Volcanic Earthquakes Research Articles

Phase delay of short-period tsunamis in the density-stratified compressible ocean over the elastic Earth

SUMMARY Tsunamis are often modelled as surface gravity waves of incompressible homogenous water propagating over a rigid seafloor. Previous studies have noted that when computing long-period tsunamis travelling at trans-oceanic distances with dominant periods of thousands of seconds, we need to consider four factors that are not included in the surface gravity wave theory: compressibility of seawater, density stratification of oceans, elasticity of the Earth and gravitational potential change associated with the tsunami motion. However, their effects on short-period tsunamis with dominant periods below 1000 s have not been examined. Here, we investigate how the four factors influence short-period tsunamis. Theoretical analyses and 1-D simulations using phase speeds of different tsunami models indicate that the resultant phase delay of short-period tsunamis becomes apparent after ∼1000 km propagation, mainly because of the first three factors. We then introduce a new phase correction method for dispersive short-period tsunamis with consideration of period-dependent ray paths and apply it to a 2-D simulation of a short-period tsunami from a submarine volcanic earthquake near Japan in 2015. The correction of the traveltime of a synthetic waveform by including the four factors amounts to ∼40 s at a distant station 1430 km away from the source, whereas the effects of the four factors on the waveforms are negligibly small at stations < ∼500km from the source. The observed traveltime at the ocean bottom pressure (OBP) gauge with a sampling interval at 15 s of the distant station can be explained only when these factors are incorporated into synthetic waveforms, indicating the effects due to the four factors are detectable by high-sampling OBP gauges that are deployed over broad oceanic regions.

Relentless Regeneration

Regenerative Medicine is a branch of medicine holding promise for the repair of cells, tissues, and organs to effective function. Much of the field has been developed in response to damage sustained by injury and disease. However, the body is under constant maintenance with a natural ebb and flow of dying and renewing cells. In addition, cells, tissues, and organs are constantly responding to routine demands to accommodate great variations in signals and responses. Remarkable examples are the cells composing the blood vessels and airways of the lungs. The ability to regulate, maintain and respond to changes in blood pressure and gas exchange across the blood-air interface illustrates the relentless ability of our bodies to self-adjust and self-renew throughout life. Likewise, the earth in which we live is under constant change and remodeling. Natural disasters such as volcanic eruptions and earthquakes, with time, recover and become incorporated into the rhythm of daily life. Initial recovery may be brutal but in both cases, the body and the earth have an amazing power to heal.

Source location of volcanic earthquakes and subsurface characterization using fiber-optic cable and distributed acoustic sensing system

We present one of the first studies on source location determination for volcanic earthquakes and characterization of volcanic subsurfaces using data from a distributed acoustic sensing (DAS) system. Using the arrival time difference estimated from well-correlated waveforms and a dense spatial distribution of seismic amplitudes recorded along the fiber-optic cable, we determine the hypocenters of volcanic earthquakes recorded at Azuma volcano, Japan. The sources are located at a shallow depth beneath active volcanic areas with a range of approximately 1 km. Spatial distribution of the site amplification factors determined from coda waves of regional tectonic earthquakes are well correlated with old lava flow distributions and volcano topography. Since DAS observation can be performed remotely and buried fiber-optic cables are not damaged by volcanic ash or bombs during eruptions, this new observation system is suitable for monitoring of volcanoes without risk of system damage and for evaluating volcanic structures.

