processing strategy, i.e., the difficulty of monitoring the real-time time and frequency signals in a timely manner, real-time GPS time and the frequency transfer have recently become trending topics. Unfortunately, data interruption occurs when conducting real-time time transfer, sometimes from unexpected reasons. In this study, to ensure the stability and precision of real-time time transfer, an adaptive prediction model and a between-epoch constraint receiver clock model are applied as the mathematic models. The purpose of prediction is to solve the ambiguity from re-convergence when the data reappear. Moreover, compared to the conventional method, the between-epoch constraint receiver clock model is employed in this study to consider the correlation of epoch-wise clock parameters to avoid wasting useful information. The simulation data and real data are compared to verify the performance of the new approach. The simulation data for 165 days are designed with random daily interruptions of 10, 30, 60 and 90 min. Real data from 12 days is captured from the incomplete data in routine observation records. Ignoring the simulation data and real data, the investigation of six stations shows that the results with the between-epoch constraint receiver clock model were smoother than those with a white noise model. With an adaptive prediction model and the between-epoch constraint receiver clock model, the simulation results illustrate that the average root mean squares (RMS) values of all the stations are significantly reduced, i.e., by 66.03% from 0.43 to 0.14 ns, by 64.91% from 0.44 to 0.15 ns, by 57.47% from 0.43 to 0.18 ns, and by 51.67% from 0.44 to 0.21 ns for the 10, 30, 60 and 90 min data interruptions, respectively. The stability of all the stations is improved by at least 50%. The improvement increases to 100% for short-term stability. The real results show that the stability of four links is boosted by at least 5%. The model proposed in this paper is more effective in producing short-term stability than long-term stability.

LSWAVE: a MATLAB software for the least-squares wavelet and cross-wavelet analyses

The least-squares wavelet analysis (LSWA) is a robust method of analyzing any type of time/data series without the need for editing and preprocessing of the original series. The LSWA can rigorously analyze any non-stationary and equally/unequally spaced series with an associated covariance matrix that may have trends and/or datum shifts. The least-squares cross-wavelet analysis complements the LSWA in the study of the coherency and phase differences of two series of any type. A MATLAB software package including a graphical user interface is developed for these methods to aid researchers in analyzing pairs of series. The package also includes the least-squares spectral analysis, the antileakage least-squares spectral analysis, and the least-squares cross-spectral analysis to further help researchers study the components of interest in a series. We demonstrate the steps that users need to take for a successful analysis using three examples: two synthetic time series, and a Global Positioning System time series.

Spatiotemporal Filtering and Noise Analysis for Regional GNSS Network in Antarctica Using Independent Component Analysis

The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

Slow slip events in the roots of the San Andreas fault.

Episodic tremor and accompanying slow slip are observed at the down-dip edge of subduction seismogenic zones. While tremors are the seismic signature of this phenomenon, they correspond to a small fraction of the moment released; thus, the associated fault slip can be quantified only by geodetic observations. On continental strike-slip faults, tremors have been observed in the roots of the Parkfield segment of the San Andreas fault. However, associated transient aseismic slip has never been detected. By making use of the timing of transient tremor activity and the dense Parkfield-area global positioning system network, we can detect deep slow slip events (SSEs) at 16-km depth on the Parkfield segment with an average moment equivalent to M w 4.90 ± 0.08. Characterization of transient SSEs below the Parkfield locked asperity, at the transition with the creeping section of the San Andreas fault, provides new constraints on the seismic cycle in this region.

