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Abstract A prolonged earthquake swarm has persisted since June 2018 in northeastern Noto Peninsula (central Japan), with activity focused into distinct southern, western, northern, and eastern clusters. To explore the role of fluids in the occurrence of this swarm, we analyzed the focal mechanisms of the earthquakes occurring from 1 January 2018 to 30 November 2022 and performed stress tensor inversions. The western, northern, and eastern clusters were dominated by a reverse fault-type mechanism with a horizontal P-axis oriented NW–SE. One of the nodal planes of those mechanisms aligns closely with the precisely relocated hypocenter distribution. The stress fields in these three clusters, as determined by stress tensor inversion, have maximum principal stresses oriented horizontally in the NW–SE direction and minimum principal stresses oriented vertically, aligning with the regional stress field. From these focal mechanisms and this stress field, we derived small misfit angles and large slip tendencies. These findings suggest that, in these three clusters, fluids diffused into faults largely aligned with the regional stress field, resulting in earthquakes with compatible focal mechanisms. Conversely, normal and strike-slip fault-type focal mechanisms, which are unfavorable to the regional stress field, dominated in the southern cluster. The estimated stress fields, deviating from the regional stress field, have maximum principal stresses closer to vertical and minimum principal stress oriented horizontally in the ENE–WSW direction. For earthquakes deeper than 15 km, this local stress field results in relatively larger misfit angles and lower slip tendency than the other clusters. These results suggest that those earthquakes in the southern cluster occurred on misoriented fault planes due to elevated pore fluid pressures. These findings provide strong evidence of the ascent of high-pore-pressure fluids from depth in the southern cluster and their subsequent diffusion into a southeast-dipping fault zone. Graphical Abstract

Abstract Recent studies have proposed the contribution of aseismic slip (AS) to earthquake swarms. We investigated the role of AS in earthquake swarms that occurred in 2009, 2015, and 2019 at the Hakone volcano, central Japan, through highly resolved hypocenter distribution analysis, geodetic observation analysis, and identification of similar earthquakes. We observed diffusion-like migration of hypocenters during these swarms. The hydraulic diffusivity varied among the swarms, indicating differing dynamics. The 2015 swarm exhibited rapid hypocenter migration and significant crustal deformation, as revealed by the temporal sequences of tiltmeters near the swarm region. Right-lateral shear dislocation on fault planes could explain the crustal deformation observed in 2015, indicating that AS released approximately 90% of the moment. However, the 2009 swarm lacked evidence of significant AS contribution, indicating that the primary mechanism was fluid pressure diffusion. The substantial contribution of AS to the 2015 swarm might be attributed to increased fluid pressure due to the intrusion of hydrothermal fluid into the shallow part beneath the volcano during volcanic unrest. Our findings imply that the temporal and spatial patterns of seismicity can provide valuable insights into the underlying mechanics of earthquake swarms. Graphical abstract

Abstract Volcanic earthquakes provide essential information for evaluating volcanic activity. Because volcanic earthquakes are often characterized by swarm-like features, conventional methods using manual picking require considerable time to construct seismic catalogs. In this study, using a machine learning framework and a trained model from a volcanic earthquake catalog, we obtained a detailed picture of volcanic earthquakes during the past 12 years at the Kirishima volcano, southwestern Japan. We detected ~ 6.2 times as many earthquakes as a conventional seismic catalog and obtained a high-resolution hypocenter distribution through waveform correlation analysis. Earthquake clusters were estimated below the craters, where magmatic or phreatic eruptions occurred in recent years. Increases in seismic activities, b values, and the number low-frequency earthquakes were detected before the eruptions. The process can be conducted in real time, and monitoring volcanic earthquakes through machine learning methods contributes to understanding the changes in volcanic activity and improving eruption predictions. Graphical Abstract

