USE OF NEURAL NETWORKS AND SAR INTERFEROMETRY FOR THE AUTOMATIC RETRIEVAL OF TECTONIC PARAMETERS

ABSTRACT From its first application in 1992 to detect the displacement field originated from the Landers earthquake In the recent years InSAR capabilities, together with classic seismological and geophysical data such as strong motion records and GPS, have also been used by geophysicists for the assessment of normal fault models Neural networks have already been recognized as being a powerful tool for inversion procedure in remote sensing applications In this study we propose an innovative approach for the seismic source classification and the fault parameter quantitative retrieval. The originality of such an approach consists in exploiting at the same time the capabilities of neural networks and of InSAR measurements in the described context. The network is trained by using a data set generated by the RNGCHN software and then tested on real measured data. The input of the net consists of a set of features calculated from the interferometric image while the output vector contains the parameters characterizing the fault. Two problems have been analysed. The first one is the identification of the seismic source mechanism. The second one addresses the fault plane parameters estimation. The paper illustrates such a methodology and its validation on a set of experimental data. The experimental set up was composed by three case studies covering different types of faults: normal, strike slip, reverse

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 Christchurch's 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

Sea-Ice Cloud Screening for Copernicus Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR)

Bayesian approach to classify SLSTR pixels over polar regions in clear ocean, clouds and sea-ice is presented. The approach is based on Look-Up-Tables estimating the probability distribution function (PDF) for a pixel, given a set of measured values for selected variables. PDF’s have been generated by analysing archived MODIS AQUA and TERRA products. MODIS data have been selected because of the long available time series, the quality of cloud mask products and possibility to simulate the SLSTR observation including the dual view capability. A first set of candidate input variables in the PDF’s, defined based on review relevant literature, has been optimized both in terms of classification skills and computational efficiency. Different combinations of variables have been considered together with ancillary data SST and observation geometry to get the final set of variables to be used for classification. The optimization process based on: visual analysis, quantitative comparison against SAR ice concentration products is presented. The method has been applied to SLTR L1 data showing improvement respect to the current operational method of cloud classification. In addition, classification of pixels covered by sea ices is provided which consequently improves the SST final product

Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event.

Volcanic Ash and SO2 retrievals using synthetic MODIS TIR data: comparison between inversion procedures and sensitivity analysis

In this work the volcanic ash and SO2 retrievals obtained by applying three different procedures (LUT - Look Up Table, NN - Neural Network and VPR - Volcanic Plume Removal) on MODIS Thermal InfraRed (TIR) synthetic measurements have been compared. The synthetic measurements are generated using MODTRAN Radiative Transfer Model (RTM) for defined volcanic cloud configurations. The results, presented as the percentage difference between the retrieved ash and SO2 total masses and the true values used for the synthetic data generation, indicate maximum differences of +/- 15% and +/- 10% for all the procedures and for ash and SO2 retrievals respectively. A sensitivity analysis has been also realized to investigate the influence of volcanic cloud altitude and water vapour profile on SO2 retrievals at 7.3 and 8.6 μm. Results confirm the high sensitivity of the 7.3 μm retrieval to the volcanic cloud altitude and show that the SO2 total masses estimated at 7.3 and 8.6 μm separately can be used to improve the information on the plume height. Finally, the water vapour profile is used to compute the minimum altitude over which the 7.3 μm retrieval is effective.