Precursory Analysis of GPS Time Series for Seismic Hazard Assessment

We discuss the use of fractal analysis of geophysical big data as a precursory method for the estimation of current seismic hazard. We begin by analyzing GPS time series of land surface displacements recorded in various regions of the planet exhibiting different degrees of seismic activity. We show how the quantitative measure of chaoticity based on a modified detrended fluctuation analysis (DFA) method can be used to distinguish critical and noncritical regimes of evolution of the Earth’s crust. Having established the reference values for the measure of chaoticity, we then choose California as the case study region and apply modified DFA to over 1200 time series for determining local areas currently exposed to higher seismic hazard. We comprehensively analyze the results obtained and compare them with those produced by conventional risk assessment methods. We demonstrate consistency of our results and yet discover that the suggested approach allows us to quantify two qualitatively different mechanisms involved in the growth of seismic hazards. We propose a hypothesis which relates these mechanisms to the known mechanisms of earthquake generation that evince a growth of collective behavior in the Earth’s crust on the eve of an earthquake. In order to verify the hypothesis, we analyze the same GPS data by an independent statistical method, canonical coherence analysis (CCA), which permits us to quantify the degree of synchronized behavior of constituents of the Earth’s crust. The CCA method confirms our previously obtained estimates, thus supporting fractal analysis of GPS time series as a suitable precursory method for current seismic hazard assessment.

Synchronous LoRa Mesh Network to Monitor Processes in Underground Infrastructure

Collecting precise real-time information on urban drainage system performance is essential to identify, predict, and manage critical loading situations, such as urban flash floods and sewer overflows. Although emerging low-power wireless communication techniques allow efficient data transfers with great above-ground performance, for underground or indoor applications in a large coverage range are difficult to achieve due to physical and topological limitations, particularly in dense urban areas. In this paper, we first discuss the range limitations of the LoRaWAN standard based on a systematic evaluation of a long-term operation of a sensor network monitoring in-sewer process dynamics. Analyses reveal an-on average-five-fold higher data packet loss for sub-surface nodes, which steadily grows with increasing distance to the gateway. Second, we present a novel LPWAN concept based on the LoRaR technology that enhances transmission reliability, efficiency, and flexibility in range-critical situations through meshed multi-hop routing and ensures a precise time-synchronization through optional GPS or DCF77 long-wave time signaling. Third, we illustrate the usefulness of the newly developed concept by evaluating the radio transmission performance for two independent full-scale field tests. Test results show that the synchronous LoRa mesh network approach clearly outperforms the standard LoRaWAN technique with regard to the reliability of packet delivery when transmitting from range-critical locations. Hence, the approach is expected to generally ease data collection from difficult-to-access locations such as underground areas.

SA102GENETIC AND CLINICAL ANALYSES OF PSYCHOSIS SPECTRUM IN A LARGE MULTI-ETHNIC YOUTH COHORT (N>8,000) REVEAL SIGNIFICANT LINK WITH ADHD

The power system at present is a complex network that accommodates several applications with diverse requirements. For the stable operation of this network the wide area synchrophasor measurement system (WASMS) has been instituted. The WASMS consists of several measuring devices called the phasor measurement units (PMUs), for collecting the information pertaining to the subtleties of the power grid, which is in the form of time-synchronized phasors measured at various spatial locations in the power system. The synchronization in the time domain is achieved by using the global positioning system timing signals. These synchronized phasors are communicated to the phasor data concentrator where the data is analyzed for detection and control of the power system abnormalities. Reliable performance of the WASMS is quintessential for effective monitoring and subsequent decision-making. Risk assessment of this system prior to its actual deployment would help the power system engineers in the design of fault-tolerant monitoring system. Risk quantifies the severity of failure, and uncertainties in the system parameters pose a major impediment for the accurate determination of the system’s risk. Therefore uncertainty analysis has to be included in the risk assessment framework for obtaining a better estimate. The aim of this book chapter is to present a Monte Carlo simulation (MCS)–based uncertainty analysis and risk assessment of the WASMS. MCS can model simultaneous component failures and is widely used for estimating the failure rates of complex systems. It can also model complex interconnections that are neither series nor parallel. MCS models are presented for the PMU and its communication system. With the uncertainty analysis included in the risk assessment, the WASMS is optimally designed such that the risk is minimized without compromising the system’s observability. Case study results of an Indian power system are presented to elucidate the proposed approach.