Abstract Decades ago, Julian (J Volcanol Geotherm Res 101:19–26, 1994. https://doi.org/10.1029/93JB03129 ) proposed the lumped parameter model of non-linear excitation of an elastic channel vibration by fluid flow as a mechanism of volcanic harmonic tremor. Since then, his model and similar flow-induced oscillation models have been applied or considered to explain volcanic tremors and low-frequency earthquakes. Here we extended Julian’s model to allow quantitative comparison with observation data and applied it to deep harmonic tremor observed at Hakone volcano, Japan. We formulated the model in terms of the channel volume and linked the solution to the volumetric moment tensor. We also incorporated the turbulent flow effect to deal with both magma and super-critical fluid as the working fluid. Assuming the realistic material parameters at the tremor source depth ( $$\sim$$ ∼ 30 km) beneath Hakone, we searched for the conditions in which tremor was generated at an observed frequency ( $$\sim$$ ∼ 1 Hz). It is shown that both magma and super-critical fluids can generate realistic tremors with similar channel sizes of several-meter long and several-centimeter wide. We convolved the model solution with the Green’s function at each seismic station to compare the model with the data. The result showed that Julian’s model could produce synthetic tremor waveforms very close to the observed ones. Although the source waveform had only a single peak at each cycle, the convolved waveform exhibited an apparent secondary peak, like the observed waveforms. While the previous models generated such waveforms exhibiting alternative large and small peaks by a non-linear effect of period-doubling before the chaos, our model did not show such transitions, at least with the investigated parameters. Although most of the parameters and physical values of the solutions were in the realistic ranges, the only problem was the presumed low elasticity of the channel as small as $$10^5$$ 10 5 Pa to generate oscillation at $$\sim$$ ∼ 1 Hz. We proposed that not the rock property alone but the channel structure consisting of rock and compressible fluids could generate the low effective elasticity. To fully validate our model, the mechanism of such small elasticity should be identified, which is our future work. Graphical Abstract

Abstract In volcanic regions, active earthquake swarms often occur in association with volcanic activity, and their rapid detection and analysis are crucial for volcano disaster prevention. Currently, these processes are ultimately left to human judgment and require significant time and money, making detailed real-time verification impossible. To overcome this issue, we attempted to apply machine learning, which has been successfully applied to various seismological fields to date. For seismic phase pick, several models have already been trained using a large amount of training data (mainly crustal earthquakes). Although there are some cases in which these models can be applied without any problems, regional dependence on pre-trained models has been reported. Since this study targets earthquakes in a volcanic region, applying existing pre-trained models may be difficult. Therefore, in this study, we compared three models; the publicly available trained model (model 0), a model which was trained with approximately 220,000 P- and S-wave onset reading data recorded at the Hakone volcano from 1999 to 2020 with initialized parameters (model 1) using the same architecture, and a model fine-tuned with the aforementioned Hakone data using the parameters of model 0 as initial values (model 2), and evaluated their phase identification performance for the Hakone data. As a result, the seismic phase detection rates of models 1 and 2 were much higher than those of model 0. However, small-amplitude signals are often missed when multiple seismic events occur within a detection time window. Therefore, we created training data with two earthquakes in the same time window, retrained the model using the data, and successfully detected events that previously would have been missed. In addition, it was found that more events were detected by setting the threshold to a low probability value for detection, increasing the number of seismic phase detections, and filtering by phase association and hypocenter location. Graphical Abstract

Abstract The feeding system of magmatic fluid from the volcanic root to a shallow magma reservoir remains a poorly understood issue. Seismic events, including volcanic tremors and low-frequency earthquakes, in a deep part beneath volcanos are key observations for understanding the feeding system at the depth. Although deep low-frequency (DLF) earthquakes beneath volcanos have been recognized universally through dense seismic observations, volcanic tremors with harmonic frequency components originating at volcanic roots have rarely been observed. Here, we report the observation of a harmonic volcanic tremor event that occurred beneath the Hakone volcano on May 26, 2019. The tremor signal continued for approximately 10 min and was recognized at seismic stations 90 km away from the Hakone volcano. The apparent velocity of the tremor wave train is 5 km/s, corresponding to the S-wave velocity of the lower crust beneath the Hakone volcano. The frequency components varied with time. In the initial part of the tremor signal, a spectrum had a broad peak of around 1.2 Hz, whereas the tremor became harmonic with a sharp fundamental peak at 0.98 Hz in the latter part, increasing its amplitude. We estimated the source location of the volcanic tremor using the relative arrival times of the waveform envelope. The optimal source locations were estimated at a deep extension of the hypocenter distribution of the DLF earthquakes beneath the Hakone volcano, around the depth level of Moho discontinuity. The DLF earthquakes were activated immediately before the onset time of the volcanic tremor and continued for several months. The harmonic volcanic tremor may have been generated by the migration of magmatic fluid in the volcano’s deep region. Graphical Abstract

