Forecasting a volcanic eruption involves determining the probability, potential size, and timing of a future event. This relies on detecting patterns of change that indicate magma is moving closer to the surface. Since volcanoes can remain quiet for long periods, scientists first establish a background level of activity. Any deviation from this baseline signals unrest and necessitates a closer look. Forecasting is not based on a single measurement but on interpreting a combination of physical, chemical, and thermal data streams.
Tracking Ground Deformation
The movement of magma into a volcano’s shallow plumbing system causes the ground surface to bulge, tilt, or shift, a process known as ground deformation. This physical change is often one of the earliest signs of unrest, resulting from the injection of new magma or the buildup of pressure in a magma chamber. Detecting these subtle movements requires a suite of sensitive geodetic instruments.
Global Navigation Satellite System (GNSS) receivers are installed across a volcano’s flanks to measure both horizontal and vertical ground displacement in near-real time. By comparing the receiver’s location to a stable reference point, volcanologists track uplift or swelling, which indicates an inflating magma reservoir. These ground-based measurements provide high temporal resolution, showing changes as they occur.
Tiltmeters, sensitive instruments placed in boreholes, measure minute changes in the slope or inclination of the ground surface. They are effective for monitoring shallow magma systems, detecting small-scale tilting. They provide a dense temporal record of how the volcano’s structure reacts to pressure changes.
For a broader spatial view, scientists use Interferometric Synthetic Aperture Radar (InSAR), a satellite-based technique that maps surface changes over time. Satellites emit radar signals and record the returning waves, detecting deformation over large areas with centimeter-level precision. InSAR is useful for identifying broad areas of uplift or subsidence and monitoring flank stability, complementing the point measurements from GNSS and tiltmeters.
Monitoring Seismic Activity
Magma movement and the resulting stress generate distinct types of earthquakes and continuous ground vibrations measured by seismometers. The rate, depth, and type of these seismic events are often more informative than magnitude alone, signaling changes in the subsurface magmatic system. Seismometers are placed across the volcano to listen for these acoustic signatures of unrest.
Volcano-Tectonic (VT) earthquakes occur when pressure from rising magma causes the brittle, overlying rock to fracture. An increasing number of VT quakes, especially if they become shallower, suggests that magma is forcing its way toward the surface. These events are characterized by an impulsive onset, indicating a sudden break.
A different event, known as a Long-Period (LP) or Low-Frequency (LF) earthquake, is caused by the resonance of cracks as magma and volcanic fluids move through them. These events have a gradual onset and directly indicate fluid dynamics within the volcano’s conduit system. The occurrence of LP events is a strong precursor, linked directly to the magmatic system.
A continuous vibration called harmonic tremor often precedes or accompanies an eruption. This long-duration seismic signal is generated by the rapid, continuous flow of magma or volcanic gases through narrow conduits. The presence and increasing amplitude of harmonic tremor suggests a sustained movement of material at depth, serving as a clear warning sign.
Analyzing Chemical and Thermal Signatures
Changes in the chemistry and temperature of gases escaping from a volcano provide direct evidence about the state of the magma body. As magma rises, it releases volatile gases that filter through the rock, revealing how close the magma is to the surface. Gas emissions are monitored from fumaroles (vents that release steam and gases) and from the broader plume.
Key gases monitored include Sulfur Dioxide (\(\text{SO}_2\)), Carbon Dioxide (\(\text{CO}_2\)), and Hydrogen Sulfide (\(\text{H}_2\text{S}\)). A significant increase in the total emission rate of \(\text{SO}_2\) indicates that magma has reached a relatively shallow depth. Changes in the ratios between different gases, such as the \(\text{CO}_2\)/\(\text{SO}_2\) ratio, provide insight into the degassing process and magma ascent.
Thermal monitoring, often using satellite or drone-based thermal imaging, detects temperature anomalies on the volcano’s surface. New hot spots or an increase in temperature within existing fumaroles indicate that hotter magmatic gases are being released. Scientists measure the Volcanic Radiative Power (VRP), a proxy for the heat radiated by the vent flux zone.
Changes in the chemistry of local water sources, such as springs and crater lakes, also contribute to the chemical picture. The input of magmatic gases can alter the water’s acidity and temperature. Regularly sampling these water bodies detects subtle shifts in composition that signal magmatic unrest below the water table.
Integrating Data for Eruption Forecasting
No single data stream can definitively predict an eruption; instead, scientists rely on the convergence of multiple signals across different monitoring networks. The simultaneous increase in seismic activity, accelerating ground swelling, and a sharp rise in \(\text{SO}_2\) emissions create a pattern of unrest that points toward a higher probability of eruption. This multiparameter approach is necessary for developing a robust forecast.
The interpretation of this integrated data leads to the assignment of a Volcanic Alert Level, a standardized system that communicates the volcano’s current state to the public and civil authorities. These levels (e.g., Green, Yellow, Orange, Red) reflect the collective assessment of current phenomena. Forecasting is not about pinpointing an exact time, but about continually updating the probability of an eruption and likely scenarios over short time scales.
The goal of this data synthesis is to provide a short-term warning, allowing for preparations and evacuations to minimize risk. Advanced models, including those using machine learning, are trained on combined seismic and geodetic data to uncover subtle trends that enhance warning reliability. Since pre-eruption behaviors vary widely, forecasting remains a continuous process of observation and re-evaluation.