Demitris Paradissis

Geodetic strain of Greece in the interval 1892–1992

A first-order triangulation of Greece was carried out in the 1890s. Reoccupation, using Global Positioning System receivers, of 46 of the 93 original markers yielded estimates of the deformation of the region over the intervening interval. Broad regions have similar geodetic strain over the 100-year time span. Strain north of the Gulf of Korinthos is predominantly north-south extension, though with a significant east-west component. The central Peloponnisos is relatively stable, whereas the gulfs of the southern Peloponnisos are all characterized by uniaxial east-west extension. The seismic expression of strain for the entire region, calculated from the seismic moment tensors of earthquakes of M S ≥ 5.8 during the past 100 years, accounts for only 20-50% of the geodetically determined strain. At a scale of 50-100 km, the fraction of the strain that is expressed seismically varies much more than this range. In particular, whereas seismic strain in the eastern Gulf of Korinthos over the past 100 years is commensurate with the geodetic strain, there is rapid extension across the western Gulf of Korinthos (∼0.3 μstrain yr -1 ), with negligible seismic strain for the 100 year period prior to 1992. The Egion earthquake of June 1995 in the western Gulf of Korinthos released only a small proportion (≤20%) of the elastic strain that had accumulated in that region. The observed distribution of displacements can be explained by the relative rotation of two plates with a broad accommodation zone between them, but it is equally consistent with the deformation that would be expected of a sheet of fluid moving toward a low-pressure boundary at the Hellenic Trench. A simple calculation implies that if the region does behave as a fluid, then its effective viscosity is ∼10 22 ∼10 23 Pa s. Such viscosities are consistent with the deformation of a lithosphere obeying a rheological law similar to that obtained for olivine in the laboratory.

THE STRAIN FIELD IN NORTHWESTERN GREECE AND THE IONIAN ISLANDS : RESULTS INFERRED FROM GPS MEASUREMENTS

Abstract Recent crustal movements detected by the analysis of repeated satellite geodetic measurements reflect the ongoing geodynamic processes in the Alpine-Mediterranean area. Superimposed on the large-scale counterclockwise rotation of the African plate, complex dynamic processes are affecting the lithospheric fragments between the African and Eurasian plates. Key features to better understand the driving forces and associated seismic activity in the Africa/Eurasia collision zone are the Calabrian and Hellenic arcs. In this paper geodynamic investigations along the West Hellenic arc are discussed. They are based on two epochs (1989 and 1993) of satellite geodetic measurements carried out using the US Global Positioning System (GPS). The results are presented in terms of relative displacements and strain rates. Within the time span of 4 years southwestern Greece has moved to the southwest relative to southeastern Italy by an average of 120 mm, increasing from 80 mm at Lefkada, in the center of the Ionian Islands, to 160 mm at the Peloponnesus. The maximum strain rate is 0.18 μstrain/a located in the vicinity of Lefkada, where anomalously high earthquake activity is observed. The data provide strong evidence for dextral strike-slip motion on the order of 25 mm/a along the Kephalonia Fault Zone (KFZ). The deformation field of the KFZ is interpreted as a transition zone between the kinematics of the Apulian platform and the West Hellenic fold and thrust belts.

Geodetic investigation of the 13 May 1995 Kozani‐Grevena (Greece) Earthquake

The Ms=6.6 13 May 1995 Kozani-Grevena earthquake struck a region of low historical seismic activity which includes a 10-year-old triangulation network in northern Greece. After the earthquake, monuments from this network were occupied with GPS to measure co-seismic displacements. Inversion of the co-seismic displacement field to yield a source mechanism is achieved by use of a hybrid simplex-Monte-Carlo method which requires no a priori constraints. The model focal mechanism agrees well with the global CMT solution and locally observed aftershocks, but implies a significantly higher scalar moment than do seismological or SAR interferometry studies, and has a longer fault length than the model based on SAR interferometry.

Mapping inflation at Santorini volcano, Greece, using GPS and InSAR

[1]xa0Recent studies have indicated that for the first time since 1950, intense geophysical activity is occurring at the Santorini volcano. Here, we present and discuss the surface deformation associated with this activity, spanning from January 2011 to February 2012. Analysis of satellite interferometry data was performed using two well-established techniques, namely, Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), producing dense line-of-sight (LOS) ground deformation maps. The displacement field was compared with GPS observations from 10 continuous sites installed on Santorini. Results show a clear and large inflation signal, up to 150u2009mm/yr in the LOS direction, with a radial pattern outward from the center of the caldera. We model the deformation inferred from GPS and InSAR using a Mogi source located north of the Nea Kameni island, at a depth between 3.3u2009km and 6.3u2009km and with a volume change rate in the range of 12 million m3 to 24 million m3 per year. The latest InSAR and GPS data suggest that the intense geophysical activity has started to diminish since the end of February 2012.

Adaptive Non-Linear Modeling for Ionospheric Disturbances Behavior Estimation on Spaceborne Synthetic Aperture Radar Interferometry

