Consorzio COMETA - Progetto PI2S2 UNIONE EUROPEA Grid on Earth Science applications: state of art and developments Ing. Danilo Reitano INGV – Sezione di Catania Grid Open Days all’Università di Messina Messina,

Messina, Grid Open Days all’Università di Messina, Summary The INGV Inside “PI2S2” Project Application: state of art

Messina, Grid Open Days all’Università di Messina, The INGV The legislative decree n. 381 of 29 September 1999 founded the new Istituto Nazionale di Geofisica e Vulcanologia, which become one of the most important European research institutions in Earth Science. The INGV main research fields are in geophysics, seismology and volcanology. The Institute comprises eight main Departments located in five main centres. Part of INGV are also two National Groups. In particular the Section of Catania studies all active volcanoes located in the eastern part of Sicily and regional seismic areas

MUR (Ministry of University and Scientific Research) MUR (Ministry of University and Scientific Research) DPC (Civil Defense Department) DPC (Civil Defense Department) EU (European Community) EU (European Community) PNRA (National Program for Antarctic Research) PNRA (National Program for Antarctic Research) Regions-Local administrations Regions-Local administrations Private-Industry Private-Industry Funds

MUR, EU:MUR, EU: Research activity DPC (Civil Defense Department)DPC (Civil Defense Department) Monitoring and surveillance systems - Research. INGV has CNT (RM), Osservatorio Vesuviano (NA) and Sala Operativa (CT) active H24 control rooms.

In order to monitor wide active areas in real time is fundamental to use all new technologies and different kind of sensors. Transmission infrastructure has a predominant role to communicate data Monitoring Networks

Seismic Network INGV UFS - Catania GPS, Tilt ground deformation measures Most of used devices are TCP/IP compliant INGV UFDG - Catania

Ground deformation network Satellite Radio Wireless Transmissions

Activity on Mt. Etna and Mt. Stromboli Active volcanoes

INGV uses a video sensors network to monitor volcanoes activity. Infrared Thermal Sensors Single frame Real time streaming video Time lapse digital video Camera Network

INGV on Grid examples: Bandwidth Improvement Infrasonic signals Images Processing Tephra and MM5 Modelling lava flows The INGV on grid

In order to provide betterconnections for GRID computing INGV has improved wired connection with GARR nodes Fig. 1 - Connection Scheme Infrastructure

Fig. 2 - New Hyperlan Infrastructure Locations: Piazza Roma: main site in Catania downtown CUAD acquisition Centre (in Catania). Nicolosi: Monitoring site for Mt. Etna volcano The new infrastructure

Infrasound signals Tipical Infrasound trace Power spectrum Explosion recorded by infrasonic sensor Clustering of infrasound events on Etna volcano

Infrasound events We get the events by using a method based on: Standard deviation squared series of the infrasonic signal (one S.D. point every 3 sec more or less) Two thresholds on the S.D. series The uppest one declairs the events The lowest one gives the start and the end of the events

A maximum correlation coefficient matrix, m, of size n X n for a buffer containing n events is built, were element mxy representing the maximum of the correlation function between the two events x and y, changing the position of the event x with respect to the event y, with a specific overlap, until the maximum’s found Not aligned events, not max correlation coefficent aligned events, max correlation coefficent Clustering Method

By using trigger method discussed before, we’ve collected about 1000 infrasound events of different type of explosions on Etna volcano, during different periods, and meshed togheter in a single buffer. In this way, we’re trying to get a wide range of dynamics as well as possible. Then we’ve filtered these signals between 0.5 and 5 Hz. Each event has a duration around 2 – 3 second. Some examples are reported below : Families detection

Features extractor Semblance method

We want to realize a final classification of the events, making a cascade of the cross-correlation clustering, features extractor and the neural network classificator Buffer events Modified Green & Nueberg algorithm Family 1 Family 2 Family n training set Testing set Perceptron N.N. New events Automatic classification Using neural network

Designed on a previous INGV project first phase to calibrate parameters and technologies used second phase to apply pattern recognition tecnique using parallel code to submit on GRID Pattern Recognition to monitor explosive Strombolian activity

To monitor strombolian activity in order to automatically classify different types of explosions and achieve real time identification of critical ones. In this project we choose the infrared camera as image source for explosions monitoring. The view from the infrared camera Main scope of the project