Abstract In the present study, we propose a new approach for determining earthquake hypocentral parameters. This approach integrates computed theoretical seismograms and deep machine learning. The theoretical seismograms are generated through a realistic three-dimensional Earth model, and are then used to create spatial images of seismic wave propagation at the Earth’s surface. These snapshots are subsequently utilized as a training data set for a convolutional neural network. Neural networks for determining hypocentral parameters such as the epicenter, depth, occurrence time, and magnitude are established using the temporal evolution of the snapshots. These networks are applied to seismograms from the seismic observation network in the Hakone volcanic region in Japan to demonstrate the suitability of the proposed approach for locating earthquakes. We demonstrate that the determination accuracy of hypocentral parameters can be improved by including theoretical seismograms for different earthquake locations and sizes, in the learning data set for the deep machine learning. Using the proposed method, the hypocentral parameters are automatically determined within seconds after detecting an event. This method can potentially serve in monitoring earthquake activity in active volcanic areas such as the Hakone region.

Abstract Since the beginning of the twenty-first century, volcanic unrest has occurred every 2–5 years at Hakone volcano. After the 2015 eruption, unrest activity changed significantly in terms of seismicity and geochemistry. Like the pre- and co-eruptive unrest, each post-eruptive unrest episode was detected by deep inflation below the volcano (~ 10 km) and deep low frequency events, which can be interpreted as reflecting supply of magma or magmatic fluid from depth. The seismic activity during the post-eruptive unrest episodes also increased; however, seismic activity beneath the eruption center during the unrest episodes was significantly lower, especially in the shallow region (~ 2 km), while sporadic seismic swarms were observed beneath the caldera rim, ~ 3 km away from the center. This observation and a recent InSAR analysis imply that the hydrothermal system of the volcano could be composed of multiple sub-systems, each of which can host earthquake swarms and show independent volume changes. The 2015 eruption established routes for steam from the hydrothermal sub-system beneath the eruption center (≥ 150 m deep) to the surface through the cap-rock, allowing emission of super-heated steam (~ 160 ºC). This steam showed an increase in magmatic/hydrothermal gas ratios (SO2/H2S and HCl/H2S) in the 2019 unrest episode; however, no magma supply was indicated by seismic and geodetic observations. Net SO2 emission during the post-eruptive unrest episodes, which remained within the usual range of the post-eruptive period, is also inconsistent with shallow intrusion. We consider that the post-eruptive unrest episodes were also triggered by newly derived magma or magmatic fluid from depth; however, the breached cap-rock was unable to allow subsequent pressurization and intensive seismic activity within the hydrothermal sub-system beneath the eruption center. The heat released from the newly derived magma or fluid dried the vapor-dominated portion of the hydrothermal system and inhibited scrubbing of SO2 and HCl to allow a higher magmatic/hydrothermal gas ratio. The 2015 eruption could have also breached the sealing zone near the brittle–ductile transition and the subsequent self-sealing process seems not to have completed based on the observations during the post-eruptive unrest episodes.

Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.

Abstract The 2015 unrest of the Hakone volcano in Japan, which began on April 26, generated earthquake swarms accompanied by long-term deformation. The earthquake swarm activity reached its maximum in mid-May and gradually calmed down; however, it increased again on the morning of June 29, 2015. Simultaneously with the earthquake increase, rapid tilt changes started 10 s before 07:33 (JST) and they lasted for approximately 2 min. The rapid tilt changes likely reflected opening of a shallow crack that was formed near the eruption center prior to the phreatic eruption on that day. In this study, we modeled the pressure source beneath the eruption center based on static tilt changes determined using both tilt meters and broadband seismometers. In the best-fit model, the source depth was 854 m above sea level, and its orientation (N316°E) agreed with the direction of maximum compression estimated based on focal mechanism and S-wave splitting data. The extent of the crack opening was estimated to be 4.6 cm, while the volume change was approximately 1.6 × 105 m3. The top of the crack reached to approximately 150 m below the eruption center. Because the crack was too thin to be penetrated by magma, the crack opening was attributed to the intrusion of hydrothermal water. This intrusion of hydrothermal water may have triggered the phreatic eruption. Reverse polarity motion with respect to that expected from crack opening was recognized in 1 Hz tilt records during the first 20 s of the intrusion of hydrothermal water. This motion, not the subsidence of volcanic edifice, was responsible for the observed displacement.

