We investigate a large geodetic data set of interferometric synthetic aperture radar (InSAR) and GPS measurements to determine the source parameters for the three main shocks of the 2016 Central Italy earthquake sequence on 24 August and 26 and 30 October (M_w 6.1, 5.9, and 6.5, respectively). Our preferred model is consistent with the activation of four main coseismic asperities belonging to the SW dipping normal fault system associated with the Mount Gorzano‐Mount Vettore‐Mount Bove alignment. Additional slip, equivalent to a M_w ~ 6.1–6.2 earthquake, on a secondary (1) NE dipping antithetic fault and/or (2) on a WNW dipping low‐angle fault in the hanging wall of the main system is required to better reproduce the complex deformation pattern associated with the greatest seismic event (the M_w 6.5 earthquake). The recognition of ancillary faults involved in the sequence suggests a complex interaction in the activated crustal volume between the main normal faults and the secondary structures and a partitioning of strain release.

International audienceA systematic multi-parameter and multi-platform approach to study the slow process of earthquake preparation is fundamental to gain some insights on this complex phenomenon. In particular, an important contribution is the integrated analysis between ground geophysical data and satellite data. In this paper we review some of the more recent results and suggest the next directions of this kind of research. Our intention is not to detect a particular precursor but to understand the physics underlying the various observations and to establish a reliable physical model of the preparation phase before an impending earthquake. In this way, future investigation will search for suitable fore-patterns, which the physical model of multi-layers coupling predicts and characterizes by quasi-synchronism in time and geo-consistency in space. We also present alternative explanations for some anomalies which are not actually related to earthquakes, rather to other natural or anthropic processes

During the past three decades, the Dead Sea (DS) water level has dropped at an average rate of ~1 m/year, resulting in the formation of thousands of sinkholes along its coastline that severely affect the economy and infrastructure of the region. The sinkholes are associated with gradual land subsidence, preceding their collapse by periods ranging from a few days to about five years. We present the results of over six years of systematic high temporal and spatial resolution interferometric synthetic aperture radar (InSAR) observations, incorporated with and refined by detailed Light Detection and Ranging (LiDAR) measurements. The combined data enable the utilization of interferometric pairs with a wide range of spatial baselines to detect minute precursory subsidence before the catastrophic collapse of the sinkholes and to map zones susceptible to future sinkhole formation. We present here four case studies that illustrate the timelines and effectiveness of our methodology as well as its limitations and complementary methodologies used for sinkhole monitoring and hazard assessment. Today, InSAR-derived subsidence maps have become fundamental for sinkhole early warning and mitigation along the DS coast in Israel and are incorporated in all sinkhole potential maps which are mandatory for the planning and licensing of new infrastructure.

Sinkholes commonly form by subsurface dissolution cavities that collapse after the overlying layers become mechanically unsupported. Sinkholes along the Dead Sea shorelines are preceded by, associated with, and followed by gradual surface subsidence that accompanies the cavities' growth. We exploit satellite radar interferometry (interferometric synthetic aperture radar) to resolve temporal and spatial relationships between gradual subsidence and sinkhole collapse. The geometry of the deflating cavity roof is determined by elastic inverse modeling of the surface displacements. A Coulomb failure stress criterion is applied to calculate the stress field induced by the deflating cavity at the ground surface. We find that the induced stress field favors generation of sinkholes at the perimeters of the subsiding areas rather than at their centers, in agreement with field observations, providing important information for sinkhole hazard assessment. Further, our analysis suggests that short-term deformation in consolidated gravel layers at shallow depths could be approximated by simple elastic modeling.

On 26 January 2014 at 13:55 UTC, an Mw 6.0 earthquake struck the island of Cephalonia, Greece, followed five hours later by an Mw 5.3 aftershock and by an Mw 5.9 event on 3 February 2014 at 03:08 UTC (National Observatory of Athens, Institute of Geodynamics [NOA-GI]). The epicenter of theMw 6.0 event was relocated 2 km east of the town of Lixouri, and that of the Mw 5.9 event at the tip of the Gulf of Argostoli, in the northern part of the Paliki peninsula (Fig. 1; Karastathis et al., 2014; Papadopoulos et al., 2014). Extensive structural damage and widespread environmental effects were induced throughout the Paliki peninsula and along the eastern coast of the Gulf of Argostoli (Valkaniotis et al., 2014). Quays, sidewalks, and piers were damaged in the waterfront areas of the towns of Lixouri and Argostoli, the island capital, and liquefactions, road failures, rock falls, and small landslides were observed.Most of the latter effects took place in the aftermath of the 26 January 2014 event and were reactivated one week later by the 3 February earthquake. In this paper, we derive the 3D surface deformation field associated with the 3 February 2014Mw 5.9 event based on the application of three different measurement techniques to Synthetic Aperture Radar (SAR) acquisitions from the Italian Space Agency (Agenzia Spaziale Italiana [ASI]) COSMO– SkyMed satellites and the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) Tan- DEM-X satellite. We then model the main seismic sources and their associated slip distributions, comparing the latter with improved hypocenter relocations, which allows us to speculate on the possible rupture mechanism. Finally, we discuss the contribution of our findings to the characterization of the seismogenic sources of this region

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.