Matteo Picchiani

Did the September 2010 (Darfield) earthquake trigger the February 2011 (Christchurch) event

We have investigated the possible cause-and-effect relationship due to stress transfer between two earthquakes that occurred near Christchurch, New Zealand, in September 2010 and in February 2011. The Mw 7.1 Darfield (Canterbury) event took place along a previously unrecognized fault. The Mw 6.3 Christchurch earthquake, generated by a thrust fault, occurred approximately five months later, 6 km south-east of Christchurchs city center. We have first measured the surface displacement field to retrieve the geometries of the two seismic sources and the slip distribution. In order to assess whether the first earthquake increased the likelihood of occurrence of a second earthquake, we compute the Coulomb Failure Function (CFF). We find that the maximum CFF increase over the second fault plane is reached exactly around the hypocenter of the second earthquake. In this respect, we may conclude that the Darfield earthquake contributed to promote the rupture of the Christchurch fault.

The October 23, 2011, Van (Turkey) earthquake and its relationship with neighbouring structures

The present work reports the analysis of a possible relationship due to stress transfer between the two earthquakes that hit the province of Van, Eastern Turkey, on October 23, 2011 (Mw = 7.2) and on November 9, 2011 (Mw = 5.6). The surface displacement field of the mainshock has been obtained through a combined data set made up of differential interferograms from COSMO-SkyMed and ENVISAT satellites, integrated with continuous GPS recordings from the Turkish TUSAGA-AKTIF network. This allowed us to retrieve the geometry and the slip distribution of the seismic source and to compute the Coulomb Failure Function (CFF) variation on the aftershock plane, in order to assess a possible causal relationship between the two events. Our results show that the November 9 earthquake could have been triggered by the October 23 shock, with transferred stress values largely exceeding 1 bar.

Features extraction from SAR interferograms for tectonic applications

In this article, a new technique for features extraction from SAR interferograms is presented. The technique combines the properties of auto-associative neural networks with those of more traditional approaches such as discrete Fourier transform or discrete wavelet transform. The feature extraction is chained to another neural module performing the estimation of the fault parameters characterizing a seismic event. The whole procedure has been validated with the experimental data acquired for the analysis of the dramatic L’Aquila earthquake which occurred in Italy in 2009. The results show the effectiveness of the approach either in terms of dimensionality reduction or in terms retrieval capabilities.

Volcanic ash retrieval from IR multispectral measurements by means of neural networks: An analysis of the Eyjafjallajokull eruption

The great eruption of the Icelandic Eyjafjallajokull volcano that occurred from the 14th of April to the 23rd of May 2010 injected large and dense ash clouds into the atmosphere, causing major international air traffic disruption worldwide.

Contextual descriptors and neural networks for scene analysis in VHR SAR images

The development of SAR technology during the last decade has made it possible to collect a huge amount of data over many regions of the world. In particular, the availability of SAR images from different sensors, with metric or sub-metric spatial resolution, offers novel opportunities in different fields as land cover, urban monitoring, soil consumption etc. On the other hand, automatic approaches become crucial for the exploitation of such a huge amount of information. In such a scenario, especially if single polarization images are considered, the main issue is to select appropriate contextual descriptors, since the backscattering coefficient of a single pixel may not be sufficient to classify an object on the scene. In this paper a comparison among three different approaches for contextual features definition is presented so as to design optimum procedures for VHR SAR scene understanding. The first approach is based on Gray Level Co- Occurrence Matrix since it is widely accepted and several studies have used it for land cover classification with SAR data. The second approach is based on the Fourier spectra and it has been already proposed with positive results for this kind of problems, the third one is based on Auto-associative Neural Networks which have been already proven effective for features extraction from polarimetric SAR images. The three methods are evaluated in terms of the accuracy of the classified scene when the features extracted using each method are considered as input to a neural network classificator and applied on different Cosmo-SkyMed spotlight products.

