Earthquake Swarm Research Articles

Activity characteristics of significant earthquake swarms in the Bohai Rim region

Activity characteristics of significant earthquake swarms in the Bohai Rim region

Stress triggering of the 2022 Lushan–Maerkang earthquake sequence by historical events and its implication for fault stress evolution in eastern Tibet

It remains unknown how stress triggering causes earthquakes in the eastern Tibetan Plateau following the Wenchuan Earthquake in 2008. The MS 6.1 Lushan earthquake on 1 June 2022 in a seismic gap between the 2008 MW 7.9 Wenchuan earthquake and the 2013 MW 6.6 Lushan earthquake provided an opportunity to detect stress evolution and seismic activity on the fault in this study. We calculated the Coulomb stress change of the June 2022 Lushan–Maerkang earthquake sequence using a Burgers viscoelastic model and, herein, discuss how the sequence have been triggered by historical earthquakes since 1900 in the eastern Tibetan Plateau. Our results suggest the following trends: 1) the 1955 M 7.6 Kangding and 2008 MW 7.9 Wenchuan earthquakes contributed most significant loading effects on the 2022 MS 6.1 Lushan earthquake; however, the 2013 MW 6.6 Lushan earthquake had an unloading effect on the 2022 Lushan earthquake. 2) The 2021 MW 7.3 Maduo earthquake contributed a loading effect on the 2022 Lushan earthquake, and the 2022 Lushan earthquake may have triggered the subsequent Maerkang earthquake swarm on 10 June 2022. 3) Viscoelastic relaxation of the lower crust and upper mantle contributed significantly to fault stress level, while a fault in a late-phase earthquake cycle may have ruptured via slight stress perturbation near a cross-border conversion between positive and negative stress from a far-field earthquake. We also provide a seismic potential assessment along the faults in the eastern Tibet. Notably, the MW 6.8 Luding earthquake that ruptured the southern segment of the Xianshuihe Fault on 5 September 2022 supports the conclusions of this study.

Evidencing Fluid Migration of the Crust during the Seismic Swarm by Using 1D Magnetotelluric Monitoring

We applied multi–temporal 1D magnetotelluric (MT) surveys to identify space–time anomalies of apparent resistivity (ρa) in the upper lithosphere in the Antarctic Peninsula (the border between the Antarctic and the Shetland plates). We used time series over several weeks of the natural Earth’s electric and magnetic fields registered at one MT station of the Universidad Nacional de Colombia (RSUNAL) located at Seymour–Marambio Island, Antarctica. We associated resistivity anomalies with contrasting earthquake activity. Anomalies of ρa were detected almost simultaneously with the beginning of a seismic crisis in the Bransfield Strait, south of King George Island (approximately 85.000 events were reported close to the Orca submarine volcano, with focal depths < 20 km and MWW < 6.9). We explained the origin of these anomalies in response to fluid migration near the place of the fractures linked with the seismic swarm, which could promote disturbances of the pore pressure field that reached some hundreds of km away.

A deep Gaussian process model for seismicity background rates

SUMMARYThe spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point processes continue to provide an important tool for analysing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogues. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time-varying seismicity rates, we introduce a deep Gaussian process (GP) formulation for the background rate as an extension to ETAS. GPs are a robust non-parametric model for function spaces with covariance structure. By conditioning the length-scale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently sampled by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Matérn GPs. We illustrate our method using synthetic examples, and show that the deep-GP-ETAS model successfully captures multiscale temporal behaviour in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA 2019 July 5 Mw 7.1 event catalogue, showing that deep-GP-ETAS can successfully characterize a classical aftershock sequence; and the 2016–2019 Cahuilla, CA earthquake swarm, which shows two distinct phases of aseismic forcing concordant with a fluid injection-driven initial sequence, arrest of the fluid along a physical barrier and release following the largest Mw 4.4 event of the sequence.