A common source for the destructive earthquakes in the volcanic island of Ischia (Southern Italy): insights from historical and recent seismicity

The island of Ischia, located in the Gulf of Naples, represents an unusual case of resurgent caldera where small-to-moderate magnitude volcano-tectonic earthquakes generate large damage and catastrophic effects, as in the case of 4 March 1881 (Imax-VIII-IXMCS) and 28 July 1883 (Imax X-XI MCS) historical earthquakes, and of the recent 21 August 2017 MW = 3.9, event. All these earthquakes struck the northern area of the island. With about 65,000 inhabitants, Ischia is a popular touristic destination for thermals baths, hosting more than 3,000,000 visitors per year, thus representing a high seismic risk area. Assessing its seismic potential appears a fundamental goal and, to this end, the estimate of the magnitude of significant historical events and the characterization of their source are crucial. We report here a reassessment of historical data of damage of 1881 and 1883 earthquakes to evaluate the main source parameters of these events (obtained with the BOXER and EXISM software) and quantitatively compare, for the first time, the results with the source characteristics, obtained from instrumental data, of the recent 2017 earthquake. The results allowed us to assess the location, as well as the possible dimension and the related maximum magnitude, of the seismogenic structure responsible for such damaging earthquakes. Our results also provide an additional framework to define the mechanisms leading to earthquakes associated with the dynamics of calderas.

RSEM Modelling for Eruption Precursors Investigation on Lokon Volcano

Acceleration of volcanic activity can be detected from changes in the amplitude and energy of the seismic signals recorded around the volcano. The acceleration can be described from RSEM modelling. RSEM is useful for determining eruption precursors on volcanoes. In this study, the calculation of the cumulative RSEM value on Lokon Volcano used a very powerful software, namely ObsPy. Based on the RSEM model, the eruption of Mount Lokon on 12 September 2014 was different in character from the eruption on 20 May 2015. One was dominated by hybrid earthquakes and the other was triggered by volcanic earthquakes. These result showed that RSEM modelling can be used to investigate precursor of eruption.

Analysis volcano deformation for determining location of the pressure source, hypocentre and magma supply as disaster mitigation efforts: case studies in Merapi volcano

Various disaster mitigation efforts due to volcanic earthquakes and volcanic eruptions are carried out to reduce the risk of casualties and damage to infrastructure. One such effort is to conduct volcanic deformation studies. The results of this deformation study include estimating the position of the hypocenter, the pressure source, and the magma supply volume, so that the volume of material released during the eruption can be estimated. With the Mogi Model approach to analysis the Tiltmeter data on the eruption of Merapi Volcano in 2016, the estimated hypocentre is at a depth of about 1,100 to 2,440 m and the depth of the pressure source is about 2,345 m below the peak of Merapi with a magma supply volume of 21.81 million m3. With the same model approach from GPS data on the 2010 eruption, the results show that the hypocentre is at a depth of about 1,100 to 2,500 m below the peak of Merapi The source of magma pressure is at 2,200 m below the peak of Merapi and the volume of magma supply before the eruption is 15 million m3. These two results indicate that the depth of the pressure source causing surface deformation originates from shallow magma and is consistent with the seismicity analysis of Merapi in the same period.

Adaptive classification using incremental learning for seismic-volcanic signals with concept drift

The accurate labeling of the volcanic earthquake signals is a crucial task in order to estimate the increase in volcanic activity (among other parameters), which contributes to determine a state of volcanic unrest (as a possible precursor of an eruption). Several automatic classification approaches have been proposed in different computer science areas in order to complement the exhaustive human task of manually labeling volcano-seismic events. However, most of these approaches have been designed under the assumption of stationarity; that is, by discarding the changing nature of the volcanic phenomenon that evolves over time. In this work, an adaptive classification strategy based on incremental learning is proposed, which identifies and learns concept drifts of data streams coming from seismic recordings. The proposed strategy uses a classifier ensemble in order to handle recurrent states and classifies data even while true labels are not yet available. Unlike usual experimental protocols in the state–of–the–art, we carried out experiments with seismic data from Villarrica volcano, considering the chronological order of the data during a long period (more than 4 years of registration) to achieve the detection of drifts. The results show that the proposed classification strategy satisfactorily counteracts the changes, in contrast to standard classifiers trained within a traditional learning scheme. The proposed strategy keeps a stable classification accuracy and achieves a reduction of up to 0.28 of the absolute error in episodes of abrupt changes.

