Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV PART II: BET_EF installation PART II: BET_EF installation

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Install BET_EF BET_EF requires the installation of the Installer 3.1 and the.NET framework (checked automatically and included in the installation package)

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Run BET_EF… and BET_UPGRADE …ready to be run… … and to browse the MANUAL

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_EF and BET_UPGRADE BET_UPGRADE package upgrading and reviewing the ET settings input of volcano information, past data and monitoring parameters … BET_EF package it runs the Event Tree Probabilities distribution Probability maps … Load information Update & review

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Recover function BET_UPGRADE automatically saves all the previous loading processes (both complete or partial)… all of them can be “recovered” and upgraded

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Goal In BET_UPGRADE, we input NODE BY NODE: 1.Volcanological information 2. Past data 3. Monitoring parameters 4. Past monitored episodes 5.Name, location, maps, etc. for the visualization of output probabilities (when necessary) Non monitoring infoMonitoring info Other info Note that: each input helps, but is not necessary: if some information is not available, the epistemic uncertainty increases, but probability estimations are still possible Monitoring info are not present at all nodes

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV NODE 1 UNREST

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Node 1 Probability of UNREST in the next time window Focus: Temporal information for all processes (definition of the forecasting window) Definition and detection of unrest episodes Possible inputs: 1.Volcanological information 2. Past data 3. Monitoring parameters

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Past data: Historical record of unrest episodes: Nu “successes” on N “measures” - Length of the time windows - number of windows without unrest episodes (N, white blocks) - number of windows in which episodes start (Nu, red blocks) NB windows containing ongoing unrest episodes (yellow blocks) are not counted Node 1: Models and Past data Volcanological information:  (best guess) and  (confidence) from a model that forecasts the next unrest episode

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 1: Monitoring Monitoring parameters: Number (main form) and definition of each one of them (monitoring form) - Name, description - Symbol & units - lower and upper thresholds - relationship

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 1: Main form Volcanological infoPast data Monitoring … and the preview of the distribution can be visualized Information is input ONLY WHEN AVALABLE

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 1: Monitoring forms Monitoring

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV NODE 2 MAGMA (movement of magma bodies)

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Node 2 Probability of MAGMA given an unrest Focus: Detection of magma Usually the less constrained process Monitoring parameters to track the presence of magma (during an ongoing unrest) Input information: 1.Volcanological information 2. Past data 3. Monitoring parameters 4. Past monitored episodes

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 2: Models and Past data Past data: Historical record of magmatic unrest episodes: Nm and Nu - Nu: number of unrest episodes (less or equal to node 1) - Nm: number of magmatic unrest episodes Volcanological information:  (best guess) and  (confidence) from a model that forecasts the occurrence of magmatic unrest among unrest episodes

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 2: Monitoring Monitoring parameters: Number (main form) and definition of each one of them (monitoring form) - Name, description - Symbol & units - lower and upper thresholds - relationship - weight

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 2: Past monitored events Past monitored events: 1) Number of monitored past UNREST (in the main form) 2) outcome, i.e., magmatic or not (Yes/No) 3) measures during the unrest

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 2: Main form Volcanological infoPast data Monitoring Past Mon.

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 2: Monitoring and Past mon. forms MonitoringPast Mon.

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV NODE 3 ERUPTION

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Node 3 Probability of ERUPTION given a magmatic unrest Focus: Forecasting of eruptions Monitoring parameters to recognize an incoming eruption (during an ongoing magmatic unrest) Input information: 1.Volcanological information 2. Past data 3. Monitoring parameters 4. Past monitored episodes

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Past data: Historical record of magmatic unrest episodes: Ne and Nm (or Nu) - Ne: number of eruptions - Nm: number of magmatic unrest episodes in alternative… - Nu: number of known unrest episodes Node 3: Models and past data Volcanological information:  (best guess) and  (confidence) from a model that forecasts the occurrence of eruptions among magmatic unrest episodes

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 3: Monitoring and Past mon. episodes Past monitored events: As in node 2, but only monitored magmatic unrest episodes Monitoring: As in node 2, with the goal of forecasting eruptions

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 3: Main form Volcanological infoPast data Monitoring Past Mon.

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 3: Monitoring forms MonitoringPast Mon.

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV NODE 4 VENT

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Node 4 Probability of a SPECIFIC VENT for the incoming eruption Focus: Spatial problem: number and geometry of possible locations are chosen by the user Localization of monitoring among the parameters at node 1,2 and 3 Monitoring do not control completely the short-term probability Input information: 1.Volcanological information 2. Past data 3. Monitoring parameters… of nodes 1,2 & 3

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 4: Spatial definition UTM or degrees Volcano coords Visualization map Volcano’s Geometry: central or grid

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 4: Spatial definition UTM or degrees Volcano coords Visualization map Volcano’s Geometry: central or grid

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 4: Models, Past data and Monitoring Monitoring: All parameters defined at nodes 1, 2 and 3 may be localized to help spatially forecast the impending eruption… … there is not a specific list of monitoring parameters at node 4!!! Past data: Historical eruption with known vent position: Ne (in the i-th location) Volcanological information:  i (best guess) and  i  (confidence) for each vent location from a model that forecasts the position of the next vent

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 4: Models and Past data Volcanological infoPast data … or loaded from a file for all VENTS

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV NODE 5 SIZE

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE: Node 5 Probability of a SPECIFIC SIZE for the incoming eruption Focus: Energetic/scenario approaches Number of size groups and their definition are not predefined, but are defined by the user Monitoring is not considered Input information: 1.Volcanological information 2. Past data

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 5: Groups, Models and Past data The number of groups and their definition must be defined by the user with a practical approach: - link with models for outcomes (lava flows, tephra fall, pyroclastic flows, …) - effusive / explosive / scenarios - VEIs - … Past data: Historical eruption with known size/type Volcanological information:  i (best guess) and  i  (confidence) for each size/type from a model that forecasts the size/type of the next eruption

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV Node 5: Groups, Models and Past data number of groups Volcanological infoPast data

Eruption Forecasting through the Bayesian Event Tree: the software package BET_EF INGV BET_UPGRADE summary FINISHED!!!! BET_EPGRADE summarizes all of the input information… …and sets all files to run BET_EF