The 2013–2020 seismic activity at Sabancaya Volcano (Peru): Long lasting unrest and eruption

Sabancaya volcano is the youngest and second most active volcano in Peru. It is part of the Ampato-Sabancaya volcanic complex which sits to the south of the ancient Hualca Hualca volcano and several frequently active faults, thus resulting in complex volcano-tectonic interactions. After 15 years of repose, in 2013, a series of 4 earthquakes with magnitude >4.5 occurred within 24 h, marking the beginning of a new episode of unrest. Several additional swarms of earthquakes occurred in the following years until magmatic eruptive activity started on 6 November 2016. This activity is ongoing as of this writing, with an average of 50 explosions per day. In this study, we present results of multiparametric monitoring of Sabancaya's activity observed during 2013–2020. Seismic data are used to create a one-dimensional seismic velocity model, to catalog, locate, and characterize earthquakes, to detect repeating earthquake families, and to monitor seismic velocity variations by ambient noise cross-correlation. These analyses are complemented by visual and remote sensing observations and ground deformation measurements. All monitored parameters showed significant changes on 6 November 2016, the day of eruption onset, thus dividing the eruptive activity into pre-eruptive and eruptive stages.The unrest is characterized by high levels of seismic activity with hundreds of events detected per day. Volcano-tectonic (VT) earthquakes were dominant during the pre-eruptive period while long-period (LP) events and explosions have been most numerous since the eruption onset. Earthquake locations highlight long-lasting seismogenic zones along multiple previously active regional faults, as well as along newly identified faults. This VT seismicity is mainly distributed in a sector from the northwest to the east of the volcanic complex at distances of up to 30 km from the crater. We focus our analysis on two eruptive episodes: the eruption onset and subsequent crater migration from south to north, and the increase of lava dome extrusion rate in 2019. Both episodes are accompanied by seismic velocity decreases of up to 0.2% and are preceded by a few weeks by bursts of distal VT activity, including numerous repeating earthquakes. These repeated events were located on several remote tectonic faults (5–25 km from the vent). We suggest that these phenomena could be due to the injection of a batch of magma in the deep reservoir and/or conduit, which would generate 1) a pressure wave propagating in the hydrothermal system, triggering the bursts of seismic activity and 2) slow rising of magma by melting old material filling the conduit that eventually produced the eruptive and dome growth acceleration events.

Assessing the potential to detect vertical magma migration using seismicity through the analysis of the seismic network design

Seismic swarms triggered by magma migration at volcanoes often imply an impending eruption. Using Seismic Amplitude Ratio Analysis (SARA) we have a tool to detect these migrations without having to do in-depth analysis of seismograms. This study examines the detection capability of SARA and introduces a new method to quantify it for different seismic network designs for different magma migrations. This is computed using the location of the seismic stations as well as a detection threshold defined using real swarm data. As an example, we applied the analysis to the Piton de la Fournaise seismic network and computed the volume under the volcano where the seismic network is capable to detect vertical migrations. Moreover, we evaluated the impact removing or adding seismic stations has on the detection capability of the network. This method provides a quick and straightforward way for volcano observatories to identify any detection gaps in a network, as well as providing a way to identify the type of migration event a network is most likely to detect.

Seismic swarm intelligence inversion with sparse probability distribution of reflectivity

Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach, such as differential evolution (DE) and particle swarm optimization (PSO). Based on the well logs, the sparse probability distribution (PD) of the reflectivity distribution is spatial stationarity. Therefore, we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity, which helps to provide more accurate potential solutions for the seismic inversion. In the proposed scheme, as two key operations, the creating of probability density function library and probability transformation are inserted into standard SI algorithms. In particular, two targeted DE-PD and PSO-PD algorithms are implemented. Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO. In particular, the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion. Overall, the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

Tomographic evidences for hydraulic fracturing induced seismicity in the Changning shale gas field, southern Sichuan Basin, China

Seismic activity near the shale gas fields in the Sichuan Basin of China has risen significantly in the past few years. Due to the lack of industrial stimulation data to the public, it is difficult to directly link the surge in seismicity near the shale gas fields with hydraulic fracturing stimulation. Meanwhile, it is also challenging to better understand the mechanisms inducing/triggering seismicity in these areas. In the absence of stimulation data, understanding subsurface fluid distribution can go a long way toward addressing these problems. Therefore, in this study, we have applied the double-difference seismic tomography method and the focal mechanism tomography method to the monitoring data of a local seismic array in the Changning shale gas field to build high-resolution three-dimensional models of seismic velocity structure and pore pressure field, then used these models to characterize fluid distributions beneath the shale gas field. From the inverted models, the correlation of overpressure regions (>12 MPa) with moderate-to-high Vp/Vs (1.75-1.90) is clearly observed. With the aid of the exploration data (including well log, active and passive seismic data), the overpressure regions are interpreted to be created by pore pressure or fluid diffusion along pre-existing faults from the shale gas reservoirs, while variations of the Vp/Vs model partly indicate water- and gas-bearing rocks. By jointly analyzing the geophysical model features and earthquake spatio-temporal characteristics, we infer that the earthquakes are primarily caused by increased pore pressures on the seismogenic faults due to the presence of water and/or gas. Besides, the poroelastic stress perturbation and aseismic slip produced by hydraulic fracturing also contribute to the generation of certain earthquake swarms.