Dike Inflation Process Beneath Sakurajima Volcano, Japan, During the Earthquake Swarm of August 15, 2015

Magma intrusion usually causes seismicity and deformation in the surrounding rock and often leads to eruptions. A swarm of volcano-tectonic (VT) earthquakes associated with rapid dike intrusion in hours occurred beneath Sakurajima volcano on August 15, 2015. We determined the hypocenters and focal mechanisms of the VT earthquake swarm. The distributions of pressure (P)- and tension (T)-axes of the azimuths of the mechanisms are also obtained. The results indicate spatiotemporal changes of the distributions of the hypocenters and P- and T-axes. The hypocenters are distributed at depths of 0.3–1 km and 7:00–10:30 JST, and are located at depths of 0.3–3 km and 10:30–12:00 during which the seismic activity is the largest. At 12:00–24:00, the hypocenters are distributed in shallow and deep clusters at depths of 0.2–1 km and 1.5–3.5 km, respectively. The dike induced rapid ground deformation and is located between the shallow and deep clusters. The strike and opening directions of the dike are parallel to the NE–SW and NW–SE directions, respectively, corresponding to the regional maximum and minimum compression stress. The T-axes of the shallow cluster are distributed parallel to the opening direction of the dike. The P-axes of the deep cluster exhibit a pattern corresponding to the NE–SW direction and the T-axes are distributed in the NW–SE direction. In contrast, a 90° rotated pattern of strike-slip faulting is also observed at the deep cluster at 12:00–24:00, where the P-axes are distributed in the NW–SE direction and the T-axes are distributed in the NE–SW direction. This reflects the change in the stress field due to the dike inflation during the earthquake generation, and indicates that the alteration of stress in the vicinity of the dike due to the dike inflation and VT earthquakes are induced by the differential stress exceeding the brittle fracture strength of the rock. Future seismic and deformation observations in volcanoes will verify whether the spatiotemporal changes of the hypocenters and focal mechanism shown by this study are unique features of rapid dike intrusion.

The 2002–2005 Changbaishan Volcanic Unrest Triggered by the 2002 M 7.2 Wangqing Deep Focus Earthquake

One of the most active intraplate volcanoes in East Asia, Changbaishan volcano experienced unrest from July 2002 to July 2005. On 2002/06/28, the M 7.2 Wangqing deep-focus earthquake occurred ∼290 km northeast of Changbaishan volcano. While some studies have suggested a possible triggering relationship, the physical mechanism of such distant interaction is still not well understood. Using a template matching technique, which cross-correlates waveform of known events with continuous data, we perform systematic detection of microseismic events recorded by station CBS near Changbaishan volcano from July 1999 to July 2007. The detected earthquakes can be further categorized into three different types: volcano-tectonic (VT) events, long-period (LP) events and harmonic-spectra (HS) events. We detect 3763 VT events between July 2002 and July 2007. The intense VT earthquake swarm during the period from July 2002 to July 2005, along with recurring LPs and HSs and other geodetic/geochemical evidence, suggest magma movement during unrest. Compared with the hand-picked catalogue, the catalogue obtained by template matching technique reveals a delayed-triggering relationship between Wangqing deep-focus earthquake and unrest. The small magnitudes of the VT events and the limited numbers of LP and HS events suggest that the Wangqing mainshock likely triggered bubble excitation in the mid-crust magma system, resulting in overpressure and a small magma injection into the shallow magma chamber at a depth of ∼5 km, leading to the 3-years unrest.

