quantifying long- and short-term volcanic hazard. erice, 6-8 nov. 2006 ingv bet: a probabilistic...

Quantifying long- and short-term volcanic hazardQuantifying long- and short-term volcanic hazard. . Erice, 6-8 Nov. 2006Erice, 6-8 Nov. 2006 INGVINGV

BET: a probabilistic tool for Eruption BET: a probabilistic tool for Eruption Forecasting and Volcanic Hazard AssessmentForecasting and Volcanic Hazard Assessment

W. Marzocchi, L. Sandri, J. Selva

INGV-Bologna

BET: a probabilistic tool for Eruption BET: a probabilistic tool for Eruption Forecasting and Volcanic Hazard AssessmentForecasting and Volcanic Hazard Assessment

W. Marzocchi, L. Sandri, J. Selva

INGV-Bologna

Project INGV-DPC V4: “Innovative techniques to study active Project INGV-DPC V4: “Innovative techniques to study active volcanoes”.volcanoes”. Responsibles: W.Marzocchi, INGV-Bo, A. Zollo, Univ. of NaplesResponsibles: W.Marzocchi, INGV-Bo, A. Zollo, Univ. of Naples

Project INGV-DPC V4: “Innovative techniques to study active Project INGV-DPC V4: “Innovative techniques to study active volcanoes”.volcanoes”. Responsibles: W.Marzocchi, INGV-Bo, A. Zollo, Univ. of NaplesResponsibles: W.Marzocchi, INGV-Bo, A. Zollo, Univ. of Naples


V4 Project Website V4 Project Website ((http://www.bo.ingv.it/v4http://www.bo.ingv.it/v4))V4 Project Website V4 Project Website ((http://www.bo.ingv.it/v4http://www.bo.ingv.it/v4))


OUTLINE of the presentationOUTLINE of the presentationOUTLINE of the presentationOUTLINE of the presentation

Defining BET Defining BET Defining BET Defining BET General theoretical description plus some details

BET softwareBET softwareBET softwareBET software Main features of the BET_EF code

MESIMEX applicationMESIMEX applicationMESIMEX applicationMESIMEX application Checking how BET works


What is BET?What is BET?What is BET?What is BET?

BET (Bayesian Event Tree)BET (Bayesian Event Tree) is a new statistical codestatistical code to estimate and visualize short- to long-term eruption forecasting (BET_EF)eruption forecasting (BET_EF) and volcanic hazard (BET_VH)(BET_VH) and relative uncertainties (epistemicepistemic and aleatoryaleatory)

BET (Bayesian Event Tree)BET (Bayesian Event Tree) is a new statistical codestatistical code to estimate and visualize short- to long-term eruption forecasting (BET_EF)eruption forecasting (BET_EF) and volcanic hazard (BET_VH)(BET_VH) and relative uncertainties (epistemicepistemic and aleatoryaleatory)

BET Output:BET Output: Time and space evolution of the probability function of each specific event in which we are interested in.

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 Input:BET Input: Volcanological data, models, and/or expert opinion. These data are provided by the end-user.

BET transforms these information into BET transforms these information into probabilitiesprobabilities


The method is based on three basic stepsThe method is based on three basic stepsThe method is based on three basic stepsThe method is based on three basic steps

1. Design of a generic Bayesian Event Tree

BibliographyBibliography

Newhall and Hoblitt, Bull. Volc. 2002 (for step 1) Marzocchi et al., JGR 2004 (for steps 2 and 3) Marzocchi et al., 2006; IAVCEI volume on statistics in Volcanology (for steps 2 and 3) Marzocchi et al., 2006, submitted to Bull. Volcan.(full description of BET)

BibliographyBibliography

Newhall and Hoblitt, Bull. Volc. 2002 (for step 1) Marzocchi et al., JGR 2004 (for steps 2 and 3) Marzocchi et al., 2006; IAVCEI volume on statistics in Volcanology (for steps 2 and 3) Marzocchi et al., 2006, submitted to Bull. Volcan.(full description of BET)

How BET works?How BET works?How BET works?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


The probability The probability of the SELECTED PATH is the product of conditional of the SELECTED PATH is the product of conditional

probability probability ii at ALL SELECTED BRANCHES: at ALL SELECTED BRANCHES:

11]] • [• [22]] • [• [33]] • [• [44]] • [• [55]] • …• …

The probability The probability of the SELECTED PATH is the product of conditional of the SELECTED PATH is the product of conditional

probability probability ii at ALL SELECTED BRANCHES: at ALL SELECTED BRANCHES:

11]] • [• [22]] • [• [33]] • [• [44]] • [• [55]] • …• …

BET Structure & ProbabilityBET Structure & ProbabilityBET Structure & ProbabilityBET Structure & Probability

NODE 1 NODE 2 NODE 3 NODE 4 NODE 5 Node 6 NODE 7 NODE 8 NODE 9 NODE 10

Unrest Origin Outcome Location Magnitude Phenomena Area Overcoming threshold

Exposure Vulnerability

Unrest Magma Eruption loc #1 VEI 3- Tephra fall Area #1 Yes People …

No Unrest No magma No eruption loc #2 VEI 4 Pyroclastic Area #2 No Building flow

… VEI 5+ …Lahars

loc #k Area #mLava flow

… …

loc #N Area #N

BET_EFBET_EFshort to long-termshort to long-term

BET_EFBET_EFshort to long-termshort to long-term

BET_VHBET_VHlong-termlong-term

BET_VHBET_VHlong-termlong-term


CONDITIONAL PROBABILITY AT THE NODE:CONDITIONAL PROBABILITY AT THE NODE:

kk = = kk(M)(M) + (1-+ (1- kk

(NM)(NM)

CONDITIONAL PROBABILITY AT THE NODE:CONDITIONAL PROBABILITY AT THE NODE:

kk = = kk(M)(M) + (1-+ (1- kk

(NM)(NM)

kk(M)(M)MONITORING PARTMONITORING PART

Monitoring Data & ModelsMonitoring Data & Models

kk(M)(M)MONITORING PARTMONITORING PART

Monitoring Data & ModelsMonitoring Data & Models

kk(NM)(NM)NON-MONITORING PARTNON-MONITORING PART

Non-monitoring Data, Geological & Non-monitoring Data, Geological & Physical ModelsPhysical Models

kk(NM)(NM)NON-MONITORING PARTNON-MONITORING PART

Non-monitoring Data, Geological & Non-monitoring Data, Geological & Physical ModelsPhysical Models

MONITORING DATAMONITORING DATA

State of unrest State of unrest at at tt00through FUZZY LOGICthrough FUZZY LOGIC

MONITORING DATAMONITORING DATA

State of unrest State of unrest at at tt00through FUZZY LOGICthrough FUZZY LOGIC

Conditional Probability [Conditional Probability [KK] (Node k)] (Node k)Conditional Probability [Conditional Probability [KK] (Node k)] (Node k)


Bayes theoremBayes theoremBayes theoremBayes theorem

MODELS Prior

MODELS Prior

DATALikelihood

… … each part each part kk(.)(.)(monitoring and non-monitoring)(monitoring and non-monitoring)… … each part each part kk(.)(.)(monitoring and non-monitoring)(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)


Through FUZZY SET theory…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 node2. 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 FUZZY SET theory…

• Through expert opinion and/or looking at “analogs” (need of WOVOdat!), the user defines: 1. the SET of parameters at each node2. 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 detailsgoing into some details: including monitoring: including monitoring… … going into some detailsgoing into some details: including monitoring: including monitoring

degree of anomaly zi

measure

State of unrest State of unrest

A priori model [k(1)]A priori model [k(1)]


… … going into some details: going into some details: from from monitoring to probabilitymonitoring to probability… … going into some details: going into some details: from from monitoring to probabilitymonitoring to probability

k(M)|H] = k

(1)[H(1)|k(1)Hk

(M)|H] = k(1)[H(1)|k

(1)H

Z(k) = i zi degree of anomaly at the node

<k(1)>1 - exp(-(k)) Average of k

(1)<k(1)>1 - exp(-(k)) Average of k

(1)

Monitoring part

zi degree of anomaly of i-th parameter

BET computes:

The user: input measures


BET_EF PACKAGEBET_EF PACKAGEBET_EF PACKAGEBET_EF PACKAGE


Event Tree for BET_EFEvent Tree for BET_EFEvent Tree for BET_EFEvent Tree for BET_EF

