Seismic Structure and Seismicity of the Villarrica Volcano (Southern Central Chile)

The seismicity of Villarrica volcano is compared before and after the Maule earthquake (Mw 8.8, 2010), and the inner seismic structure is obtained through tomography

A Transformer-Based Classification System for Volcanic Seismic Signals

Volcanic seismic signals are a key element in volcano monitoring to assess the state of unrest and a possible eruption style and timing. Different sources such as brittle fracture (volcano-tectonic - VT) or fluid movement (long period - LP) generate signals with distinct characteristics in frequency content and shape, but site effects such as attenuation or background noise make their determination difficult to the untrained eye. In cases of unrest or an eminent eruption, the amount of data would require a fast and reliable source of pre-classification to classify and catalogue to aid in the job usually done by a human. To model the problem, we will develop a custom-made Transformer model. Transformers are state-of-the-art deep learning methodologies that work with sequence-based data such as audio, text or, in this case, volcanic signals. The power of transformers lies in their ability to identify complex, disconnected patterns and then use them to identify phenomena in a very effective manner. We will be building the model architecture in TensorFlow and will be running them through SHARCNET. Unfiltered continuous data from seismic stations in Villarrica volcano will be used as train dataset and catalogued from at least these two types of events (VT and LP). The model will be then tested with a different set of stations to assess changes in the signal due to attenuation at the site. This will allow to discriminate the same event in different stations

A Transformer-Based Classification System for Volcanic Seismic Signals

Monitoring volcanic events as they occur is a task that, to this day, requires significant human capital. The current process requires geologists to monitor seismographs around the clock, making it extremely labour-intensive and inefficient. The ability to automatically classify volcanic events as they happen in real-time would allow for quicker responses to these events by the surrounding communities. Timely knowledge of the type of event that is occurring can allow these surrounding communities to prepare or evacuate sooner depending on the magnitude of the event. Up until recently, not much research has been conducted regarding the potential for machine learning (ML) models to supplement or substitute human monitoring of volcanoes. Recent initiatives in this field have demonstrated that it is possible to classify volcanic events using ML techniques. Additionally, recent research in general signal processing has shown that the novel technique of multi-head self-attention (MHSA), used in natural language processing (NLP), is also useful in signal analysis. In this report, we seek to apply MHSA to create a deep neural network (DNN) that can automatically classify volcanic events. Our proposed model architecture provides minor improvements over existing approaches on pre-processed data. When considering raw signals coming directly from monitoring stations, our model outperforms existing approaches by a great margin

Comparison of seismic activity for Llaima and Villarrica volcanoes prior to and after the Maule 2010 earthquake

Llaima and Villarrica are two of the most active volcanoes in the Chilean Southern Volcanic Zone and presently show contrasting types of activity. Llaima is a closed vent edifice with fumarolic activity, while Villarrica has an open vent with a lava lake, continuous degassing and tremor activity. This study is focused on characterizing the relationships between volcanic and seismic activity in the months before and after the 2010 M8.8 Maule earthquake, which was located in NNW direction from the volcanoes. Time series for tremors, long-period and volcano-tectonic events were obtained from the catalogue of the Volcanic Observatory of the Southern Andes (OVDAS) and from the SFB 574 temporary volcanic network. An increase in the amount of tremor activity, long-period events and degassing rates was observed at Villarrica weeks before the mainshock and continued at a high level also after it. This increase in activity is interpreted to be caused by enhanced magma influx at depth and may be unrelated to the Maule event. In Llaima, an increase in the volcano-tectonic activity was observed directly after the earthquake. The simultaneous post-earthquake activity at both volcanoes is consistent with a structural adjustment response. Since this enhanced activity lasted for more than a year, we suggest that it is related to a medium-term change in the static stress. Thus, the Maule earthquake may have affected both volcanoes, but did not trigger eruptions, from which we assume that none of the volcanoes were in a critical state

Remote sensing of thermal emission and degassing at Villarrica Volcano, Chile

ABSTRACT FINAL ID: V44C-02 Villarrica is one of the most active volcanoes in Chile and is presently characterized by activity from open continuously degassing conduit. The twohundred meter diameter crater contains a persistent lava lake which has a diameter of approximately ten meters. For the quantification of SO2 fluxes three stationary NOVAC-type scanning Mini-DOAS UV spectrometers were installed at the volcano in March 2009. To validate the ground-based gas measurements we compared them to thermal anomalies detected by the MODVOLC algorithm which is provided by the Hawai'i Institute of Geophysics and Planetology (http://modis.higp.hawaii.edu). The MODVOLC algorithm (WRIGHT et al., 2002) uses data from the space-based Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration’s Terra and Aqua platforms and it exhibits the possibility to monitor volcanic heat sources. Although it was not intended to detect low intensity activity from open vent degassing, we find that in the case of Villarrica the detected thermal anomalies seem to originate from the heat carried by the gas phase. Additionally we have registered correlation between SO2 fluxes and thermal activity during the whole period of our ground-based gas-monitoring. Thermal anomalies detected by the MODVOLC algorithm coincide with observed periods of increased gas flux, enabling us to estimate gas fluxes indirectly from thermal data. Wright R. et al. (2002) Remote Sensing of Environment 82, 135-15

The influence of earthquakes on degassing at Villarrica Volcano, Chile

Villarrica is one of the most active volcanoes in Chile and is presently characterized by continuous degassing, high-level seismicity and a persistent lava lake within its crater. Three stationary NOVAC-type scanning Mini-DOAS UV spectrometers for the quantification of SO2 fluxes were installed at the volcano in March 2009. Seismic stations used for this study include the OVDAS (Observatorio Volcanológico de los Andes del Sur) volcano monitoring network, and 7 dedicated short period and broadband seismometers that were deployed in the region for more than one year. We have registered several cases of correlation between SO2 fluxes and seismic activity (LP events). Seismic events have in several cases been followed by an increase in degassing activity. The response seems to occur on two different time scales. Regional earthquake events in 2009 and 2010, and the 2011 Araucania event which occurred on January 2 and had a magnitude of 7.1, were followed by strongly increased degassing activity at Villarrica 2-4 days later, interpreted as due to increased bubble nucleation in the magmatic system at depth. The large Maule earthquake on February 27, 2010 with a magnitude of 8.8 had little immediate effect, but was followed several weeks later by an immense increase in degassing activity of about one order of magnitude compared to the baseline level. We speculate that this was a result of changing stress fields in the lower crust and at mantle depths caused by the Maule event, possibly changing melting conditions temporarily. Numerical models based on seismic, petrologic and gas flux data are used to demonstrate the feasibility of the time-lag between seismicity and degassing. We thus aim at gaining insight into the interface between magmatic and volcano-tectonic processes, especially factors playing a role for the onset of volcanic unrest. www.ifm-geomar.d

Guidelines for the use and interpretation of assays for monitoring autophagy

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Guidelines for the use and interpretation of assays for monitoring autophagy

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field