Downscaling Vertical GPS Observations to Derive Watershed‐Scale Hydrologic Loading in the Northern Rockies

AbstractGPS time series of vertical displacement include the elastic response of the Earth to a combination of regional and local loading signals arising from hydrologic mass transfer. The regional loading, controlled by seasonal, synoptic precipitation patterns, dominates the displacement of individual stations and is highly correlated among stations with separation distances from 10 to 300 km. The local loading, controlled by small‐scale precipitation and storage variability, has much shorter correlation lengths of <30 km. We develop a new method to separate the regional and local contributions using common mode analysis and show that GPS is capable of measuring the local hydrologic load changes at watershed scales of tens of kilometers. Using this methodology, GPS‐measured displacement provides an integrated measurement of hydrologic load at a spatial scale between the existing long‐wavelength resolution of the Gravity Recovery and Climate Experiment and point measurement resolution of a precipitation station. Thus, GPS time series record critical observations for monitoring integrated hydrologic budgets at scales useful for water management and assessment of the hydro‐ecological response to climate change.

Comprehensive Analysis of the Effects of Common Mode Error on the Position Time Series of a Regional GPS Network

This study uses the daily horizontal and vertical Global Positioning System (GPS) position time series of 41 regionally distributed GPS stations in Southern California covering the period of January 2011 to January 2017 to extract the common mode error (CME) in east-north-up (ENU) components with the principal component analysis (PCA) method. We assume the first component with common spatial responses is CME. Firstly, we focus on the effects of CME on the root mean square (RMS) of the GPS time series. The results indicate that after being filtered, CMEs can reduce the average RMS values by 42.9, 39.1 and 28.8% for ENU components, respectively. Secondly, we investigate the changes in noise type before and after spatio-temporal filtration. The outcome reveals that noise model of the GPS position time series can be mainly described by power law noise (PL) plus white noise (WH). After CME correction, 32, 12 and 13% of the stations’ noise types are altered for ENU components, respectively. Thirdly, we confirm that stations whose noise properties can be best described by PL + WH, PL + WH and FL + WH or RW (Random walk noise) + FL + WH show no obvious differences in velocity and velocity uncertainty. Stations whose noise properties are described by RW + FL + WH, PL + WH and RW + FL + WH show significant differences in velocity and velocity uncertainty. Therefore, for all GPS stations, excluding the ones whose noise properties are described by the RW + FL + WH model, we estimate the noise parameters and velocity before and after spatio-temporal filtration by fitting a PL + WH model. According to our findings, the PCA method does not appear to be efficient for horizontal components. The CME is superimposed on the stochastic process realization for the vertical components. The impact of CME on vertical velocity estimation cannot be ignored, and eliminating CME enhances the credibility of the determined velocity field. Finally, we discuss the correlation between CME and mass loadings. The results of the Pearson correlation analysis and wavelet coherence analysis indicate that CME cannot be interpreted by mass loadings in a regional GPS network and that it may be related to GPS technique errors.

Short Baseline Observations at Geodetic Observatory Wettzell

The Geodetic Observatory Wettzell (GOW), jointly operated by the Federal Agency for Cartography and Geodesy (BKG), Germany and the Technical University of Munich, Germany is equipped with three radio telescopes for Very Long Baseline Interferometry (VLBI). Correlation capability is primarily designed for relative positioning of the three Wettzell radio telescopes i.e., to derive the local ties between the three telescopes from VLBI raw data in addition to the conventional terrestrial surveys. A computing cluster forming the GO Wettzell Local Correlator (GOWL) was installed in 2017 as well as the Distributed FX (DiFX) software correlation package and the Haystack Observatory Postprocessing System (HOPS) for fringe fitting and postprocessing of the output. Data pre-processing includes ambiguity resolution (if necessary) as well as the generation of the geodetic database and NGS card files with υ Solve. The final analysis is either carried out with local processing software (LEVIKA short baseline analysis) or with the Vienna VLBI and Satellite (VieVS) software. We will present an overview of the scheduling, correlation and analysis capabilities at GOW and results obtained so. The dataset includes auxiliary files (schedule and log files) which contain information about the participating antenna, observed sources, clock offset between formatter and GPS time, cable delay, meteorological parameters (temperature, barometric pressure, and relative humidity) and ASCII files created after fringe fitting and final analysis. The published dataset can be used by the researchers and scientists to further explore short baseline interferometry.

