investigating lava rheology using lab-based lava flows, computer vision, and finite-elements modeling

Lava rheology is a major control on lava flow behavior and a critical parameter in flow simulations, but is very difficult to measure at field conditions or correctly extrapolate from the lab scale. We present a new methodology for investigating lava rheology through a combination of controlled experiments, image analysis and numerical forward modeling. Our experimental setup, part of the Syracuse University Lava Project (http://lavaproject.syr.edu) includes a large furnace capable of melting up to 400 Kg of basalt, at temperatures up to 1350°C. The lava is poured into a channel made of sand to produce meters-long flows. We document the flows using a dual-camera system - a high-resolution video camera and an infrared thermal camera - placed directly above the flows. An added benefit from our setup is an empirical, calibrated conversion method from visible video data to thermal data, which can be useful for situations where a thermal camera is not available. Our experimental setup is special, as it is probably the only facility that allows such large scale controlled lava flows made of natural basaltic material. After collecting the data, we analyze the images for lava deformation and compare with numerical forward-models to constrain the rheological parameters and laws which best describe the flowing lava. For the video analysis, we employ the technique of Differential Optical Flow, which uses the time-variations of the spatial gradients of the field to estimate velocity between consecutive frames. Our forward-models are calculated by solving the Stokes flow equation on an unstructured finite-element mesh defined using the geometry of the observed flow itself. We explore a range of rheological parameters, including the power-law exponent n and the thermal activation energy. We compare our findings with predictions of the composition-based GRD model (Giordano, Russell and Dingwell, 2008). Figure 1 shows an example of a flow analysis result. On the left is a visible image of the flow, overlain with the calculated velocity magnitude. On the right are cross-sections through the data (blue) and numerical models of the flow (black) which use a range of thermal activation energy values. We show that for the high-temperature portion of the flow, where the cross-sections were taken, a Newtonian rheology with an effective activation energy of B = 1000 gives the best fit to the data. The methodology we present here can be used in field conditions to obtain in-situ information on lava rheology, without physical interaction with the flow and without being limited to current point-wise local measurements currently available.