Space distribution of the seismic source energy between 1-1-2000 and 3-7-2022 at Campi Flegrei caldera

The spatial distribution of seismic sources is commonly visualized through location maps, where a symbol (commonly a dot or a cross) shows where the earthquake source is located inside the area under study. In the present note I propose a different visualization approach, based on the isoline representation of the Energy Space Density (ESD), which is conceptually similar to approaches already used in literature to represent the pattern of space distribution of strong earthquakes or to spatially identify earthquake swarms. Visualizing such a pattern jointly with the hypocentral distribution map, favors the conceptual association of the Seismic Energy Release to the known geological structures in the area. A test is carried out using the Campi Flegrei seismic catalogue between 2000 and, 2022, period in which the seismic activity in the area resumed after a long calm of several years. The catalogue reports >4000 seismic events with magnitude ranging from 0.5 to 3.5, all located inside a very small volume (5 × 5 × 5 km) and associated with the phenomenon called ”bradiseism”, a slow but intense ground uplift (>80 cm in the 22-year interval spanned by the catalogue). Results indicate that the ESD pattern enhances the visualization of the rock fracture density associated with the ground deformation, in agreement with other studies in the same area, producing a picture of the most fractured rock volumes, possibly associated with fluid storage under the active areas of Solfatara and Pisciarelli.

Analysis of the 2021 Milford, Utah earthquake swarm: Enhanced earthquake catalog and migration patterns

An earthquake swarm occurred in Spring 2021 in south-central Utah near the town of Milford. The University of Utah Seismograph Stations located 125 earthquakes between March 19 and May 10 with magnitudes ranging from 0.5 to 3.2. We implement a matched-filter technique in order to identify additional earthquakes that went undetected during the routine network location. The 125 network-located earthquakes are used as templates and are cross-correlated with continuous data for the dates Feb 17—June 10. This time period corresponds to approximately 1 month before the earthquake swarm began through 1 month after it ended. For the matched-filter analysis, we rely heavily on station FOR1, which is located within 5 km of most template events. Four other stations within 20–30 km of most template events provide a supplement to the closest station. The matched-filter implementation results in the detection of over 600 earthquakes in addition to the original 125 catalog events. This is one of the largest swarms ever recorded in Utah, and no previous large swarms have been recorded in this location. We use HypoDD to obtain relative double-difference locations of the catalog events. Both routine locations and HypoDD relocations of the catalog events suggest a fault dipping west beneath the mountain range, opposite of typical Basin and Range normal faults that dip beneath alluvial valleys. Moment tensors for the largest five events show normal faulting consistent with the west-dipping fault seen in the seismicity. Hydrothermal features in the area, including a geothermal power plant, suggest that fluids may be a contributing factor to the earthquake swarm triggering. We examine the role of fluids by exploring bounds on diffusion parameters and investigating spatial migration characteristics of the swarm seismicity. We conclude that this swarm is the result of heterogenous stress conditions in a prefractured region.

Seismic and aseismic slip during the 2006 Copiapó swarm in North-Central Chile

Earthquake swarms commonly occur along the Chilean subduction zone, witnessing fast seismic and slow aseismic slip behavior at the plate interface. However, the largest seismic swarms observed in Chile, particularly in the Copiapó-Atacama region, remain poorly documented, and the underlying processes have yet to be understood. Here, we perform seismological and geodetic analyses to investigate the 2006 Copiapó swarm, which developed in April and May 2006. The swarm began on April 19, with a magnitude Ml 5.3 earthquake. During the nine following days, we observe a migration of seismicity along the plate interface, the occurrence of doublets events, and a potential slow slip event in the GPS time series at site Copiapó. Then, on April 30, a first earthquake with Mw 6.6 occurred at 15 km depth at the plate contact. It likely triggered a second earthquake of magnitude Mw 6.5, which occurred 144 min later, 10 km northwest of the first earthquake. Using InSAR, we determined the slip distribution associated with these two earthquakes and detailed the postseismic slip they triggered in the next days and weeks. This “postseismic” phase appears to be predominantly aseismic, while the moment released during the “coseismic” phase is comparable to other seismic crises that occurred in Atacama. Although we did not find a larger seismic and aseismic ratio than in other swarms in South America, we suggest a similar mechanism of slow deformation as a driver of seismicity during seismic swarms. Finally, we propose that the slow and fast behavior of the 2006 Copiapó swarm is a consequence of the subduction of the Copiapó Ridge seamounts, which affects both the plate interface and the overriding plate by inducing complex interactions between seismic and aseismic processes.