Abstract On 29 June 2015, a small phreatic eruption occurred at Hakone volcano, Central Japan, forming several vents in the Owakudani geothermal area on the northern slope of the central cones. Intense earthquake swarm activity and geodetic signals corresponding to the 2015 eruption were also observed within the Hakone caldera. To complement these observations and to characterise the shallow resistivity structure of Hakone caldera, we carried out a three-dimensional inversion of magnetotelluric measurement data acquired at 64 sites across the region. We utilised an unstructured tetrahedral mesh for the inversion code of the edge-based finite element method to account for the steep topography of the region during the inversion process. The main features of the best-fit three-dimensional model are a bell-shaped conductor, the bottom of which shows good agreement with the upper limit of seismicity, beneath the central cones and the Owakudani geothermal area, and several buried bowl-shaped conductive zones beneath the Gora and Kojiri areas. We infer that the main bell-shaped conductor represents a hydrothermally altered zone that acts as a cap or seal to resist the upwelling of volcanic fluids. Enhanced volcanic activity may cause volcanic fluids to pass through the resistive body surrounded by the altered zone and thus promote brittle failure within the resistive body. The overlapping locations of the bowl-shaped conductors, the buried caldera structures and the presence of sodium-chloride-rich hot springs indicate that the conductors represent porous media saturated by high-salinity hot spring waters. The linear clusters of earthquake swarms beneath the Kojiri area may indicate several weak zones that formed due to these structural contrasts.

Abstract The 2015 eruption of Hakone volcano was a very small phreatic eruption, with total erupted ash estimated to be in the order of only 102 m3 and ballistic blocks reaching less than 30 m from the vent. Precursors, however, had been recognized at least 2 months before the eruption and mitigation measures were taken by the local governments well in advance. In this paper, the course of precursors, the eruption and the post-eruptive volcanic activity are reviewed, and a preliminary model for the magma-hydrothermal process that caused the unrest and eruption is proposed. Also, mitigation measures taken during the unrest and eruption are summarized and discussed. The first precursors observed were an inflation of the deep source and deep low-frequency earthquakes in early April 2015; an earthquake swarm then started in late April. On May 3, steam wells in Owakudani, the largest fumarolic area on the volcano, started to blowout. Seismicity reached its maximum in mid-May and gradually decreased; however, at 7:32 local time on June 29, a shallow open crack was formed just beneath Owakudani as inferred from sudden tilt change and InSAR analysis. The same day mud flows and/or debris flows likely started before 11:00 and ash emission began at about 12:30. The volcanic unrest and the eruption of 2015 can be interpreted as a pressure increase in the hydrothermal system, which was triggered by magma replenishment to a deep magma chamber. Such a pressure increase was also inferred from the 2001 unrest and other minor unrests of Hakone volcano during the twenty-first century. In fact, monitoring of repeated periods of unrest enabled alerting prior to the 2015 eruption. However, since open crack formation seems to occur haphazardly, eruption prediction remains impossible and evacuation in the early phase of volcanic unrest is the only way to mitigate volcanic hazard.

