Seismic Precursors to the Whakaari 2019 Phreatic Eruption detected in Five Other Volcanoes

Volcanic eruptions that occur without warning can be deadly in touristic and populated areas. Even with real-time geophysical monitoring, forecasting sudden eruptions is difficult because their precursors are hard to recognize and can vary between volcanoes. Here, we describe a general seismic precursor signal for gas-driven eruptions, identified through correlation analysis of 18 well-recorded eruptions in New Zealand, Alaska and Kamchatka. We show that the displacement seismic amplitude ratio, a ratio between high and medium frequency volcanic tremor, has a characteristic rise in the days prior to eruptions that likely indicates formation of a hydrothermal seal that enables rapid pressurization. Applying this model to the fatal 2019 eruption at Whakaari (New Zealand), we identify pressurization in the week before the eruption, and cascading seal failure in the 16 hours prior to the explosion. This method for identifying and proving generalizable eruption precursors can help improve short term forecasting systems.

Fast Identification of Volcanic Tremor and Lahar Signals during the 2009 Redoubt Eruption Using Permutation Entropy and Supervised Machine Learning

Despite their usefulness for volcano monitoring, emergent seismic signals, such as volcanic tremor or signals generated by lahars, are difficult to identify with confidence in a timely fashion. Machine-learning algorithms offer an objective alternative to traditional methods of identifying such volcanoseismic signals, because they are able to handle quickly large amounts of data, while requiring little input from the user. In this work, we combine permutation entropy and centroid as well as dominant frequency with supervised machine learning to evaluate their potential in identifying volcanic tremor and lahar signals recorded during the 2009 Redoubt volcano eruption. The particular dataset was chosen for the reason that the properties and occurrence times of the volcanoseismic signals during the eruption are well known from previous studies. We find that the selected features can effectively discriminate both types of signals against the seismic background, especially for stations that are near the source. Results show that the identification success rate for volcanic tremor reaches up to 96%, whereas this rate becomes up to 91% for lahar signals. The calculation of the features as well as the application of the machine-learning algorithms is fast, allowing their implementation in the operational environment of a volcano observatory during a volcanic crisis. Finally, the proposed methodology can potentially be used to objectively identify other emergent seismic signals such as tectonic tremor along subduction zones, glacial tremor, or seismic signals generated during landslides.

A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

The Whakaari/White Island volcano, located ~ 50 km off the east coast of the North Island in New Zealand, has experienced sequences of quiescence, unrest, magmatic and phreatic eruptions over the last decades. For the last 15 years, seismic data have been continuously archived providing potential insight into this frequently active volcano. Here we take advantage of this unusually long time series to retrospectively process the seismic data using ambient noise and tremor-based methodologies. We investigate the time (RSAM) and frequency (Power Spectral Density) evolution of the volcanic tremor, then estimate the changes in the shallow subsurface using the Displacement Seismic Amplitude Ratio (DSAR), relative seismic velocity (dv/v) and decorrelation, and the Luni-Seismic Correlation (LSC). By combining our new set of observations with the long-term evolution of earthquakes, deformation, visual observations and geochemistry, we review the activity of Whakaari/White Island between 2007 and the end of 2018. Our analysis reveals the existence of distinct patterns related to the volcano activity with periods of calm followed by cycles of pressurization and eruptions. We finally put these results in the wider context of forecasting phreatic eruptions using continuous seismic records.

Volcanic tremor of the 2010 Eyjafjallajökull eruption

Summary Volcanic eruptions in Iceland generally start with an increase in tremor levels. These signals do not have clear onset, like many earthquakes. As the character of the tremor signal is variable from one volcano to another, locating the source of the tremor signal may require different techniques for different volcanoes. Continuous volcanic tremor varied considerably during the course of the Eyjafjallajökull summit eruption, April 14th to May 22nd 2010, and was clearly associated with changes in eruptive style. The tremor frequencies ranged between 0.5 and 10 Hz, with increased vigour during an effusive and explosive phase, in comparison with purely explosive phases. Higher-frequency tremor bursts early in the eruption were caused by processes at the eruption site. Location of the tremor using a method based on differential phase information extracted from inter-station correlograms showed the tremor to be stable near the eruption vent, through time, for signals between 0.5 Hz and 2 Hz. Analyses of power variations of the vertical component of the tremor with distance from the eruption site are consistent with tremor waveform content being dominated by surface waves in the 0.5-2 Hz frequency range. The tremor source depth was argued to be shallow, less than about 1 km. The attenuation quality factor (Q) was found to be on the order of Q = 10-20 for paths in the area around Eyjafjallajökull and Q = 20-50 for paths outside the volcano. The pattern of radiated wave energy from the tremor source varied with time, defining ten different epochs during the eruption. Thus the tremor-source radiation did not remain isotropic, which needs to be considered when locating tremor based on amplitude, i.e. azimuthally variable source radiation.