Modeling Magma System Evolution During 2006–2007 Volcanic Unrest of Atka Volcanic Center, Alaska

AbstractSurface deformation and seismicity provide critical information to understand the dynamics of volcanic unrest. During 2006–2007, >80 mm/yr uplift was observed by interferometric synthetic aperture radar (InSAR) at the central Atka volcanic center, Alaska, coinciding with an increasing seismicity rate. On November 25, 2006, a phreatic eruption occurred at the Korovin volcanic vent, 5‐km north of the central Atka, following the drainage of its crater lake a month prior to the eruption. The InSAR data are assimilated into three‐dimensional finite element models using the Ensemble Kalman Filter to investigate: (1) the pressure source creating the surface deformation; (2) the triggering of the volcano‐tectonic (VT) earthquakes in the Atka volcanic center; and (3) the triggering of the phreatic eruption at Korovin. The models show that the pressure source required to create the surface deformation is a NE‐tilted, oblate ellipsoid, which rotated from steep to gentle dipping from June to November 2006 before the eruption. The modeled dilatancy in a pre‐existing weak zone, coinciding with the Amlia‐Amukta fault, driven by the pressure source has a spatial and temporal correlation with the evolution of the VT earthquakes during the unrest. The fault dilatancy may have increased the connected porosity and permeability of the fault zone allowing fluid injection which triggered the observed seismicity. In addition, the dilatated fault may have increased the fluid capacity of the fault zone by ∼105 m3, causing the discharge of the crater lake at Korovin. Consequently, the phreatic eruption of the Korovin volcano may have been triggered.

Volcanic seismicity beneath Chuginadak Island, Alaska (Cleveland and Tana volcanoes): Implications for magma dynamics and eruption forecasting

Cleveland and Tana are remote volcanoes located in the central Aleutian volcanic arc on the eastern end of the Islands of Four Mountains (IFM). The persistently active Mount Cleveland volcano, on the western side of Chuginadak Island, is surrounded by several closely spaced Quaternary volcanic centers including Carlisle, Herbert, Kagamil, Tana, and Uliaga, and numerous small satellite vents on Chiginadak between Cleveland and Tana. The Alaska Volcano Observatory (AVO) installed two permanent broadband seismometers on Chuginadak Island in 2014, and we operated a temporary broadband network focused on the western side of the island in 2015–2016. Collectively, these stations provided the first seismic observations of this frequently active volcano and the surrounding Holocene-aged volcanic vents. During the study period (July 2014–January 2019), eruptive activity at Cleveland was characterized by small explosions separated by periods of lava effusion that formed small domes in the volcano's summit crater. We characterize seismicity beneath Chuginadak Island through automated analysis of event waveform frequency content, development of a one-dimensional P-wave velocity model, calculation of earthquake hypocenters, magnitudes, focal mechanisms, and identification of earthquake families. This analysis reveals the full range of seismic event types expected in a highly active volcanic environment and includes Volcano-Tectonic (VT) earthquakes, Long-Period (LP) events, and explosion signals. LP events appear to cluster at shallow depth beneath the active crater of Mount Cleveland and almost all of the explosions occur without identifiable short-term (hours to days) seismic precursors. VT earthquakes beneath Mount Cleveland occur at depths of 2 to 8 km below sea level (BSL) and range in magnitude from −0.2 to 1.8. VT focal mechanisms have horizontal P-axes that align with the regional axis of maximum stress. These observations, and a relatively slow one-dimensional seismic velocity model, are consistent with a shallow body of magma that is fed through a deeper conduit system. The time-history of VT earthquakes and shallow LP events suggest their occurrence may track the transfer of magma and fluids from the mid-crust to the shallow portions of the conduit system and may provide a means to anticipate future explosions and periods of dome growth. VT hypocenters also extend ~7 km northeast of Cleveland's summit at depths of 5 to 10 km BSL, under a group of Holocene-aged vents between Mount Cleveland and Tana. These earthquakes have vertically-oriented P-axes and a greater percentage occur in families. These observations, combined with observations of vent orientation and morphology and gas flux, suggest the area between Cleveland and Tana represents a zone of complicated volcano-tectonic interaction, similar to calderas elsewhere in the Aleutian arc. The presence of a larger volcanic system in the eastern IFM could influence magmatism and account for the multiple closely spaced volcanic centers in this region.

