Unmanned aerial vehicles (UAVs), represented by rotor-based drones, are suitable for volcanic observations owing to the advantages of mobility and safety. In this study, vertical profiles of wind and aerosol concentrations at altitudes up to 1000 m around Mt. Sakurajima, one of the most active volcanoes in Japan, were measured in situ using a drone equipped with an ultrasonic anemometer and aerosol sensor. The drone-measured wind profiles were compared with Doppler LiDAR data and analysis values derived from a meteorological model. Drone-measured vertical profiles collected at a vertical speed of 1 m·s⁻¹ (upward and downward) showed strong agreement with the LiDAR observations, as did the averaged values of hovering drone measurements. Obvious vertical wind shear was found by the drone in the vicinity of Mt. Sakurajima. An aerosol sensor was installed on the drone with the capability to measure fine (PM_2.5) and coarse particles (PM₁₀) simultaneously; in this manner, volcanic ash and aerosol pollutants around the volcano could be distinguished. Thus, it was proven that drones could be applied to investigate wind conditions and aerosols in situ, even at dangerous locations near active volcanoes.

We estimated the vent radius within Showa Crater of Sakurajima volcano from ejection velocity and flow rate of gas‐particle mixtures. The ejection velocity was calculated from video recordings, and the flow rate and volume from infrasound data. Based on the assumption that the vent shape does not change during an explosion, the vent radius was estimated from 201 impulsive Vulcanian eruptions at Showa Crater, yielding values of 6.4–42.3 m (median 23.8 m), which is comparable with the width of fresh lava capping the vent, as photographed from a helicopter. Long‐term changes in vent radius (i.e., over several months) show a relationship with magma accumulation within a reservoir 2–5 km beneath the crater. If the top of the conduit is assumed to be cylindrical, then the vertical extent of the gas‐rich zone is estimated to be 120 m, which may reflect the depth of gas accumulation and buildup of significant overpressure.

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 10⁶ samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [M_j 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities. Graphical Abstract

Mechanisms that control fluctuation of long-term eruptive activity are not well understood. Since 1955, Sakurajima volcano has experienced repeated Vulcanian eruptions accompanied by deflation. However, ground deflations sometimes occurred even if no eruption took place. Characteristics of the non-eruptive deflation (NED) have not been investigated because of the related ambiguous ground vibration, infrasound and visual phenomena. In this study, we categorize deflation events contained in continuous deformation records covering eruptive activities at the Sakurajima volcano Minamidake crater during the period from November 2017 to December 2020. The deflation events are classified into eruptive and NED types by examining the seismic data, infrasound generations, and visual phenomena. Their event type occurrence characteristics are then further clarified based on their relationships to pressure source volume changes. Comparing the pressure source volume changes with the monthly mass of volcanic ash expelled from the crater, which is estimated from deposition data around the volcano, we found the contribution of NED events to volcanic ash mass eruption amounts to be much smaller than that for Vulcanian events, and visual observations confirm that NED events are phenomena that primarily emit volcanic gas. The NED event changes during the episode are interpreted by the magna transition from bubble-rich to bubble-poor conditions and are supported by the sulfur dioxide (SO₂) discharge rate. The activity of NED events is helpful to understand long-term variations in eruptive activity.