Seismo-acoustic characterisation of the 2018 Ambae (Manaro Voui) eruption, Vanuatu

A new episode of unrest and phreatic/phreatomagmatic/magmatic eruptions occurred at Ambae volcano, Vanuatu, in 2017–2018. We installed a multi-station seismo-acoustic network consisting of seven 3-component broadband seismic stations and four 3-element (26–62 m maximum inter-element separation) infrasound arrays during the last phase of the 2018 eruption episode, capturing at least six reported major explosions towards the end of the eruption episode. The observed volcanic seismic signals are generally in the passband 0.5–10 Hz during the eruptive activity, but the corresponding acoustic signals have relatively low frequencies (< 1 Hz). Apparent very-long-period (< 0.2 Hz) seismic signals are also observed during the eruptive episode, but we show that they are generated as ground-coupled airwaves and propagate with atmospheric acoustic velocity. We observe strongly coherent infrasound waves at all acoustic arrays during the eruptions. Using waveform similarity of the acoustic signals, we detect previously unreported volcanic explosions at the summit vent region based on constant-celerity reverse-time-migration (RTM) analysis. The detected acoustic bursts are temporally related to shallow seismic volcanic tremor (frequency content of 5–10 Hz), which we characterise using a simplified amplitude ratio method at a seismic station pair with different distances from the vent. The amplitude ratio increased at the onset of large explosions and then decreased, which is interpreted as the seismic source ascent and descent. The ratio change is potentially useful to recognise volcanic unrest using only two seismic stations quickly. This study reiterates the value of joint seismo-acoustic data for improving interpretation of volcanic activity and reducing ambiguity in geophysical monitoring.

Volcanic Tremor and Deformation at Kīlauea

Two new studies investigate activity at Hawaii’s Kīlauea leading up to and following the 2018 eruption to better understand the volcano’s plumbing and behavior.

Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

Kusatsu-Shirane volcano eruption on January 23, 2018, observed using JMA operational weather radars

A phreatic eruption suddenly occurred at Motoshirane (Kusatsu-Shirane volcano, Japan) at 10:02 JST on January 23, 2018. A member of the Japan Self-Defense Force was killed by volcanic blocks during training in Motoshirane, and 11 people were injured by volcanic blocks or fragments of broken glass. According to a field survey, ash fall was confirmed in Minakami, about 40 km east-northeast from Motoshirane. Although the eruption was not captured by a distant camera, the eruption plume/cloud was captured by three of the Japan Meteorological Agency’s operational weather radars. These radars observed the echo propagated to the northeast in the lower troposphere, and to the east in the middle troposphere. This is generally consistent with the observed ash fall distribution. Using the modified probabilistic estimation method, the maximum plume height was estimated to be about 5580 ± 506 m (1σ) above sea level. Estimates of the erupted mass based on the range of plume heights from radar observations and the duration of volcanic tremor during the eruption (about 8 min) do not match that obtained from a field survey (3.0–5.0 × 107 kg). This discrepancy confirms that estimates of erupted mass based on plume heights must account for eruption style parametrically, which can only be constrained by case studies of varied eruption styles.

PARAMETER-PARAMETER FISIKA UNTUK MENGUNGKAP STRUKTUR STATIS BAWAH PERMUKAAN GUNUNGAPI

ABSTRAKParameter-parameter fisika gunungapi diungkap dengan metode geofisika. Survei kakas gravitasi dan magnetik yang menghasilkan anomali positive bagi medan gravitasi dan magnetiknya, mengungkap struktur statis bawah permukaannya. Analisis tremor volkanik mengungkap dinamika internalnya. Gerakan-gerakan (aliran) fluida magma di dalam gunungapi menjadi sumber getar yang memancarkan gelombang seismik yang di sebut tremor volkanik. Lokasi, migrasi, daya pancar, bentuk geometri sistem pipa-kantong magma, periodisasi, model matematis dan sebagainya. Gempa volkanik yang disebabkan aktivitas magma dapat dijadikan indikator. Hasil pengeplotan posisi hiposenter dan episenter terhadap gempa volkanik yang terjadi, juga dapat mengungkap struktur statis bawah permukaan gunungapi. Kata Kunci : parameter-parameter fisika gunungapi; struktur statis bawah permukaanbawah permukaan ABSTRACTUsing methods of geophysics, physical parameters of volcano are described. Gravity and magnetic surveys yield positive anomaly on their fields, which can be interpreted as an accumulated material beneath the surface with certain values of its mass density and magnetic susceptibility. Analysis of volcanic tremor at the volcano to the knowledge of its internal dynamics. Fluid magma movements inside a volcano acts as source of vibrations which radiate sesmic wave called volcanic tremor. Location, migration, radiation power, geometry of magma chamber-pipe system, periodicities, mathematical models, etc. Volcanic earthquakes caused by magma activity can also be used as indicators. The results of the hypocenter and epicenter position of the volcanic earthquake that occurred, can also reveal the subsurface static structure of the volcano. Keywords : physical parameters;subsurface static structure