Measurement of terrestrial radiation level in a neotectonic fault system in Trinidad

Present study area, which is located in the Central Range Fault (CRF) in the island of Trinidad, can provide solution to that problem as this strike-slip fault system is reported to be active in present time (during Holocene, most current geological epoch) with a movement of geodetic rate of 12 ± 3 mm/yr. Most recent work, with improved GPS time series analysis indicated that as creeping fault rather than locked fault. Moreover, the selected fault system is accompanied by mud volcanoes, which can amplify the level of radiation activity by slight but significant anomalies as described by some researchers from other areas. Furthermore, being one of the first environmental radiation study in the Caribbean Islands, this study is significant. With the aim to achieve a continuous monitoring of radiological data to assess the activity along CRF for possible earthquake forecasting, objectives of this preliminary investigation are to delineate CRFZ using radiation survey and to investigate the potential of using this survey to detect tectonic activities in this zone. As a pilot study, radiation measurements were carried out with the help of a portable Geiger Muller counter in 52 locations along the 25 km long section of CRF between Naved Dam and Pointe-a-Pierre. Radiation measurements were taken both at 1 m level above the surface and at nearly 1 m below the ground. Results from this pilot study showed that lithology was not the lone contributor to terrestrial radiation levels, as CRFZ and associated fissure plane have good control also (∼3–5 times higher than the average background level). Although present study was not able to indicate the possibility of earthquakes in recent times (as previously stated), however, elevated radiation values in the vicinity of CRF, associated fissures (possibly interconnected fractures) and active mud volcanoes indicate possible activity in the study area. However, detail and periodic radiological studies are required before concluding that CRF is a creep fault rather than a locked fault.

GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station

Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These files have not been fully analysed until now. This study reports for the first time on the consistent and homogeneous data processing and analysis of the 15-year long time series. Daily coordinates are obtained using Precise Point Positioning (PPP) processing based on two approaches. The first approach is based on the Kalman filter and uses the RTKLIB open source library to produce daily solutions by unconventionally running the filter in the forward and backward direction. The second approach uses APPS web service and is based on GIPSY scientific processing engine. The two approaches show an excellent agreement with less than 3 mm rms error horizontally and 6 mm rms error vertically. The derived position time series is analysed in terms of trend, periodicity and noise characteristics. The noise of the time series was found to be power-law noise model with spectral index closer to flicker noise. In addition, several periodic signals were found at 5, 14, 183 and 362 days. Furthermore, most of the horizontal movement was found to be in the North direction at a rate of 11.23 ± 0.09 mm/y, whereas the rate in the East direction was estimated to be 1.46 ± 0.05 mm/y. Lastly, the 15-year long time series revealed a movement upwards at a rate of 0.79 ± 0.35 mm/y. Despite being an unattended station, Aboa provides one of the most continuous and longest GPS time series in Antarctica. Therefore, we believe that this research increases the awareness of local geophysical phenomena in a less reported area of the Antarctic continent.

