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Self‐ignition during the explosive eruption of mud volcanoes can create flames that in some cases reach heights that exceed hundreds of meters. To study the controls on electrical discharge in natural mud, we performed laboratory experiments using a shock‐tube apparatus to simulate explosive eruptions of mud. We vary the water content of the mud and proportions of fine particles. We measure electric discharge within a Faraday cage and we use a high‐speed video camera to image the eruption of mud and some of the electric discharge events. We find that (a) decreasing the proportion of fine particles and (b) increasing water content each suppress the number and magnitude of electric discharge events. Experimentally observed mud volcano lightning occurs where particles exit from the vent and within the jet of erupting particles.

Over the last decades, remote observation tools and models have been developed to improve the forecasting of ash‐rich volcanic plumes. One challenge in these forecasts is knowing the properties at the vent, including the mass eruption rate and grain size distribution (GSD). Volcanic lightning is a common feature of explosive eruptions with high mass eruption rates of fine particles. The GSD is expected to play a major role in generating lightning in the gas thrust region via triboelectrification. Here, we experimentally investigate the electrical discharges of volcanic ash as a function of varying GSD. We employ two natural materials, a phonolitic pumice and a tholeiitic basalt (TB), and one synthetic material (soda‐lime glass beads [GB]). For each of the three materials, coarse and fine grain size fractions with known GSDs are mixed, and the particle mixture is subjected to rapid decompression. The experiments are observed using a high‐speed camera to track particle‐gas dispersion dynamics during the experiments. A Faraday cage is used to count the number and measure the magnitude of electrical discharge events. Although quite different in chemical composition, TB and GB show similar vent dynamics and lightning properties. The phonolitic pumice displays significantly different ejection dynamics and a significant reduction in lightning generation. We conclude that particle‐gas coupling during an eruption, which in turn depends on the GSD and bulk density, plays a major role in defining the generation of lightning. The presence of fines, a broad GSD, and dense particles all promote lightning.

Volcanic eruptions are energetic events driven by the imbalance of magmatic forces. The magnitudes of these forces remain poorly constrained because they operate in regions that are inaccessible, either underground or dangerous to approach. New techniques are needed to quantify the processes that drive eruptions and to probe magma storage conditions. Here we present X‐ray microdiffraction measurements of volcanic stress imparted as lattice distortions to the crystal cargo of magma from Yellowstone and Long Valley eruptions. Elevated residual stresses between 100 and 300 MPa are preserved in erupted quartz. Multiple volcanic forces could be culpable for the deformation so we analyzed crystals from pyroclastic falls, pyroclastic density currents, and effusive lavas. Stresses are preserved in all quartz but cannot be attributed to differences in eruption style. Instead, lattice deformation likely preserves an in situ measurement of the deviatoric stresses required for the brittle failure of viscous, crystal‐bearing glass during ascent.

Rocks are heterogeneous multiscale porous media: two rock samples with identical bulk properties can vary widely in microstructure. The advent of digital rock technology and modern 3‐D printing provides new opportunities to replicate rocks. However, the inherent trade‐off between imaging resolution and sample size limits the scales over which microstructure and macrostructure can be identified and related to each other. Here, we develop a multiscale digital rock construction strategy by combining X‐ray computed microtomography and focused‐ion beam (FIB)‐scanning electron microscope (SEM) images, and we apply the technique to a tight sandstone. The computed tomography (CT) scanning images characterize macroscale pore structures, while the FIB‐SEM images capture microscale pore textures. The FIB‐SEM images are then coupled to CT images via a template‐matching algorithm and superposition. Bulk properties, including porosity and pore and throat size distribution, can be recovered with this approach. Permeability prediction with a pore network model for the largest connected pore network are 3 orders and 1 order of magnitude greater than the bulk rock measured value using the CT‐only and the SEM‐CT coupled images, respectively.