High resolution upper crustal velocity and seismogenic structure of the Huoshan “seismic window” in the Dabie orogenic belt

Huoshan “seismic window” is located in the northern margin of the Dabie Orogen belt, the contact zone between North China and the Yangtze plate, where the seismic activity is mainly concentrated in this area. Small earthquake swarms in this region often appear before significant moderate-strong earthquakes in East China. To reveal the fine structures of several faults and discuss their correlation with the seismic activity, a seismic array composed of 136 sets of short period seismometers was deployed in the Huoshan region and its surrounding areas. Ambient noise data were recorded for 1 month, and a 3-D velocity structure model of the upper crust was constructed. The results show that with the Meishan-Longhekou and Qingshan-Xiaotian faults as the boundary, the Dabie Mountain, Hefei Basin, North Dabie and North Huaiyang show exhibit obvious high and low velocity variation characteristics. The shallow crust along the NW-trending, normal Qingshan-Xiaotian fault, presents a high velocity zonal distribution, which is related to the large number of magmatic and metamorphic rocks exposed under the strong metamorphic power in this region. The NE-trending Tudiling-Luoerling fault comprised three groups of parallel and nearly vertical en echelon secondary faults in the upper crust, which control most of the seismic activities. A relatively low velocity anomaly is observed in the seismic concentrated occurrence layer near the intersection of the fault. It suggests that this area inherits the property of the low velocity detachment zone of the middle crust in the wing of the Dabie Mountain dome structure, and a relatively broken weak structural zone form near the intersection of the fault, which leads to the release of small strain accumulation, thus enriching small earthquake activities. The focal depth of earthquakes above ML3 mainly distribute along the Tudiling-Luoerling fault plane, which is indicated this fault is the main seismogenic fault of greater seismicity in Huoshan “seismic window”. This study provides an important model basis for reliable earthquake location, focal mechanism and the determination of possible earthquake risk sites, and provides a reference for the further study of small earthquake activity mechanism of other “seismic windows” in China.

Afterslip of the Mw 8.3 2015 Illapel Earthquake Imaged Through a Time‐Dependent Inversion of Continuous and Survey GNSS Data

AbstractWe use continuous and survey GNSS data to image the spatial and temporal evolution of afterslip during the 2 months following the Mw 8.3 2015 Illapel earthquake. Our approach solves for the incremental daily slip at the subduction interface using nonnegative least squares with spatial and temporal Laplacian regularization constraints. We find that afterslip developed at three specific areas at the megathrust, surrounding the coseismic rupture. In addition, well resolved afterslip also occurs within the coseismic rupture area that experienced ∼4 m of seismic slip. Our afterslip model shows striking correlations with the spatial distribution of aftershocks and repeating earthquakes. We capture the local afterslip triggered by a Mw 6.8 and two 6.9 aftershocks that ruptured downdip and north of the coseismic rupture, respectively. The latter ones were possibly triggered by the afterslip that developed north of the rupture. We also find a pulse of enhanced aseismic slip lasting a few days south of the rupture that spatially and temporally correlates with a seismicity burst. We finally find that areas of enhanced afterslip spatially correlates with areas having experienced regular seismic swarms observed during the years prior to the Illapel earthquake. This correlation supports the view of localized fluid high pore pressure areas behaving aseismically and surrounding a highly locked asperity, preventing the seismic rupture to propagate into them.

An Analytic Expression for the Volcanic Seismic Swarms Occurrence Rate. A Case Study of Some Volcanoes in the World

AbstractSeismic swarms are defined as a group of earthquakes occurring very close in time and space but without any distinctively large event triggering their occurrence. Up to now no simple law has been found to describe the swarm occurrence rate. Here we find an expression able to fit the average occurrence rate on some volcanic areas. This expression exhibits some differences in respect to the classical Omori law. Namely the c parameter of the Omori law is equal to zero and the power law decay of the average occurrence rate of the earthquakes is followed by an exponential decaying regime. Both the results can be interpreted in term of fluid injection and/or movements. Indeed this is a more impulsive phenomenon compared to the occurrence of a large earthquake, with a duration compatible with a c = 0. The exponential decay following the power law one could be explained by a viscoelastic relaxation of the stress induced by the injection and/or movements of fluids in the earth crust.