Estimation of relative source locations from seismic amplitude: application to earthquakes and tremors at Meakandake volcano, eastern Hokkaido, Japan

Although seismic amplitudes can be used to estimate event locations for volcanic tremors and other seismic events with unclear phase arrival times, the precision of such estimates is strongly affected by site amplification factors. Therefore, reduction of the influence of site amplification will allow more precise estimation of event locations by this method. Here, we propose a new method to estimate relative event locations using seismic amplitudes. We use the amplitude ratio between two seismic events at a given station to cancel out the effect of the site amplification factor at that station. By assuming that the difference between the hypocentral distances of these events is much smaller than their hypocentral distances themselves, we derive a system of linear equations for the differences in relative event locations. This formulation is similar to that of a master event location method that uses differences in phase arrival times. We applied our new method to earthquakes and tremors at Meakandake volcano, eastern Hokkaido, Japan. Comparison of the hypocentral distributions of volcano-tectonic earthquakes obtained thereby with those obtained from phase arrival times confirmed the validity of our new method. Moreover, our method clearly identified source migration among three source regions in the tremor on 16 November 2008, consistent with previous interpretations of other geophysical observations in our study area. Our method will thus be useful for detailed analyses of seismic events whose onset times are ambiguous.

Volcanic emission and seismic tremor at Santiaguito, Guatemala: New insights from long-term seismic, infrasound and thermal measurements in 2018–2020

Long-term instrumental monitoring of open-vent volcanoes provides the necessary datasets to characterize volcanic activity and unravel its temporal changes. This is particularly important for active lava domes, which can undergo rapid transitions in behavior over the course of their eruption. Here, we analyzed seismic, acoustic infrasound and thermographic data collected between January 2018 and September 2020 to resolve volcanic processes taking place at the Santiaguito lava dome complex in Guatemala. During this period lava effusion filled the crater of the active Caliente lava dome. The extrusive activity was accompanied by small-to-moderate explosions, prolonged episodes of gas emissions, and occasional rockfalls. Automated algorithms were applied to identify seismic signals associated with different processes and to characterize the temporal evolution of activity. We identified ~70–250 tectonic events per week and detected signals associated with gas-and ash explosions occurring at a rate of ~70–100 events/week. Lava dome growth activity was accompanied by the emplacement of a lava flow along the eastern upper flank of Caliente and seismicity possibly due to the occurrence of rockfalls. We observed episodes of harmonic tremor in seismic and acoustic data associated with sustained gas emissions, estimated to originate at shallow depths of about 500–750 m below the crater. Data indicated that both the recurrence rate of tremor (~10–50 events/week) and its duration (~40–130 min/week) were slightly lower and shorter between January 2019 and March 2020 than in rest of the study period, despite minor variations in explosive activity. Finally, within a period of 11 weeks, between 18 January and 4 April 2018, we found 129 volcano tectonic earthquakes; we were able to locate 10 of them at depths between 1.3 and 2.3 km, ~1.5 km southwest of Caliente. This multi-parametric study provides valuable insights into geophysical signals and associated processes at Santiaguito, helping to resolve temporal occurrence of each event type during protracted effusive-explosive activity.

СЕЙСМИЧНОСТЬ СЕВЕРНОЙ ЕВРАЗИИ в 2014 г.

An overview of Northern Eurasia seismicity in 2014 is given. This territory includes 16 regions of Russia and neighboring countries. Seismic monitoring was carried out by 618 stationary seismic stations, including 591 digital, 27 analog stations and eight seismic groups. Also, temporary stations operated in some re-gions. These networks have registered over 30 thousand tectonic and volcanic earthquakes, for 571 of them the focal mechanisms are determined. According to the data collected and presented in the Annual, 413 earthquakes were felt in settlements of Northern Eurasia in 2014, manifestations of 14 of them were surveyed and described in the special articles of this issue, together with data on the focal mechanisms, preceding seismicity, aftershock processes and seismotectonic conditions. Estimates of the number of earthquakes and seismic energy released in 2014 in the regions of Northern Eurasia in comparison with long-term characteristics of seismic regime indicate that in most regions the seismic process proceeded in the “background” or “background lowered” regimes accor-ding to the definition on the SOUS'09 scale. Only the level of seismicity in the Pribaikalye and Transbaikalia region is assessed as “background increased”. The intensification of seismicity in the source zones of the past strongest earthquakes in the Alpine-Himalayan collision-fold belt – Crimean 1927, Spitak 1988, Zakatala 2012 – is noted. The tangible earthquakes that occurred in the previously aseismic areas of the Siberian and Turan platforms – Gonam earthquake on January 4 with KP=14.2, I0=8, Boguchan earthquake on January 17 with KP=13.3, I0=7 and Karaganda earthquake on June 21 with KP=11.7, I0=5–6 – indicate the need to revise the concept of a low seismic hazard in these platform areas.