Abstract To understand the initial process of the phreatic eruption of the Hakone volcano from June 29 to July 01, 2015, we analyzed infrasound data using the cross-correlation between infrasound and vertical ground velocity and compared the results of our analysis to the crustal deformation detected by tiltmeters and broadband seismometers. An infrasound signal and vertical ground motion due to an infrasound wave coupled to the ground were detected simultaneously with the opening of a crack source beneath the Owakudani geothermal region during the 2-min time period after 07:32 JST on June 29, 2015 (JST = UTC + 8 h). Given that the upper end of the open crack was approximately 150 m beneath the surface, the time for the direct emission of highly pressurized fluid from the upper end of the open crack to the surface should have exceeded the duration of the inflation owing to the hydraulic diffusivity in the porous media. Therefore, the infrasound signal coincident with the opening of the crack may reflect a sudden emission of volcanic gas resulting from the rapid vaporization of pre-existing groundwater beneath Owakudani because of the transfer of the volumetric strain change from the deformation source. We also noticed a correlation pattern corresponding to discrete impulsive infrasound signals during vent formation, which occurred several hours to 2 days after the opening of the crack. In particular, we noted that the sudden emission of vapor coincided with the inflation of the shallow pressure source, whereas the eruptive burst events accompanied by the largest vent formation were delayed by approximately 2 days. Furthermore, we demonstrated that the correlation method is a useful tool in detecting small infrasound signals and provides important information regarding the initial processes of the eruption.

Abstract In the present study, we analyze the seismic signals from a continuous volcanic tremor that occurred during a small phreatic eruption of the Hakone volcano, in the Owakudani geothermal region of central Japan, on June 29, 2015. The signals were detected for 2 days, from June 29 to July 1, at stations near the vents. The frequency component of the volcanic tremors showed a broad peak within 1–6 Hz. The characteristics of the frequency component did not vary with time and were independent of the amplitude of the tremor. The largest amplitude was observed at the end of the tremor activity, 2 days after the onset of the eruption. We estimated the location of the source using a cross-correlation analysis of waveform envelopes. The locations of volcanic tremors are determined near the vents of eruption and the surface, with the area of the upper extent of an open crack estimated using changes in the tilt. The duration-amplitude distribution of the volcanic tremor was consistent with the exponential scaling law rather than the power law, suggesting a scale-bound source process. This result suggests that the volcanic tremor originated from a similar physical process occurring practically in the same place. The increment of the tremor amplitude was coincident with the occurrence of impulsive infrasonic waves and vent formations. High-amplitude seismic phases were observed prior to the infrasonic onsets. The time difference between the seismic and infrasonic onsets can be explained assuming a common source located at the vent. This result suggests that both seismic and infrasonic waves are generated when a gas slug bursts at that location. The frequency components of the seismic phases observed just before the infrasonic onset were generally consistent with those of the tremor signals without infrasonic waves. The burst of a gas slug at the surface vent may be a reasonable model for the generation mechanism of the volcanic tremor and the occurrence of impulsive infrasonic signals.

Abstract In order to clarify the origin of aftershocks, we precisely analyze the hypocenters and focal mechanisms of the aftershocks following the 2000 Western Tottori Earthquake, which occurred in the western part of Japan, using data from dense seismic observations. We investigate whether aftershocks occur on the mainshock fault plane on which coseismic slip occurred or they represent the rupture of fractures surrounding the mainshock fault plane. Based on the hypocenter distribution of the aftershocks, the subsurface fault structure of the mainshock is estimated using principal component analysis. As a result, we can obtain the detail fault structure composed of 8 best-fit planes. We demonstrate that the aftershocks around the mainshock fault are distributed within zones of 1.0–1.5 km in thicknesses, and their focal mechanisms are significantly diverse. This result suggests that most of the aftershocks represent the rupture of fractures surrounding the mainshock fault rather than the rerupture of the mainshock fault. The aftershocks have a much wider zone compared with the exhumed fault zone in field observations, suggesting that many aftershocks occur outside the fault damage zone. We find that most aftershocks except in and around the large-slip region are well explained by coseismic stress changes. These results suggest that the thickness of the aftershock distribution may be controlled by the stress changes caused by the heterogeneous slip distribution during the mainshock. The aftershock is also distributed within a much wider zone than the hypocenter distribution observed in swarm activity in the geothermal region, which is thought to be caused by the migration of hydrothermal fluid. This result implies a difference in generation processes: Stress changes due to the mainshock contribute primarily to the occurrence of aftershocks, whereas earthquake swarms in the geothermal region are caused by fluid migration within the localized zone. Graphical abstract .