Exploring the unsupervised classification of seismic events of Cotopaxi volcano

Duque, Adrian; González, Kevin; Pérez, Noel; Benítez, Diego S.; Grijalva, Felipe; Lara-Cueva, Román; Ruiz, Mario

doi:10.1016/j.jvolgeores.2020.107009

Cited by 19 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will help us in terms of preparedness for volcanic risk reduction. The automatic volcanic event classification has been examined on Cotopaxi volcano, Mexico, to classify the seismic events automatically to minimize the hazards associated with the volcanic eruptions [18,19]. Another previous study successfully applied the classification algorithms based on the seismicity factor to determine the updated volcano's status [20].…”

Section: Discussionmentioning

confidence: 99%

Event classification of volcanic earthquakes based on K-Means clustering: Application on Anak Krakatau Volcano, Sunda Strait

Hasib,

Nur,

Baith

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

It is important to quickly recognize any physical changes in volcanology and accompanying phenomena at each stage of an eruption in terms of mitigating volcanic eruptions. Automatic classification of the type of volcanic earthquake is required, especially since the data recorded by seismic equipment is classified as big data. Analyzing big data manually will take a lot of time. Therefore, we use unsupervised machine learning such as K-means clustering to generate an automated system of classifying the volcanic events based on their waveform and spectrum characteristics. We examine the clustering of volcanic earthquakes at Anak Krakatau volcano, Sunda Strait during June to July 2014. We use one seismic station which is KRA4 to calculate the K-means clustering at Anak Krakatau volcano. We apply unsupervised machine learning such as K-means clustering to classify volcanic earthquakes. We successfully applied the K-means clustering method and found three clusters that represent the volcanic earthquake types based on the characteristics of the waveform in time and frequency domains. We observed different waveform and frequency characteristics for different clusters. The result is Cluster 1 is characterized by rapid increases in a few seconds, then gradual decreases with time, and the frequency dominant range of 4-4.7 Hz. Cluster 2 is characterized by gradual increases in a few seconds, then gradual decreases with time, and the frequency dominant range of 6-6.5 Hz. Cluster 3 is characterized by gradual increases in a few seconds, then gradual decreases with time in longer duration, and the frequency dominant range of 7-7.5 Hz. This study is useful to automatically classify the big data of daily volcanic activity that is generated continuously to mitigate the volcanic hazard.

show abstract

Section: Discussionmentioning

confidence: 99%

Event classification of volcanic earthquakes based on K-Means clustering: Application on Anak Krakatau Volcano, Sunda Strait

Hasib,

Nur,

Baith

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…The selection of features was based on a state-of-the-art study, drawing from the suggestions made in studies such as [17,[20][21][22]26] to aggregate contributions of information from various domains, such as the temporal, frequency, and scale domains. Conversely, feature reduction was proven to be an effective method for training neural networks [26].…”

Section: Volcanic Seismic Event Detection 221 Data Curation and Enric...mentioning

confidence: 99%

“…ML techniques have been used to process data in volcanology [19]. The Cotopaxi Volcano (Mexico) was analyzed through unsupervised learning, where the classification of seismic events was achieved using six clustering-based methods [20]. The authors performed feature extraction on the events, each of which was described by an 84-dimensional feature vector, including 13 features from the time domain, 21 features from the frequency domain, and 50 features from the scale domain.…”

Section: Introductionmentioning

confidence: 99%

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Sosa,

Molina,

Spagnotto

et al. 2024

Electronics

View full text Add to dashboard Cite

This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices. A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA into the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for the development of an early alert system.

show abstract

“…The deployment of nodal arrays on Mount St. Helens (Hansen & Schmandt, 2015), Kiluaea (S.-M. , and Piton de la Fournaise (Brenguier, Kowalski, et al, 2016), and of DAS arrays on Azuma volcano (Nishimura et al, 2021) and Etna (Currenti et al, 2021) speak to the opportunity that dense deployments may ultimately provide in understanding volcanoes. Machine learning will also play an important role in understanding volcano seismicity, with recent work exploring supervised ML (Malfante et al, 2018) as well as unsupervised ML (Duque et al, 2020) to classify volcano seismic signatures.…”

Section: Volcano Seismologymentioning

confidence: 99%

Big Data Seismology

Arrowsmith

Trugman

MacCarthy

et al. 2022

Reviews of Geophysics

View full text Add to dashboard Cite

The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.

show abstract

Exploring the unsupervised classification of seismic events of Cotopaxi volcano

Cited by 19 publications

References 45 publications

Event classification of volcanic earthquakes based on K-Means clustering: Application on Anak Krakatau Volcano, Sunda Strait

Event classification of volcanic earthquakes based on K-Means clustering: Application on Anak Krakatau Volcano, Sunda Strait

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Big Data Seismology

Contact Info

Product

Resources

About