Automatic monitoring of ash and meteorological clouds by Neural Networks

Volcanic eruptions affect at different levels the population and economy of interested areas. Moreover, volcanic ash detection represents a key issue for aviation safety due to the harming effects on aircraft. For these reasons, an accurate and fast analysis of the data is needed to monitor the phenomenas evolution and to manage the risk mitigation phase. In this scenario, the introduction of an inversion approach based on Neural Networks (NNs) has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the application of brightness temperature difference approach. In this work we show that NNs algorithms are suitable for an accurate mapping of ash cloud on Moderate Resolution Imaging Spectroradiometer (MODIS) images in a very cloudy scenario as the ones of 2010 Eyjafjallajökull and 2011 Grimsvötn eruptions.

A neural network architecture combining VHR SAR and multispectral data for precision farming in viticulture

Concurrent availability of VHR (Very High Resolution) images at both optical and microwave bands opens new challenges in many applicative scenarios of Earth Observation (EO). In particular this is true for precision farming activities where the retrieval on the metric scale of biophysical parameters and of information regarding vegetation spatial distributions can be very effective in supporting farmers during the production cycles. However, the inversion problem giving the value of the desired variable from the measured electromagnetic quantities (the image data) can be very complex and the nonlinear relationships involved need to be handled by suitable algorithms. In this paper a complete processing scheme providing quantities of interest for precision viticulture from data provided by WorldView-2 (WV2) and COSMOSkyMed (CSK) space platforms is presented. Once the appropriate season time was selected, the satellite data have been acquired over the test area within a limited time window and concurrently with the collection of the groundtruth. The workflow, besides adequate pre-processing steps, includes two neural networks (NN) modules, one is dedicated to the extraction of a restricted number of nonlinear components from the WV2 data, the other one to the actual inversion problem. The obtained results seem to be satisfactory with respect to the requirements provided by the users.

The ESA learneo! Project for stimulating Earth Observation Education

LeanEO! is a 2-year Earth Observation education project funded by the European Space Agency (ESA) and developed by different European Institutions. Its main aim is to increase the understanding and knowledge of satellite data obtained from ESA missions and demonstrate how these can be used when faced with environmental problems in the real world. The project has developed hands-on training resources for use primarily (but not exclusively) by teachers and students at upper high school to university level. Each lesson comes complete with data, analysis tools and exhaustive background information necessary for the completion of the suggested activities and provides answers to the various study questions. Model answers are supplied for users working on their own or with limited specialist support. In this paper the aims and the opportunities provided by the project will be described in detail.

Retrieval of fault parameters of October 23, 2011 Eastern Turkey eartquake obtained by Neural Network

We have analysed the seismic source of the active fault generated Van Mw=7.1 earthquake occurred in Eastern Turkey the 23rd October 2011. To this aim the surface displacement field has been measured applying SAR Interferometry (InSAR) technique to the available dataset of coseismic COSMO-SkyMed image pairs. The seismic source model has been obtained by the use of a data inversion procedure based on the concurrent application of InSAR techniques and Neural Networks. The proposed approach elaborates the information on the coseismic deformation pattern stemming from available differential interferograms. The interferogram is the expression of the active fault at depth, thus its shape, size and its features somehow refer to the geometry and slip of the fault generating the seism. A Neural Network has been trained to recognize some fault parameters (Length, Width, Strike, Dip, Depth) from the unwrapped interferogram. The retrieval exercise consists in estimating these parameters from the coseismic interferogram exploiting Neural Networks.

Associative memory techniques for the exploitation of remote sensing data in the monitoring of volcanic events

The possibility offered by space-based sensors represents an irreplaceable resource for monitoring in near real time the eruption activities. The high revisit time of sensor like MODIS, seems to be the most effective way to mitigate the aviation hazard imaging the phenomenon evolution. In this work we propose a neural networks based approach to the volcanic ash mass retrieval. In comparison with the techniques based on radiative transfer models, the proposed algorithm has shown similar accuracy and faster computation. This issue can be of real interest to address the problems inherent the volcanic activity in short time. A set of MODIS images collected during the Eyjafjallajokull eruption, occurred from the 14th of April to the 23rd of May 2010, has been used to analyze the performance variations due to different selection of the algorithm inputs, i.e. the MODIS channels from visible to thermal infrared electromagnetic spectrum. The best wavelength sets for the retrieval of the ash mass, optical thickness and effective radius have been identified by means of neural network pruning algorithm.