NODE 1 NODE 2 NODE 3 NODE 4 NODE 5Unrest Origin Outcome Location Magnitude

Unrest Magma Eruption loc #1 SIZE 1

No Unrest No magma No eruption loc #2 SIZE 1+

… SIZE 2

loc #N SIZE 2+

all locations SIZE 3+

Number & geometry chosen by the user Number of size groups

defined by the user


BET_EF PackageBET_EF PackageBET_EF PackageBET_EF Package

Hazard procedure Target volcano

Event selection (Unrest + Magmatic Intrusion + Eruption+Vent all locs + SIZE=2+)

OUTPUT


Probability visualizationProbability visualizationProbability visualizationProbability visualization

ABSOLUTE PROBABILITY CONDITIONAL 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


Monitoring measuresMonitoring measuresMonitoring measuresMonitoring measures

Measured values are directly input in BET_EF at nodes 1, 2, 3, & 4


Probability MapsProbability MapsProbability MapsProbability 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.


Probability MapsProbability MapsProbability MapsProbability 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.


BET_ UPGRADE (example node 1)BET_ UPGRADE (example node 1)BET_ UPGRADE (example node 1)BET_ UPGRADE (example node 1)

models data monitoring monitoring thresholds


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 volcanoesApplication to volcanoesApplication to volcanoesApplication to volcanoes

ALL VOLCANOES can be loaded in BET_EF with the ALL VOLCANOES can be loaded in BET_EF with the BET_UPGRADE PACKAGEBET_UPGRADE PACKAGEALL VOLCANOES can be loaded in BET_EF with the ALL VOLCANOES can be loaded in BET_EF with the BET_UPGRADE PACKAGEBET_UPGRADE PACKAGE

Until now, we have (preliminary) implemented BET_EF for Until now, we have (preliminary) implemented BET_EF for Mt. VesuviusMt. Vesuvius, and we , and we are doing the same for are doing the same for Campi FlegreiCampi Flegrei (INGV-DPC V3_2 and V3_4 projects). (INGV-DPC V3_2 and V3_4 projects).

Until now, we have (preliminary) implemented BET_EF for Until now, we have (preliminary) implemented BET_EF for Mt. VesuviusMt. Vesuvius, and we , and we are doing the same for are doing the same for Campi FlegreiCampi Flegrei (INGV-DPC V3_2 and V3_4 projects). (INGV-DPC V3_2 and V3_4 projects).


BET_EF will be distributed for free BET_EF will be distributed for free after a pilot test carried out after a pilot test carried out by volcanologists with experience in managing volcanic crises. by volcanologists with experience in managing volcanic crises.


Application to MESIMEXApplication to MESIMEX

You can download a report (in Italian) at the web siteYou can download a report (in Italian) at the web sitehttp://www.bo.ingv.it/~warnerhttp://www.bo.ingv.it/~warner

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, 2004.A revision of parameters and thresholds is under consideration in the project INGV-DPC V3_4 Vesuvius (leaded by R. Cioni & E. Del Pezzo)


Application to MESIMEXApplication 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%


CO2 flux = 10 Kg m-2 d-1

Other parameters inside the background



Maximum magnitude in the last month = 4.2


N. events in the last month = 38


Seismic events localized out of crater




Maximum magnitude in the last month= 4.2


N. events in the last month = 61 LP events in the last month = 2 T fumaroles = 100 0C

Presence of SO2 Localization of VT and LP







Presence of SO2

Localization of VT and LP

5 10-5, d/dt = 5 10-5 d-1

3.6 Hz, ddt = -0.4 Hz d-1







Presence of SO2


1 10-4, d/dt = 5 10-5 d-1

3.5 Hz, ddt = -0.5 Hz 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 = 110 0C

Presence of SO2


3 10-4, d/dt = 2 10-4 d-1

2.5 Hz, ddt = -1 Hz d-1 (tremor episodes) 0.3 (variations in hypocenters location)



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 homePoints to take homePoints to take homePoints to take home

quantifying long- and short-term volcanic hazard. erice, 6-8 nov. 2006 ingv bet: a probabilistic...

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