Magmatic plumbing and dynamic evolution of the 2021 La Palma eruption

The 2021 volcanic eruption at La Palma, Canary Islands, was the island’s most voluminous historical eruption. Little is known about this volcano’s feeding system. During the eruption, seismicity was distributed in two clusters at ~10-14 km and ~33-39 km depth, separated by an aseismic zone. This gap coincides with the location of weak seismic swarms in 2017-2021 and where petrological data have implied pre-eruptive magma storage. Here we use seismological methods to understand the seismic response to magma transfer, with 8,488 hypocentral relocations resolving small-scale seismogenic structures, and 156 moment tensors identifying stress heterogeneities and principal axes flips. Results suggest a long-lasting preparatory stage with the progressive destabilisation of an intermediate, mushy reservoir, and a co-eruptive stage with seismicity controlled by the drainage and interplay of two localised reservoirs. Our study provides new insights into the plumbing system that will improve the monitoring of future eruptions in the island.

Southwest Australia Seismic Network (SWAN): Recording Earthquakes in Australia’s Most Active Seismic Zone

AbstractThe geological structure of southwest Australia comprises a rich, complex record of Precambrian cratonization and Phanerozoic continental breakup. Despite the stable continental cratonic geologic history, over the past five decades the southwest of Western Australia has been the most seismically active region in continental Australia, though the reason for this activity is not yet well understood. The Southwest Australia Seismic Network (SWAN) is a temporary broadband network of 27 stations that was designed to both record local earthquakes for seismic hazard applications and provide the opportunity to dramatically improve the rendering of 3D seismic structure in the crust and mantle lithosphere. Such seismic data are essential for better characterization of the location, depth, and attenuation of the regional earthquakes, and hence understanding of earthquake hazard. During the deployment of these 27 broadband instruments, a significant earthquake swarm occurred that included three earthquakes of local magnitude 4.0 and larger, and the network was supplemented by an additional six short-term nodal seismometers at 10 separate sites in early 2022, as a rapid deployment to monitor this swarm activity. The SWAN experiment has been continuously recording since late 2020 and will continue into 2023. These data are archived at the International Federation of Digital Seismograph Networks (FDSN) - recognized Australian Passive Seismic (AusPass) Data center under network code 2P and will be publicly available in 2025.

Hydrogeochemical characteristics and genesis of Hongshuilantang Hot Spring and its water temperature anomalies during the Rushan earthquake swarm in Eastern China

Water temperatures of hot springs close to tectonic fault zones often show some variations before earthquakes, and analyses of earthquake precursors in hot springs have significant referential meaning for earthquake monitoring and forecasting. This study measured the concentration of major ions in water from the Hongshuilantang Hot Spring in 2017 and 2020. The ion composition was classified by hydrochemistry into the HCO3·SO4-Na chemical type. The composition of hydrogen and oxygen isotopes in the Hongshuilantang Hot Spring were located near the global meteoric water line (GMWL), indicating that the recharge source of the hot spring was meteoric water. The δD and δ18O values were not plotted on the Glogal Meteroric Water Line (GMWL), and there were some deviations, which suggested that hot spring water underwent water–rock interactions. Deep circulation water played an important role during the evolution process of thermal water. Water temperature showed a decreasing trend from October 2013 to June 2015 during the Rushan earthquake swarm in eastern China. Because of the occurrence of the earthquake swarm, we inferred that regional stress in this area began to be released, allowing continuous rebalancing. Free surface water appeared in some aquifers, and the seepage of low-temperature underground water into the upper aquifer led to a drop in water temperature in the hot spring. The Hongshuilantang Hot Spring and the epicenter of the Rushan earthquake swarm were located on the Muping–Jimo seismological fault zone, with the same seismotectonic system and some genesis relationships.

