Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV BET: a probabilistic tool for Eruption Forecasting and Volcanic Hazard Assessment W. Marzocchi, L. Sandri, J. Selva INGV-Bologna BET: a probabilistic tool for Eruption Forecasting and Volcanic Hazard Assessment W. Marzocchi, L. Sandri, J. Selva INGV-Bologna Project INGV-DPC V4: “Innovative techniques to study active volcanoes”. Responsibles: W.Marzocchi, INGV-Bo, A. Zollo, Univ. of Naples

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV V4 Project Website (

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV OUTLINE of the presentation Defining BET General theoretical description plus some details BET software Main features of the BET_EF code MESIMEX application Checking how BET works

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV What is BET? BET (Bayesian Event Tree)statistical code eruption forecasting (BET_EF) (BET_VH)epistemicaleatory BET (Bayesian Event Tree) is a new statistical code to estimate and visualize short- to long-term eruption forecasting (BET_EF) and volcanic hazard (BET_VH) and relative uncertainties (epistemic and aleatory) BET Output: BET Output: Time and space evolution of the probability function of each specific event in which we are interested in. BET Input: BET Input: Volcanological data, models, and/or expert opinion. These data are provided by the end-user. BET transforms these information into probabilities

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV The method is based on three basic steps 1. Design of a generic Bayesian Event Tree Bibliography â Newhall and Hoblitt, Bull. Volc (for step 1) ã Marzocchi et al., JGR 2004 (for steps 2 and 3) ã Marzocchi et al., 2006; IAVCEI volume on statistics in Volcanology (for steps 2 and 3) ã Marzocchi et al., 2006, submitted to Bull. Volcan.(full description of BET)Bibliography â Newhall and Hoblitt, Bull. Volc (for step 1) ã Marzocchi et al., JGR 2004 (for steps 2 and 3) ã Marzocchi et al., 2006; IAVCEI volume on statistics in Volcanology (for steps 2 and 3) ã Marzocchi et al., 2006, submitted to Bull. Volcan.(full description of BET) How BET works? 2. Estimate the conditional probability at each node 3. Combine the probabilities of each node to obtain probability distribution of any relevant event

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV The probability  of the SELECTED PATH is the product of conditional probability  i at ALL SELECTED BRANCHES:  1 ] [  2 ] [  3 ] [  4 ] [  5 ] … The probability  of the SELECTED PATH is the product of conditional probability  i at ALL SELECTED BRANCHES:  1 ] [  2 ] [  3 ] [  4 ] [  5 ] … BET Structure & Probability BET_EF short to long-term BET_EF BET_VHlong-termBET_VHlong-term

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV CONDITIONAL PROBABILITY AT THE NODE:  k  =   k (M)  + (1-  k (NM)  CONDITIONAL PROBABILITY AT THE NODE:  k  =   k (M)  + (1-  k (NM)   k (M)  MONITORING PART Monitoring Data & Models  k (M)  MONITORING PART Monitoring Data & Models  k (NM)  NON-MONITORING PART Non-monitoring Data, Geological & Physical Models  k (NM)  NON-MONITORING PART Non-monitoring Data, Geological & Physical Models MONITORING DATA State of unrest  at t 0 through FUZZY LOGIC MONITORING DATA State of unrest  at t 0 through FUZZY LOGIC Conditional Probability [  K ] (Node k)

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Bayes theorem MODELS Prior MODELS Prior DATA Likelihood … each part  k (.)  (monitoring and non-monitoring) At each node we account for: Models + data Epistemic and aleatoric uncertainities At each node we account for: Models + data Epistemic and aleatoric uncertainities POSTERIOR PDF  k  =  k (.)  [H (.) |  k (.)  H (.)  (no epistemic uncertainty)

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Through FUZZY SET theory… Through expert opinion and/or looking at “analogs” (need of WOVOdat!), the user defines: 1.the SET of parameters at each node 2.INTERVAL OF VALUES as threshold for each parameter Smooth variation of probabilities are found for small changes in monitoring parameters (smooth transitions) Through FUZZY SET theory… Through expert opinion and/or looking at “analogs” (need of WOVOdat!), the user defines: 1.the SET of parameters at each node 2.INTERVAL OF VALUES as threshold for each parameter Smooth variation of probabilities are found for small changes in monitoring parameters (smooth transitions) … going into some details : including monitoring degree of anomaly z i measure State of unrest  A priori model [  k (1) ]

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV … going into some details: from monitoring to probability  k (M) |H] =  k (1)  [H (1) |  k (1)  H   Z (k) =  i z i degree of anomaly at the node  1 - exp(-  (k) ) Average of  k (1)  Monitoring part z i degree of anomaly of i-th parameter BET computes: The user: input measures

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV BET_EF PACKAGE

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Event Tree for BET_EF Number & geometry chosen by the user Number of size groups defined by the user

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV BET_EF Package Hazard procedure Target volcano Event selection (Unrest + Magmatic Intrusion + Eruption+Vent all locs + SIZE=2+) OUTPUT

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Probability visualization ABSOLUTE PROBABILITYCONDITIONAL PROBABILITY AT THE NODE Selection done: (1) unrest -> (2) magmatic intrusion -> (3) eruption -> (4) location all -> (5) SIZE=2+ Probability that all the events in the selected path occur contemporaneously Probability that the events at the selected node occur, given previous nodes

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Monitoring measures Measured values are directly input in BET_EF at nodes 1, 2, 3, & 4

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Probability Maps Probability maps are visualized in BET_EF. Each grid point is defined by a probability distribution (epistemic uncertainity); the parameter of each distribution are reported in tables.