Estimation of the ionospheric disturbances behavior is very important in many applications, including efficient Synthetic Aperture Radar (SAR) signal processing through accurate noise modeling and removal, monitoring of environmental evolution and geodynamics SAR Interferometry (InSAR) purposes. Modeling ionospheric disturbances behavior is a challenging research issue which involves many non-linearities, dynamics and external factors. In this paper, we propose a dynamic, recursive highly non-linear forecasting model regarding the ionospheric component of the spaceborne InSAR technique. In particular, we introduce a framework which takes into consideration the error between the predicted and the actual data and in the sequel adapt a highly non-linear model in a way to optimize prediction accuracy. In this way, we face the problems arising from the traditional approaches which try to pre-compute the noisy effects of the wave propagation through ionosphere on spaceborne SAR images. The model exploits concepts from functional analysis and represents an unknown non-linear function using a series of known functional components, which are then used for ionospheric forecasting. Emphasis will be given in the computational complexity of the model so that the forecasting will be accomplished in real time context which can be applied in Dynamic Synthetic Aperture Radar Interferometry (DInSAR) technique. The model has been tested with ionospheric noise derived from real interferograms produced by earthquakes occurred in Greece the last fifteen years. Specifically, using this adaptive non-linear modeling we extract the noise due to the ionospheric propagation from comparatively processed interferograms, gathered from different highly seismicity areas in Greece. Additionally, we compare the observed Total Electron Content (TEC) during ionospheric disturbances, using the ionospheric station at the National Observatory of Athens and the main Global Position System (GPS) Station at Dionysos Satellite Observatory (DSO) of the National Technical University of Athens (NTUA), Greece, with the ionospheric TEC derived from the non-linear adaptive modeling.

An Echo State Network for Ionospheric Disturbances Behavior Modeling on Spaceborne Interferometric Synthetic Aperture Radar

Modeling the ionospheric disturbances behavior is of great importance in many SpaceGeodetic applications, including robust Spaceborne Synthetic Aperture Radar (SAR) signal processing through accurate noise modeling and removal, 3D-monitoring of land and sea surface and geodynamics SAR Interferometry (InSAR) purposes. Estimating ionospheric disturbances behavior is a challenging research issue which involves many non-linearities, dynamics and external factors. In this paper, we propose an Echo State Network (ESN) for Modeling Ionospheric Disturbances Behavior. The echo state network (ESN) is a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are randomly assigned and are fixed. The weights of output neurons can be learned so that the network can (re)produce specific temporal patterns. The representation is accomplished through network parameters. Thus, we come up against the problems arising from the traditional approaches which try to precompute the noisy effects of the wave propagation through ionosphere on Spaceborne SAR images. The main advantage of the ESN is that although its behavior is non-linear, the error function is thus quadratic with respect to the parameter vector and can be differentiated easily to a linear system. The model has been validated with ionospheric noise derived from real interferograms produced by earthquakes occurred in Greece the last ten years, during various ionospheric disturbances. By using the ESN for Modeling Ionospheric Disturbances Behavior, we extract the noise due to the ionospheric propagation from various comparatively processed interferograms, gathered from different highly seismicity areas in Greece. In addition, we compare the observed Total Electron Content (TEC) during severe ionospheric disturbances, using the local Ionospheric Station at the National Observatory of Athens and the main Global Position System (GPS) Station at Dionysos Satellite Observatory (DSO) of the National Technical University of Athens (NTUA), Greece, with the ionospheric TEC derived from the proposed ESN.

An Interdisciplinary Approach to Studying Seismic Hazard Throughout Greece.

This paper describes and reviews the progress of a three year (11/97–11/00) European Commission FP4 (Climate and Natural Hazards) funded project entitled GPS Seismic Hazard in Greece (SING), A major international interdisciplinary consortium is investigating and comparing strain derived using both geodetic and seismic methods.

GEODETIC MEASUREMENTS IN THE AEGEAN SEA REGION FOR THE DETECTION OFCRUSTAL DEFORMATION

Greece and the Aegean Sea form part of one of the most rapidly deforming parts of the Earths surface, and are characterized by a high level of intra-plate seismic activity in comparison to neighboring regions. AEGEANET is a geodetic network that we have established in order to measure consistently the geodetic strain in the broader Aegean Sea region, including parts of the Greek mainland and spanning several areas of known fault systems. Our measurements so far span approximately 4-, and 42-year periods up to 1997 using a combination of old triangulation/trilaterationderived coordinates and recent, repeated GPS observations at various subsets of the stations of this network. The observed displacements reflect the present day and long-term tectonic deformation of the region, showing more than one metre of northsouth extension across the network. The crust in this region appears to contain a few slowly deforming blocks separated by more rapidly deforming zones. This conclusion is supported by the velocity and strain fields that we have estimated for six sub-regions, which provide a more detailed view of the crustal deformation in this region.

Deep Convolutional Neural Networks for Modeling Patterns of Spaceborne Interferometric SAR Systems Signals

Spaceborne radar systems (SAR) are nowadays considered as very beneficial schemes towards a successful implementation of many engineering applications such as surveillance, maritime traffic management, reconnaissance, etc. Among others, modeling and prediction of ionospheric disturbances are considered as crucial towards successful SAR-based engineering applications. However, modeling ionospheric disturbances behavior is a very challenging research task due to high non-linearities involved in the mature of the data and their dynamics. For this reason, previous research efforts have been concentrated on the use of adaptable neural networks models and echo state machines that enable the effective modeling and prediction of the ionospheric disturbances. In this paper, we investigate the use of deep machine learning algorithms and particularly of Deep Convolutional Neural Networks (CNNs). Deep machine learning paradigm has been introduced in the last decade as a new advanced tool for modeling complex dynamic processes. Deep learning better emulates human brain operation, and makes effective processing of large amounts of unlabeled training data for extracting structures and internal representations from the raw sensory inputs. In this paper, CNNs are used to identify patterns in Spaceborne Interferometric SAR (InSAR) systems signals. CNNs can be controlled by varying their depth and breadth, and they also make strong and mostly correct assumptions about the nature of data taking into account local dependencies and varying statistics. SAR systems signals derived from real interferograms produced by earthquakes occurred in Greece the last fifteen years from the Dionysos Satellite Observatory of the National Technical University of Athens (NTUA) in Greece and objective criteria, such as false positives and negatives, are used to evaluate the efficiency of the proposed schemes.