Time Dependence of Passive Degassing at Volcán Popocatépetl, Mexico, From Infrared Measurements: Implications for Gas Pressure Distribution and Lava Dome Stability

Abstract

The transport and storage of gas within lava domes is critical to their stability or explosivity. To analyze gas flow through these systems, degassing from Volcán Popocatépetl, Mexico, is studied here. Short‐duration (hours) passive degassing at Popocatépetl is dominated by oscillating components with periods between 100 and 1,000 s in agreement with similar measurements across a range of other volcanic systems. Over these timescales, porous gas flow through an idealized dome obeys a nonlinear diffusion equation. Approximate solutions contain series of diffusive pressure waves which decay to a steep boundary layer near the dome surface. Wave behavior is governed by the oscillation period and dome rock permeability, acting as a low‐pass filter on flux oscillations. This may explain the weakness of higher‐frequency (>0.01 Hz) oscillations in the Popocatépetl degassing data. These waves are shown to significantly modify the dome effective strength. For ∼100‐m‐radius domes, the pore pressure is comparable to the overburden, and the total strength of the dome is dominated by tensile strength. The pressure in smaller domes (≪100 m) dominates and may induce failure. This may explain observations of extrusion and destruction cycling during dome‐producing eruptions. The difference between pore pressure and overburden exhibits a shallow (outermost 5–25%), high‐pressure layer which changes in size, shape, and overpressure magnitude throughout oscillations. Linear governing equations assumed by previous studies underestimate the pore pressure distribution and do not capture this shallow layer. Recognition of this layer allows for better understanding of the destabilization or explosion of pressurized lava domes.

A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements

“…Additional error is incurred due to the highly unsteady nature of volcanic emissions. Compared with anthropogenic sources, volcanic outgassing is highly variable, varying by orders of magnitude on a variety of timescales e.g., [31][32][33][34][35][36]. In our simple SO 2 dispersion model, the fitting solution is the result of time integration of a dense sequence of discrete puffs, which is at least somewhat accurate to nature; however, error is incurred whenever the time delay between a new discrete puff and the next satellite overpass is maximized, that is when a discrete puff occurs just after an overpass.…”

Probabilistic Enhancement of the Failure Forecast Method Using a Stochastic Differential Equation and Application to Volcanic Eruption Forecasts

“…However, the frequent dome collapse events between February and April, 1990 (about fifteen events in 112 days) during nearly continuous exogenous dome building at low extrusion rate were associated with erratic and short-lived seismic trends (Cornelius and Voight, 1994), and forecasting exclusively based on RSAM would have been misleading. Instead, inverse Real-time Seismic Spectral Amplitude Measurement (SSAM) plots would have been informative for early detection of long-period seismicity of low energy content, typical for this type of eruption (Hyman et al, 2018).…”

Lava dome cycles reveal rise and fall of magma column at Popocatépetl volcano

“…The associated volcanic hazards, of both exogenously and endogenously growing domes, are highly dependent on the resulting dome morphology 6 , as instabilities may trigger rockfalls and collapses, potentially generating deadly pyroclastic density currents 7 , 8 . Moreover, rapid changes in the dome permeability can lead to sudden transitions from passive degassing to violent explosive events 9 – 13 . Due to the hazardous and unpredictable nature of growing lava domes, the study of dome morphology and extrusion dynamics have been essentially approached through modeling (analog 2 , 3 , 14 or numerical 15 , 16 ) and remote sensing, including photogrammetry from ground- 17 , 18 , air- 19 , 20 , and satellite-based sensors using both optical and infrared bands 21 – 24 .…”