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Probability Maps Probability maps may also be loaded in GoogleEarth, complete with 3D plot, description of the parameters of the probability distribution at each grid point.

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV BET_ UPGRADE (example node 1) modelsdatamonitoring thresholds

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Major requirements to load a volcano in BET_EF are: 1.Models and/or theoretical believes, and/or expert elicitation 2.Catalog of past volcanic events and related phenomena 3.Monitoring parameters and relative threshold intervals 4.Number and geometry of vent locations Application to volcanoes ALL VOLCANOES can be loaded in BET_EF with the BET_UPGRADE PACKAGE Until now, we have (preliminary) implemented BET_EF for Mt. Vesuvius, and we are doing the same for Campi Flegrei (INGV-DPC V3_2 and V3_4 projects).

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV BET_EF will be distributed for free after a pilot test carried out by volcanologists with experience in managing volcanic crises.

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX You can download a report (in Italian) at the web site BET_EF code applied to MESIMEX. The code is developed in the INGV-DPC V4 project (leaded by W. Marzocchi & A. Zollo). The details are in Marzocchi et al., 2006; submitted to Bull. Volc. Monitoring parameters and thresholds are taken from Marzocchi et al., JGR, A revision of parameters and thresholds is under consideration in the project INGV-DPC V3_4 Vesuvius (leaded by R. Cioni & E. Del Pezzo)

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 17 Oct. 2006, 08:00; Probability per month Conditional Probability of specific size: Monitoring-independent! VEI=3: 64% VEI=4: 25% VEI=5+: 11%

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  CO 2 flux = 10 Kg m -2 d -1  Other parameters inside the background Application to MESIMEX 17 Oct. 2006, 09:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 17 Oct. 2006, 09:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  Maximum magnitude in the last month = 4.2  Other parameters inside the background  N. events in the last month = 38  CO 2 flux = 10 Kg m -2 d -1  Seismic events localized out of crater Application to MESIMEX 18 Oct. 2006, 07:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 18 Oct. 2006, 07:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  CO2 flux = 20 Kg m -2 d -1  Maximum magnitude in the last month= 4.2  Other parameters inside the background  N. events in the last month = 61  LP events in the last month = 2  T fumaroles = C  Presence of SO 2  Localization of VT and LP Application to MESIMEX 19 Oct. 2006, 09:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 19 Oct. 2006, 09:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  CO 2 flux = 30 Kg m -2 d -1  Maximum magnitude in the last month= 4.2  Other parameters inside the background  N. events in the last month = 104  LP events in the last month = 26  T fumaroles = C  Presence of SO 2  Localization of VT and LP   , d  /dt = d -1   3.6 Hz, d  dt = -0.4 Hz d -1 Application to MESIMEX 19 Oct. 2006, 18:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 19 Oct. 2006, 18:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  CO 2 flux = 300 Kg m -2 d -1  Maximum magnitude in the last month= 4.2  Other parameters inside the background  N. events in the last month = 183  LP events in the last month = 61  T fumaroles = C  Presence of SO 2  Localization of VT and LP   , d  /dt = d -1   3.5 Hz, d  dt = -0.5 Hz d -1 Application to MESIMEX 20 Oct. 2006, 15:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 20 Oct. 2006, 15:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  CO 2 flux = 400 Kg m -2 d -1  Maximum magnitude in the last month= 4.2  N. events in the last month = 258  LP events in the last month = 131  T fumaroles = C  Presence of SO 2  Localization of VT and LP   , d  /dt = d -1   2.5 Hz, d  dt = -1 Hz d -1 (tremor episodes)   0.3 (variations in hypocenters location) Application to MESIMEX 21 Oct. 2006, 17:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV Application to MESIMEX 21 Oct. 2006, 17:00; Probability per month

Quantifying long- and short-term volcanic hazard. Erice, 6-8 Nov INGV  BET is a transparent tool to calculate and to visualize probabilities related to eruption forecasting/hazard assessment  BET “dynamically” manages long-term (land use planning of the territory) and short-term (during emergency to help managing of short-term actions, e.g., evacuation) probabilities for each kind of possible event  BET considers all of the available (and relevant) information (models, state of the volcano, geologic/volcanologic/historic data, monitoring observations, expert elicitation); the output is the quantitative merging of all of them  BET takes properly into account the epistemic and aleatory uncertainties. This allows to highlight what we know and what we do not know about the system, indicating future possible works to improve the scheme  BET uses fuzzy logic to manage monitoring measurements smooth transitions in probability and overcome single threshold definition Points to take home