The application is composed by:  a Loader module which captures images to be processed  a Trigger module which analyzes acquired images and selects the ones suspected to be an event, basing on predetermined parameters  a Scanner module which classify events founded by the Trigger, discarding wrong detections Image capturing (4 images per second) LOADERTRIGGERSCANNER Implementation of a search window for event identification Event classification How it works

Positive trigger: event of When the number of pixels inside the window is over the threshold an event sequence is started and recorded. An example

Position parameteres are also used to determine the origin and level of explosion. LEVEL 1 LEVEL 2 AREA 1 AREA 2AREA 3 The scanner module

THRESHOLD GAUSSIAN FILTER White pixels that can generate false events After the application of the gaussian filter the number of white pixels is reduced Due to the automatic white level balancing, in foggy conditions there are many sparse white pixels in the thresholded image. This can be solved using a Gaussian filter. Critical conditions managements

Event Classification using Matlab code Testing of various pattern recognition tecnique to better define explosion edge in order to obtain parameters useful to classification. Region Growing Starting from a seed it permits to ‘grow up’ reducing errors It is called 3 times, as the craters number Second phase – work in progress

Region growing Threshold Original Image Region growing Threshold Examples Region Growing benefits

Techniques to better inscribe the explosion inside a geometric figure: Bounding box Principal Component Analisys Convex Hull Obtaining useful classification parameters

Messina, Grid Open Days all’Università di Messina, TEPHRA Model TEPHRA is two dimensional advection-diffusion model implemented by Bonadonna et al. that describes the sedimentation process of particles from volcanic plumes. TEPHRA includes grain- size dependent diffusion law, particle density variation, a stratified atmosphere, and terminal settling velocities function of Reynold’s number.

Messina, Grid Open Days all’Università di Messina, Tephra fallout risk ● AVIATION ● HEALTH ● VEGETATION ● BUILDINGS TEPHRA belongs to that class of models mainly used for Civil Protection purposes, as it helps to give public warnings and plan mitigation measures. It is well known that the tephra fallout produced during explosive Etna eruptions is dangerous for: Catania, Fontanarossa Airport October 2002, Catania during ash fallout from Mt. Etna

Messina, Grid Open Days all’Università di Messina, Use of TEPHRA Model DAILY meteorological data provided by Aeronautical Military and volcanological parameters are processed by TEPHRA to simulate two different eruptive scenarios (corresponding to 1998 and Etna eruptions). The model outputs are plotted on maps and transferred to Civil Protection EARLY in the morning.

Messina, Grid Open Days all’Università di Messina, TEPHRA Model simulations Job1 Job2 Job3 Job4 Job5 Job6 Job7 Job8 T E P H R A M o d e l INPUT PARAMETERS: Meteorological and Volcanic parameters OUTPUT PARAMETERS: Hazard Maps 00: :00 03: :00 06: :00 09: :00 12: :00 15: :00 18: :00 21: :00

Parallel Code The computation of TEPHRA is greatly accelerated by using PARALLEL COMPUTING. SPMD (Single Program - Multiple Data) is the parallel code model realized by MPI specification embedded in the code together with normal C language: multiple instances of the same code are run on different computer nodes (different processors). Since the results at each grid point does not depend on the solution at the other points then: ● The whole set of points is divided among several different computer nodes ● Each node runs the same code and estimates tephra accumulation for its own set of grid points ● The master node takes care of administrative burden, gathers the results of each node and produces the final output as hazard map.

Porting of TEPHRA model on Grid Thanks to its parallel code TEPHRA Model was ported to GRID infrastructure. The job submission process runs automatically through the use of a batch script that: 1) retrieves different input files and composes JDLs 2) submits the job to GRID 3) checks the status 4) retrieves the output. Previous steps are made daily for many simulations (16).

TEPHRA Model on Grid GRID permits us to have more computing resources, hence more speed on execution time. No predicting of waiting time on job submission is possible, because it depends on GRID workload. It is a problem because it couldn’t assure to obtain the outputs early in the morning. PROBLEM: SOLUTION >>>

Emergency Queue We can minimize waiting submission time by using a special queue named EMERGENCY QUEUE. This special queue gives high priority to a particular job and suspends all other submitted jobs during its running time.

Grid benefit are very useful for INGV activity Grid benefit are very useful for INGV activity Social impact for faster simulations and scenarios evaluation during activity Social impact for faster simulations and scenarios evaluation during activity INGV / Civil Protection better sharing data INGV / Civil Protection better sharing data Storage networks and CPU massive use Storage networks and CPU massive use Conclusions

Grazie.... Danilo Reitano INGV Catania Section