Dynamic Hazard Maps

Chuck Connor, Laura Connor, Aurelie Germa, Rocco Malservisi

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1- Questions

Long-term volcanic hazard assessments aim to forecast the possible behavior of future volcanic activity and need to account for the dynamic nature of volcanic systems.

2- Expected outcomes:

3- Modeling framework

Our modeling framework has to relate statistical models of spatial intensity (vents per unit area), volume intensity (erupted volume per unit area) and volume-flux (erupted volume per unit of time and area) to geological models of subsurface processes of magma generation, storage and transport.

framework spatio-temporal distribution of volcanism statistical model of field growth numerical model of sub-surface dynamic_hazard_maps

This project has been presented to AGU Fall Meeting 2013, San Francisco.

Abstract: A New Way to estimate volcanic hazards and present multi-hazard maps.
Aurelie Germa; Charles Connor; Laura Connor; Rocco Malservisi

To understand long term hazards in distributed volcanic systems, we are developing a research framework to relate statistical models of spatial intensity (vents per unit area), volume intensity (erupted volume per unit area) and volume-flux intensity (erupted volume per unit time and area) to conceptual models of the subsurface processes of magma storage and transport. The distribution of mapped vents and volumes erupted from these vents are used to develop nonparametric (kernel density) statistical models for distributed volcanic systems. Using radiometric age determinations of vents and erupted units, we then estimate the recurrence rate of volcanism and associated uncertainty using a Monte Carlo approach. The outputs of Monte Carlo simulation of recurrence rates allow us to produce dynamic statistical maps that reveal the spatio-temporal evolution of volcanic activity within the field studied.

To further improve our research framework, we have implemented solutions to differential equations governing magma production and transport to model subsurface processes of magma ascent. This behavior can be statistically approximated by modeling the flow of a viscous fluid within a homogeneous porous medium using Darcy’s law with variable conductivity dependent on flow rate and lithospheric stresses (Bonafede and Boschi, 1992; Bonafede and Cenni, 1998). Using this continuous formulation, additional complexities that influence magma migration such as complex sources, magma generation, magma rheology, tectonic stresses, and/or anisotropic/heterogeneous behavior of the porous medium, can be simply implemented by varying the choice of source and conductivity parameters. In this way we can explore physical processes that may give rise to heterogeneous flux in numerical models and relate these outputs to observed vent distributions and volume flux at the surface.

Overall, data extracted from our research framework should link statistical models of volcano distribution with the processes governing magma production and ascent, and thus improve our understanding of the evolution of volcanic fields to improve methods for long-term volcanic hazard assessments. This study will enable us to predict how spatial intensity, volume intensity and volume-flux change in response to geochemical and geophysical changes that invariably occur in an active volcanic system.

Clicking on the small poster will take you to a full-size image. poster_AgU2013