Nevado del Ruiz Volcano (NRV) had a phreatomagmatic eruption in 1985. The eruption partially melted the volcano's ice cap leading to floods and lahars flowing down to nearby towns, which killed at least 25,000 people. This event has raised particular importance of monitoring activity including small eruptions at ice-capped high-altitude volcanoes. However, the high altitude makes it difficult to maintain monitoring stations near the summit crater. Moreover, the visibility of the summit area is frequently prevented by clouds. In this paper, we report the results of a feasibility study for detecting thermal anomalies and small eruptions using satellite thermal remote sensing and ground-based infrasound technique. We newly included South and Central America to the observation areas of the near-real-time monitoring system of the active volcanoes, which uses infrared images from satellites. We also operated three infrasound stations in the distances of 4-6 km from the active crater. Each of the stations consisted of a pair of infrasound sensors, and a cross-correlation technique was applied. The thermal and infrasound data acquisition started in August 2015 and December 2016, respectively, and recorded the recent dome-forming activity of NRV. We proposed parameters representing the visibility of the thermal anomalies and infrasound signals. These parameters are useful for monitoring because the severe weather condition at NRV frequently prevents signal detections. We discussed the detected thermal anomalies and infrasound signals in comparison with their visibilities and the changes in the volcanic activity of NRV reported by the local observatory. The thermal anomaly and infrasound detections were consistent with the high eruptive activity occurring at the NRV from October 2015 to May 2017 and its subsequent decline. Within the active period, there were breaks in the detections of thermal anomaly and infrasound. The visibility analyses allowed us to interpret the breaks as a result of bad weather conditions and to distinguish them from the confirmed low-activity periods after May 2017.

resumen En el volcán Puracé se realizó un estudio de eventos sísmicos tipo tornillo durante el periodo comprendido entre 2000 y 2012. Con el fin de caracterizar la fuente de generación y la naturaleza de los fluidos que interactúan en las grietas internas del volcán Puracé, el factor Q del resonador y las frecuencias dominantes fueron calculados utilizando el método Sompi. Encontramos que los valores del parámetro Q del resonador variaron entre 100 y 400 en promedio, y que en algunos periodos (2010) el Q del resonador alcanzó valores de hasta 1200. Las frecuencias complejas presentaron diferentes rangos (1,5-13 Hz), predominando en promedio los 6 Hz. Fue posible observar variaciones temporales tanto en el parámetro Q del resonador como en frecuencias dominantes, que pudieron estar asociadas a cambios en el contenido del fluido, desde más gaseoso hasta una mezcla de gas y material particulado (azufre mineralizado), y a cambios en los tamaños de las grietas, de 30 a 50 m, que se estima están ubicadas a una profundidad de entre 300 y 900 m del fondo del cráter.Proponemos un modelo conceptual en el cual el fluido que genera los eventos tornillo está asociado a la actividad magmática del volcán Puracé, que libera pulsos de gas que interactúan con el sistema hidrotermal y con capas superficiales de zonas mineralizadas con azufre nativo, que a su vez se mezcla con el gas proveniente del magma, lo cual causa la aparición de sismos tipo tornillo

Volcanic seismicity is one of the most relevant parameters for the evaluation of volcanic activity and consequently the prognosis of eruptions. Earthquakes of volcanic origin are of different classes, directly related to the physical process that generates them. The distribution of the data between classes of seismic-volcanic signals generally presents an unbalanced profile (imbalanced datasets), which can hinder the performance of the classification in machine learning models. Therefore, this research presents a characterization technique (feature extract) that, in addition to reducing the dimension of each seismic record, allows a representation of the signals with the most relevant and significant information. This work proposes the use of a Dual Feature Autoencoder (DAF), which is compared with conventional characterization techniques such as Linear Prediction Coefficients (LPC) and Principal Component Analysis (PCA). The training of the model was performed with a dataset containing volcano-tectonic earthquakes (VT), long period events (LP) and Tornillo-type events (Tor) of the Galeras volcano, one of the most active volcanoes in Colombia. The classification results reach 99% of the classification of the mentioned classes.

The analysis and classification of seismic records of volcanoes allow us to determine the alert state in which it is. A timely study of these signs can contribute to decision-making to safeguard the integrity of people in the face of a natural disaster. The present work applies a methodology that combines the analysis of linear prediction coefficients and artificial neural networks to classify earthquakes. Two types of earthquakes that come from Galeras Volcano, Colombia, are studied volcano-tectonic and long-period. The classification is made using the clustering technique based on unsupervised learning. The signals are transformed using the linear prediction filter coefficients technique, which has the function of reducing the size of the vector that contains the original data. MATLAB software is used to generate a self-organizing network that handles clustering. The results show that the best alternative in unsupervised learning is to use the linear prediction coefficients of order 5, 6, and 7 to represent a seismic signal. For lower orders, the necessary information is not captured and for higher orders, noise information is shown.

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