Natural emergencies

Natural disasters are casualties that occur outside of human consciousness and activity. They can occur quickly or gradually. These are events that end with the disappearance. Natural disasters: landslides, floods, strong winds, fires, droughts, landslides, avalanches, rain. Some natural emergencies lead to the development of man-made emergencies. The causes of earthquakes are divided into: - Tectonic earthquakes; - volcanic earthquake;

Identification of infrasonic and seismic components of tremors in single-station records: application to the 2013 and 2018 events at Ioto Island, Japan

Infrasonic stations are sparse at many volcanoes, especially those on remote islands and those with less frequent eruptions. When only a single infrasound station is available, the seismic–infrasonic cross-correlation method has been used to extract infrasound from wind noise. However, it does not work with intense seismicity and sometimes mistakes ground-to-atmosphere signals as infrasound. This paper proposes a complementary method to identify the seismic component and the infrasonic component using a single microphone and a seismometer. We applied the method to estimate the surface activity on Ioto Island. We focused on volcanic tremors during the phreatic eruption on April 11, 2013, and during an unconfirmed event on September 12, 2018. We used the spectral amplitude ratios of the vertical ground motion to the pressure oscillation and compared those for the tremors with those for known signals generated by volcano-tectonic earthquakes and airplanes flying over the station. We were able to identify the infrasound component in the part of the seismic tremor with the 2013 eruption. On the other hand, the tremor with the unconfirmed 2018 event was accompanied by no apparent infrasound. We interpreted the results that the infrasound with the 2013 event was excited by the vent opening or the ejection of ballistic rocks, and the 2018 event was not an explosive eruption either on the ground or in the shallow water. If there was any gas (and ash) emission, it might have occurred gently undersea. As the method uses the relative values of on-site records instead of the absolute values, it is available even if the instrument sensitivity and the station site effects are poorly calibrated.

Recurrence of Deep Long-Period Earthquakes beneath the Klyuchevskoi Volcano Group, Kamchatka

Abstract—Long-period earthquakes and tremors, on a par with volcano-tectonic earthquakes, are one of two main classes of volcano-seismic activity. It is believed that long-period volcanic seismicity is associated with pressure fluctuations in the magmatic and hydrothermal systems beneath volcanoes and can therefore be used as a precursor of the impending eruptions. At the same time, the physical mechanism of the long-period seismicity is still not fully understood. In this work, we have studied the long-period earthquakes that occur at the crust–mantle boundary beneath the Klyuchevskoi volcanic group in Kamchatka in order to establish their recurrence law—the relationship between the magnitude and frequency of occurrence of the events. In the region under study, the earthquakes pertaining to this type are most numerous and characterize the state of the deep magma reservoir located at the crust–mantle boundary. The changes in the seismic regime in this part of the magmatic system can be one of the early precursors of eruptions. For a more thorough characterization of the frequency–magnitude relationship of the discussed events, we compiled a new catalog of the deep long-period earthquakes based on the matched-filter processing of continuous seismograms recorded by the network stations of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences in 2011–2012. For these earthquakes, we also used a magnitude determination method that provides the estimates close to the moment magnitude scale. The analysis of the obtained catalog containing more than 40 000 events shows that the frequency–magnitude relationships of the earthquakes markedly deviate from the Gutenberg–Richter power-law distribution, probably testifying to the seismicity mechanism and peculiarities of the sources that differ from the common tectonic earthquakes. It is shown that the magnitude distribution of the deep long-period earthquakes is, rather, described by the distributions with characteristic mean values such as the normal or gamma distribution.

