Earthquake-induced debris flows at Popocatépetl Volcano, Mexico

Coviello, Velio; Capra, Lucía; Norini, Gianluca; Dávila, Norma; Ferrés, Dolors; Márquez-Ramírez, Víctor Hugo; Pico, Eduard

doi:10.5194/esurf-2020-36

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Coincidentally this earthquake happened exactly 32 years after the devastating Mexico City earthquake on 19 September 1985. Hundreds of shallow landslides were triggered on the flanks of the Popocatepetl volcano, which is located approximately 70 km south-west of the epicenter 31 . The study area selected for this event encloses the Popocatepetl volcano and has a total extent of 365 .…”

Section: Study Areas and Datasetmentioning

confidence: 99%

A new strategy to map landslides with a generalized convolutional neural network

Prakash

Manconi

Loew

2021

Sci Rep

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Rapid mapping of event landslides is crucial to identify the areas affected by damages as well as for effective disaster response. Traditionally, such maps are generated with visual interpretation of remote sensing imagery (manned/unmanned airborne systems or spaceborne sensors) and/or using pixel-based and object-based methods exploiting data-intensive machine learning algorithms. Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data. These methods follow a standard supervised learning workflow that involves training a model using a landslide inventory covering a relatively small area. The trained model is then used to predict landslides in the surrounding regions. Here, we propose a new strategy, i.e., a progressive CNN training relying on combined inventories to build a generalized model that can be applied directly to a new, unexplored area. We first prove the effectiveness of CNNs by training and validating on event landslides inventories in four regions after earthquakes and/or extreme meteorological events. Next, we use the trained CNNs to map landslides triggered by new events spread across different geographic regions. We found that CNNs trained on a combination of inventories have a better generalization performance, with a bias towards high precision and low recall scores. In our tests, the combined training model achieved the highest (Matthews correlation coefficient) MCC score of 0.69 when mapping landslides in new unseen regions. The mapping was done on images from different optical sensors, resampled to a spatial resolution of 6 m, 10 m, and 30 m. Despite a slightly reduced performance, the main advantage of combined training is to overcome the requirement of a local inventory for training a new deep learning model. This implementation can facilitate automated pipelines providing fast response for the generation of landslide maps in the post-disaster phase. In this study, the study areas were selected from seismically active zones with a high hydrological hazard distribution and vegetation coverage. Hence, future works should also include regions from less vegetated geographic locations.

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Section: Study Areas and Datasetmentioning

confidence: 99%

A new strategy to map landslides with a generalized convolutional neural network

Prakash

Manconi

Loew

2021

Sci Rep

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“…Sismos de gran magnitud (>7Mw) inducen deslizamientos en suelos de páramo (>4000 msnm) que al estar saturados de agua incrementan la probabilidad de formar grandes flujos de lodo, convirtiéndose en un escenario de multi-amenaza y de efecto en cascada. Eventos de esta naturaleza han ocurrido en otras partes del mundo como los reportados en el volcán Popocatetépelt (2017) en México, tras el terremoto de 7.1Mw de Puebla-Morelos (Coviello et al, 2021).…”

Section: Mecanismo Generador De Los Flujos De Lodo Del Carihuairazo D...unclassified

Flujos de lodo del volcán Carihuairazo provocados por el terremoto de Ambato, Ecuador, en 1698 y su reconstrucción numérica con perspectivas a futuro

Vasconez

Vásconez²,

Mothes³

2022

RGF

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En 1698, registros históricos narran la ocurrencia de un gran terremoto (7.3 MIC) a los pies del volcán Carihuairazo, Ecuador. El terremoto destruyó la antigua ciudad de Ambato y desencadenó enormes flujos de lodo que descendieron por el río Ambato. Los flujos de lodo enterraron las entonces ruinas de la ciudad y a los sobrevivientes del terremoto. El presente manuscrito utiliza información de los registros históricos y datos de campo para reconstruir los flujos de lodo del evento, utilizando simulaciones numéricas con el fin entender el fenómeno y posibles implicaciones futuras. Eventos de esta naturaleza han ocurrido en la historia reciente del Ecuador y representan una amenaza latente para la sociedad.

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“…Coincidentally this earthquake happened exactly 32 years after the devastating Mexico City earthquake on 19 September 1985. Hundreds of shallow landslides were triggered on the flanks of the Popocatepetl volcano, which is located approximately 70 km south-west of the epicenter 26 . The study area selected for this event encloses the Popocatepetl volcano and has a total extent of 365 km 2 .…”

Section: Study Area C (Mexico)mentioning

confidence: 99%