Storytelling, language, and the earthquake swarm of May 2018: Insights into Shimaore and Kibushi from narrative analysis

The earthquake swarm that struck Mayotte in 2018 incited panic and various reactions among the local population. Because earthquakes are rare on the island, people were beside themselves, with many leaving their homes to wait out the night under the open air. This article analyzes 36 short narratives that recount events surrounding the crisis. Told in Shimaore and Kibushi, these stories describe what happened at night, particularly when people sought refuge in open spaces, fearing another earthquake might strike. The article focuses on the language used in the stories to discuss earthquakes and the earthquake swarm. Analyses reveal that the stories have a similar structure and language related to space, movement, affect and evaluation are parallel in the two languages. Some differences between the two languages are observed regarding terminology, such as the noun form for “earthquake.” Narratives reveal evaluative language that negatively portrays the events, and the language that stresses the collective experience on the island. L’essaim de séismes qui a frappé Mayotte en 2018 a suscité la panique, la détresse et diverses réactions parmi la population locale. Les tremblements de terre étant une rareté sur l’île, les gens étaient inquiets, beaucoup quittaient leurs maisons pour attendre la nuit à l’air libre. Cet article analyse 36 courts récits qui relatent les événements entourant la crise. Racontées en shimaore et en kibushi, ces histoires décrivent ce qui s’est passé la nuit, en particulier lorsque les gens ont quitté leurs maisons pour se réfugier dans des espaces ouverts, craignant qu’un autre tremblement de terre ne se produise. Dans l’ensemble, les histoires ont une structure similaire, et le langage lié à l’espace, le mouvement, l’affect et l’évaluation sont parallèles dans les deux langues. Certaines différences sont observées concernant la terminologie, comme la forme nominale pour « tremblement de terre » dans les deux langues.

Onset of a submarine eruption east of Mayotte, Comoros archipelago: the first ten months seismicity of the seismo-volcanic sequence (2018–2019)

From 10 May 2018 to 1 November 2022 (time of writing), an unprecedented seismic activity is observed east of Mayotte Island (France), related to the largest submarine eruption ever recorded with offshore geophysical studies. Using signals from regional and local seismic stations, we build a comprehensive catalog of the local seismicity for the first ten months of the sequence. This catalog includes a total of 2874 events of magnitude (Mlv) ranging from 2.4 to 6.0, with 77% of them relocated using a double difference location procedure. The hypocentral locations over this period are highly dependent on the small seismic network available. Therefore we compare the locations of later events using a similar network and those estimated from a local ocean bottom seismometer (OBS) network installed since March 2019. Based on the time space evolution and characteristics of the seismicity, five distinct phases can be identified, corresponding to the successive activation of two deep seismic swarms, related to the lithospheric-scale magma ascent up to the seafloor, along with progressive deepening of the seismicity interpreted as decompression of a 40 km deep reservoir.

Pre-eruption magmatic processes and magma plumbing system at Hachijo-Nishiyama volcano, Izu–Bonin arc, Japan

Nishiyama volcano on Hachijojima Island is an active basaltic volcano located in the Izu–Bonin arc. In this study, petrological and geochemical analyses were conducted on mafic lavas and pyroclastics to understand the magma plumbing system and pre-eruption magmatic processes. Whole-rock major element compositions show significant variations (49.4–54.9 wt.% SiO2), and the samples contain variable amounts of plagioclase phenocrysts (1–40 vol.%). The whole-rock Sr, Nd, and Pb isotopic compositions of samples from the youngest volcanic stage (< 0.7 ka) are homogeneous, whereas some samples from the older stage (3–1 ka) have relatively low Pb isotopic ratios. This observation suggests that the younger magmas were derived from a single parental magma, but another parental magma with distinct geochemical features was involved in the magmatic system before 1 ka. The temporal variation in the FeO*/MgO ratios of the volcanic products is complex and is considered to reflect the intermittent injection of primitive magmas into the main magma chamber in which fractional crystallization occurred. Two-pyroxene geobarometry suggests that the main magma chamber was located at a depth of 9–12 km. The core region of some plagioclase phenocrysts consists of a glass inclusion-free inner core and an inclusion-rich outer mantle, suggesting that some plagioclase crystallized in the main magma chamber, which was followed by overgrowth during magma ascent because of increasing liquidus temperatures due to decompression-induced water exsolution from the melt. The whole-rock compositions of some eruption units with different Al2O3/MgO ratios exhibit distinct plagioclase-controlled trends, which negates the possibility that plagioclase accumulation occurred in a stable magma chamber. In addition, the density of plagioclase was higher than that of the melt during the magma ascent to the surface. From these observations, it is suggested that the accumulation of plagioclase phenocrysts occurred in ascending magmas as the plagioclase settled relative to the surrounding melt. The estimated depth of 9–12 km for the main magma chamber coincides with the depth range over which earthquake swarms occurred in 2002, suggesting that the magma chamber is still active, and that the earthquake swarms may reflect the injection of primitive magma into the magma chamber.Graphical