Abstract
Magma intrusions grow to their final geometries by deforming the Earth’s crust internally and by displacing the Earth’s surface. Interpreting the related displacements in terms of intrusion geometry is key to forecasting a volcanic eruption. While scaled laboratory models enable us to study the relationships between surface displacement and intrusion geometry, past approaches entailed limitations regarding imaging of the laboratory model interior or simplicity of the simulated crustal rheology. Here we apply cutting-edge medical wide beam X-ray Computed Tomography (CT) to quantify in 4D the deformation induced in laboratory models by an intrusion of a magma analogue (golden syrup) into a rheologically-complex granular host rock analogue (sand and plaster). We extract the surface deformation and we quantify the strain field of the entire experimental volume in 3D over time by using Digital Volume Correlation (DVC). By varying the strength and height of the host material, and intrusion velocity, we observe how intrusions of contrasting geometries– cryptodomes, cup shapes, cone sheets and dikes – grow, and induce contrasting strain field characteristics and surface deformation in 4D. We observe dominantly mixed-mode (opening and shear) fracture localisation in low-cohesion material overburden versus opening-mode fracture localisation in high-cohesion material overburden. The results demonstrate how the combination of CT and DVC can greatly enhance the utility of optically non-transparent crustal rock analogues in obtaining insights into shallow crustal deformation processes. This unprecedented perspective on the spatio-temporal interaction of intrusion growth coupled with host rock deformation provides a conceptual framework that can be tested by geological field observations at eroded volcanic systems and by the ever increasing spatial and temporal resolution of geodetic data at active volcanoes.
Highlights
Magma intrusion induces crustal deformation, which in turn results in surface displacements, in changes in the local gravimetric field and in earthquake swarms, all of which can be monitored with geodetic and geophysical techniques (Brenguier et al, 2016; Fernández et al, 2017 and references therein; Figure 1A)
The results demonstrate how the combination of Computed Tomography (CT) and Digital Volume Correlation (DVC) can greatly enhance the utility of optically non-transparent crustal rock analogs in obtaining insights into shallow crustal deformation processes
A further 12 experiments were monitored with dynamic wide beam X-ray Computed Tomography (CT)
Summary
Magma intrusion induces crustal deformation, which in turn results in surface displacements, in changes in the local gravimetric field and in earthquake swarms, all of which can be monitored with geodetic and geophysical techniques (Brenguier et al, 2016; Fernández et al, 2017 and references therein; Figure 1A). The most widely-used inverse geodetic models are limited by the simplifications and boundary conditions that are implemented to make mathematical solutions possible They assume simple elastic or viscoelastic crustal deformation, and they represent intrusions as infinitesimal point pressure sources, finite pressurized spheroids or rectangular dislocation planes (Mogi, 1958; Okada, 1985; Yang et al, 1988; Figure 1C). Observations of natural outcrops of frozen and exhumed magmatic intrusions, have revealed a great variability and complexity of intrusion geometry. Natural magma intrusion may comprise complex combinations of the above-mentioned forms Limitations of such field observations are that they represent a final static and commonly incomplete account of the intrusion processes, and they usually lack any link with the surface. Other aspects of some sheet intrusion geometries and their field relationships, such as folds and reverse faults around blunt, irregularly-shaped intrusion tips, are alternatively interpreted as formed by “viscous indentation” of magma within plastically deforming host-rock (e.g., Mathieu et al, 2008; Abdelmalak et al, 2012; Delcamp et al, 2014; Spacapan et al, 2017; Bertelsen et al, 2018; Haug et al, 2018)
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