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Continuous monitoring of active volcanoes is essential for understanding their behavior and providing accurate forecasts and warnings. Automating the detection of volcano microseismic events plays a crucial role in large-scale analysis, early warning systems, and efficient handling of large-scale data. This paper presents a novel approach for the automated detection of microseismic events associated with volcanic activity, focusing on the Cotopaxi volcano in Ecuador. Inspired by voice activity detection (VAD) systems used in speech processing, we propose an Adaptive-Microseismic Activity Detector (A-MAD) that modifies VAD techniques to identify frames within seismic signals containing volcanic activity. The A-MAD system incorporates a spectral subtraction stage to mitigate the impact of environmental noise and employs Gaussian Mixture Models (GMMs) to model the probabilistic distribution of microseismic events. Mel Frequency Cepstral Coefficients (MFCCs) are used as features to describe the seismic signals, enabling adaptability for varying noise levels. Experimental results on signals from Cotopaxi Volcano in Ecuador demonstrate the effectiveness of the A-MAD system, achieving high accuracy (96.39% for discrete events and 98.45% for continuous signals) while meeting the efficiency requirements of volcano monitoring institutions. This work contributes to the advancement of early warning systems for volcanic eruptions, providing a robust and adaptive approach for microseismic activity detection.

This research article presents a comprehensive study focusing on advancing radar systems for unmanned aerial vehicle (UAV) surveillance in cluttered environments. The proliferation of UAV technology and its diverse applications have raised concerns about airspace security. To tackle this issue, this article introduces a novel simulator designed to evaluate the performance of primary monopulse radar processors. The simulator accurately replicates scenarios involving clutter Weibull distributions, stationary and moving targets, as well as pulse compression situations, thereby enabling precise and controlled evaluations. The study employs the simulator to assess radar processors, including a variant of moving target detection (MTD) and a constant false alarm rate (CFAR) processor. By implementing a rigorous methodology, the article underscores the significance of simulating cluttered conditions in refining the effectiveness of radar processors. The results yield valuable insights, facilitating objective interpretations. The proposed simulator and its implications contribute to enhancing UAV surveillance and airspace security, thereby pushing forward the capabilities of radar systems.

With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be considered to automatize the analysis of data acquired by in situ monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fréchet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance.

AbstractUrban tree planting initiatives (TPIs) have become a prominent form of public work that has the potential to increase the livability and sustainability of cities. However, if not sufficiently resourced and thoughtfully planned and implemented, these high-profile programs may fail to achieve intended goals. To promote successful implementation of TPIs, we offer recommendations for the pre-planting, installation, and post-planting phases. During each of these phases we present five cross-cutting themes: ensuring adequate funding for all project phases; cultivating place-based landscape design; engaging and investing in communities; implementing sound planting practices; and evaluating performance and adapting over time. [ABSTRACT FROM AUTHOR]

Actualmente el Volcán Cotopaxi ha experimentado un incremento progresivo en su actividad sísmica, por lo que los especialistas requieren herramientas de alta eficiencia y confiabilidad para la monitorización volcánica. Este artículo presenta una detección basada en clasificación supervisada de los eventos sismo-volcánicos y no volcánicos registrados durante el año 2010. Nuestro algoritmo emplea cuatro características adquiridas por medio de la energía de los coeficientes de aproximación y detalle de la descomposición wavelet analizados con las familias Daubechies y Symlet. La clasificación de eventos fue realizada con el algoritmo de árboles de decisión empleando técnicas embebidas: validación cruzada y podamiento para una reducción del número de características. Los mejores resultados son obtenidos con la aplicación de la wavelet madre Symlet con una exactitud del 99% y una precisión de 98%.