Book contents
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Series page
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Copyright page
- Contents
- Contributors
- Preface
- Acknowledgements
- Part I Introduction
- Part II ‘Fluid’ Earth Applications: From the Surface to the Space
- Part III ‘Solid’ Earth Applications: From the Surface to the Core
- 11 Trans-Dimensional Markov Chain Monte Carlo Methods Applied to Geochronology and Thermochronology
- 12 Inverse Problems in Lava Dynamics
- 13 Data Assimilation for Real-Time Shake-Mapping and Prediction of Ground Shaking in Earthquake Early Warning
- 14 Global Seismic Tomography Using Time Domain Waveform Inversion
- 15 Solving Larger Seismic Inverse Problems with Smarter Methods
- 16 Joint and Constrained Inversion as Hypothesis Testing Tools
- 17 Crustal Structure and Moho Depth in the Tibetan Plateau from Inverse Modelling of Gravity Data
- 18 Geodetic Inversions and Applications in Geodynamics
- 19 Data Assimilation in Geodynamics: Methods and Applications
- 20 Geodynamic Data Assimilation: Techniques and Observables to Construct and Constrain Time-Dependent Earth Models
- 21 Understanding and Predicting Geomagnetic Secular Variation via Data Assimilation
- 22 Pointwise and Spectral Observations in Geomagnetic Data Assimilation: The Importance of Localization
- Index
- References
12 - Inverse Problems in Lava Dynamics
from Part III - ‘Solid’ Earth Applications: From the Surface to the Core
Published online by Cambridge University Press: 20 June 2023
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Series page
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Copyright page
- Contents
- Contributors
- Preface
- Acknowledgements
- Part I Introduction
- Part II ‘Fluid’ Earth Applications: From the Surface to the Space
- Part III ‘Solid’ Earth Applications: From the Surface to the Core
- 11 Trans-Dimensional Markov Chain Monte Carlo Methods Applied to Geochronology and Thermochronology
- 12 Inverse Problems in Lava Dynamics
- 13 Data Assimilation for Real-Time Shake-Mapping and Prediction of Ground Shaking in Earthquake Early Warning
- 14 Global Seismic Tomography Using Time Domain Waveform Inversion
- 15 Solving Larger Seismic Inverse Problems with Smarter Methods
- 16 Joint and Constrained Inversion as Hypothesis Testing Tools
- 17 Crustal Structure and Moho Depth in the Tibetan Plateau from Inverse Modelling of Gravity Data
- 18 Geodetic Inversions and Applications in Geodynamics
- 19 Data Assimilation in Geodynamics: Methods and Applications
- 20 Geodynamic Data Assimilation: Techniques and Observables to Construct and Constrain Time-Dependent Earth Models
- 21 Understanding and Predicting Geomagnetic Secular Variation via Data Assimilation
- 22 Pointwise and Spectral Observations in Geomagnetic Data Assimilation: The Importance of Localization
- Index
- References
Summary
Abstract: Lava flow and lava dome growth are two main manifestations of effusive volcanic eruptions. Less-viscous lava tends to flow long distances depending on slope topography, heat exchange with the surroundings, eruption rate, and the erupted magma rheology. When magma is highly viscous, its eruption on the surface results in a lava dome formation, and an occasional collapse of the dome may lead to a pyroclastic flow. In this chapter, we consider two models of lava dynamics: a lava flow model to determine the internal thermal state of the flow from its surface thermal observations, and a lava dome growth model to determine magma viscosity from the observed lava dome morphological shape. Both models belong to a set of inverse problems. In the first model, the lava thermal conditions at the surface (at the interface between lava and the air) are known from observations, but its internal thermal state is unknown. A variational (adjoint) assimilation method is used to propagate the temperature and heat flow inferred from surface measurements into the interior of the lava flow. In the second model, the lava dome viscosity is estimated based on a comparison between the observed and simulated morphological shapes of lava dome shapes using computer vision techniques.
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- Publisher: Cambridge University PressPrint publication year: 2023