The scheme offered here was used to characterize mostly monogenetic volcanoes; however the classification scheme may also be usable for other landscape features.
This calculation is performed using the database of events and a classification scheme for volcano morphology (shape) used by Jenkins et al. (2012a).
Because a few volcanoes change their eruptive and degassing behavior from time to time, our classification of a volcano is for the foreseeable future but not necessarily forever.
A database of events taken from three monitoring stations on the volcano was used to create a classification structure, independent of which station provided the signal.
We present an automatic recognition system of volcano seismicity, by considering feature extraction, event classification, and subsequent event detection, in order to reduce the processing time as a first step towards a high reliability automatic detection system in real-time.
A companion paper will further explain and explore the PEI, and provide the PEI classifications for the world's volcanoes.
Volcanology deals with the formation, distribution, and classification of volcanoes as well as with their structure and the kinds of materials ejected during an eruption (such as pyroclastic flows, lava, dust, ash, and volcanic gases).
In a previous work, we proposed a structure for automatic classification of seismic events in Llaima volcano, one of the most active volcanoes in the Southern Andes, located in the Araucanía Region of Chile.
Figure 1 Classification of volcanic rocks from the Izu-Oshima volcano using discrimination diagrams.
Fig. 7 Flowchart illustrating the classification choices required for recording instances of volcano deformation in a database.
First, we checked the typical landslide texture caused by Kumamoto earthquake on the central cones of Aso volcano based on Varnes (1978) and U.S. Geological Survey (2004) classifications.