STOCHASTIC MODEL OF THE BRAZILIAN GPS NETWORK COORDINATES TIME SERIES

Abstract It is well known that daily estimates of GPS coordinates are highly temporally correlated and that the knowledge and understanding of this correlation allows to establish more realistic uncertainties of the parameters estimated from the data. Despite this, there are currently no studies related to the analysis and calculation of the noise sources in geodetic time series in Brazil. In this context, this paper focuses on the investigation of the stochastic properties of a total of 486 coordinates time series from 159 GPS stations belonging to the Brazilian Network for Continuous Monitoring of GNSS (RBMC) using the maximum likelihood estimation approach. To reliably describe the GPS time series, we evaluate 4 possible stochastic models as models of each time series: 3 models with integer spectral indices (white noise, flicker plus white noise and random-walk plus white noise model) and 1 with fractional spectral index (fractional power-law plus white noise). By comparing the calculated noise content values for each model, it is possible to demonstrate a stepwise increase of the noise content, being the combination of a fractional power-law process and white noise process, the model with smaller values and the combination of random walk process with white noise process, the model with greater values. The analysis of the spatial distribution of the noise values of the processes allow demonstrate that the GPS sites with the highest accumulated noise values, coincide with sites located in coastal zones and river basins and that their stochastic properties can be aliased by the occurrence of different physical signals typical of this type of zones, as the case of the hydrological loading effect.

Offset detection in GPS position time series using multivariate analysis

Proper analysis and subsequent interpretation of GPS position time series is an important issue in many geodetic and geophysical applications. The GPS position time series can possibly be contaminated by some abrupt changes, called offsets, which can be well compensated for in the functional model. An appropriate offset detection method requires proper specification of both functional and stochastic models of the series. Ignoring colored noise will degrade the performance of the offset detection algorithm. We first introduce the univariate analysis to identify possible offsets in a single time series. To enhance the detection ability, we then introduce the multivariate analysis, which considers the three coordinate components, north, east and up, simultaneously. To test the performance of the proposed algorithm, we use synthetic daily time series of three coordinate components emulating real GPS time series. They consist of a linear trend, seasonal periodic signals, offsets and white plus colored noise. The average detection power on individual components, either north, east or up, are 32.3 and 47.2% for the cases of white noise only and white plus flicker noise, respectively. The detection power of the multivariate analysis increases to 70.8 and 87.1% for the above two cases. This indicates that ignoring flicker noise, existing in the structure of the time series, leads to lower offset detection performance. It also indicates that the multivariate analysis is more efficient than the univariate analysis for offset detection in the sense that the three coordinate component time series are simultaneously used in the offset detection procedure.

Transient crustal deformation from karst aquifers hydrology in the Apennines (Italy)

The increasing accuracy and spatiotemporal resolution of space geodetic techniques have positively impacted the study of shallow crustal deformation in response to the redistribution of water masses. Measurable deformations have been documented in areas where snow and water variability is large and persists over sufficiently long periods. Here we analyze GPS time series and hydrological data from the Central-Southern Apennines, a tectonically-active region hosting large karst aquifers. We document the occurrence of regional-scale horizontal and vertical transient deformation that is clearly correlated to seasonal and multiyear hydrological variability. These transient signals, which are most strongly observed at GPS sites surrounding the main karst aquifers, modulate long term tectonic deformation. Our results suggest that the karst aquifers in this region experience alternating periods of expansion and contraction in response to increasing/decreasing precipitation and, consequently, higher/lower hydraulic head in the aquifers. Thanks to the availability of a dense continuous GPS network and complementary hydrological datasets, we are able to verify the processes causing the observed deformation. We model the shallow crust in the region as a continuous anelastic solid and use Green's functions for finite strain cuboid sources to estimate the strain rate distribution associated with the GPS observations. We use the Mw 6.1 L'Aquila earthquake, which struck the Central Apennines in 2009 and whose effects are evident in geodetic data, to document the potential effects of moderate earthquakes on karst aquifers and to demonstrate the importance of correctly discerning tectonic from nontectonic signals in geodetic time series. Enhanced understanding of the karst aquifers behavior is of primary interest for improved management of this vital water resource and for a better understanding of the possible interactions between groundwater content and pore pressure variations in the crust and seismicity.