Statistical moments of power spectrum: a fast tool for the classification of seismic events recorded on volcanoes

Abstract. Spectral analysis has been applied to almost thousand seismic events recorded at Vesuvius volcano (Naples, southern Italy) in 2018 with the aim to test a new tool for a fast event classification. We computed two spectral parameters, central frequency and shape factor, from the spectral moments of order 0, 1, and 2, for each event at seven seismic stations taking the mean among the three components of ground motion. The analyzed events consist of volcano-tectonic earthquakes, low frequency events and unclassified events (landslides, rockfall, thunders, quarry blasts, etc.). Most of them are of low magnitude, and/or low maximum signal amplitude, therefore the signal to noise ratio is very different between the low noise summit stations and the higher noise stations installed at low elevation around the volcano. The results of our analysis show that volcano-tectonic earthquakes and low frequency events are easily distinguishable through the spectral moments values, particularly at seismic stations closer to the epicenter. On the contrary, unclassified events show the spectral parameters values distributed in a broad range which overlap both the volcano-tectonic earthquakes and the low frequency events. Since the computation of spectral parameters is extremely easy and fast for a detected event, it may become an effective tool for event classification in observatory practice.

On finding possible frequencies for recognizing microearthquakes at Cotopaxi volcano: A machine learning based approach

Adequate detection and classification of seismic events are crucial for understanding the internal status of a Volcano. Machine learning-based classifiers use different features from the time, frequency, and scale domains related to seismic events. Regarding power spectrum-based features, several methods can be used to compute such features. However, the more suitable method for analyzing volcanic activity is undetermined. This paper presents a study about the main frequency bands, which allows maximizing the performance metrics of an automated classifier for long-period (LP) and volcano-tectonic (VT) events based on parametric (Yule-Walker and Burg) and non-parametric (Welch and Multitaper) power spectrum density estimation methods. Feature selection using embedded (pruning) and wrapper (recursive feature elimination) methods was applied to select the main frequencies that maximize the balanced error rate of suitable classification algorithms, such as decision trees (DT) and support vector machines (SVM). Bootstrapping was used to estimate a confidence interval for the frequencies of the microearthquakes. An amplitude threshold difference of at least 3 dB was used to guarantee that possible frequency features that characterize each type of event do not overlap between classes. The method who achieved the worst overall performance was not considered by the voting strategy. A Dataset from Cotopaxi volcano was used to test the proposed classification schema. The best results show for DT classifier a total of 10 key frequencies, while for SVM classifier 39 key frequencies grouped in three main frequency bands, as main features to distinguish LP events from VT earthquakes. The best classification results were achieved by the Welch method with the DT and by the Multitaper method with the SVM classifiers. Furthermore, the study confirms that there is a frequency band above 40 Hz, which seems like a critical feature for the detection and classification of stages.

Determination of Mount Merapi Volcanic Earthquake Hypocentre By Using Seismic Wave Polarization Analysis

Volcanic earthquakes of mount Merapi have been investigated periodically. The investigation aims to determine the hypocenter and epicenter of mount Merapi's volcanic earthquake using wave polarization analysis. The analysis was carried out in three domains, which are the time domain, the frequency domain, and the space domain. The analysis in the time domain was conducted by the arrival time of the volcanic earthquake, and the analysis in the frequency domain was done by observing the spectrum to get information on source frequency and bandwidth passed from polarization analysis, while the analysis in the space domain was conducted especially on hypocenter determination of the volcanic earthquakes. The analysis leads to the frequency of source 6 Hz and a bandwidth of 0.1 Hz. Thus, the hypocenter of volcanic earthquakes by polarization analysis was distributed to depth from 670 m to 3250 m from Merapi's top