[{"command":"openDialog","selector":"#drupal-modal","settings":null,"data":"\u003Cdiv id=\u0022republish_modal_form\u0022\u003E\u003Cform class=\u0022modal-form-example-modal-form ecl-form\u0022 data-drupal-selector=\u0022modal-form-example-modal-form\u0022 action=\u0022\/en\/article\/modal\/7238\u0022 method=\u0022post\u0022 id=\u0022modal-form-example-modal-form\u0022 accept-charset=\u0022UTF-8\u0022\u003E\u003Cp\u003EHorizon articles can be republished for free under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence.\u003C\/p\u003E\n \u003Cp\u003EYou must give appropriate credit. We ask you to do this by:\u003Cbr \/\u003E\n 1) Using the original journalist\u0027s byline\u003Cbr \/\u003E\n 2) Linking back to our original story\u003Cbr \/\u003E\n 3) Using the following text in the footer: This article was originally published in \u003Ca href=\u0027#\u0027\u003EHorizon, the EU Research and Innovation magazine\u003C\/a\u003E\u003C\/p\u003E\n \u003Cp\u003ESee our full republication guidelines \u003Ca href=\u0027\/horizon-magazine\/republish-our-stories\u0027\u003Ehere\u003C\/a\u003E\u003C\/p\u003E\n \u003Cp\u003EHTML for this article, including the attribution and page view counter, is below:\u003C\/p\u003E\u003Cdiv class=\u0022js-form-item form-item js-form-type-textarea form-item-body-content js-form-item-body-content ecl-form-group ecl-form-group--text-area form-no-label ecl-u-mv-m\u0022\u003E\n \n\u003Cdiv\u003E\n \u003Ctextarea data-drupal-selector=\u0022edit-body-content\u0022 aria-describedby=\u0022edit-body-content--description\u0022 id=\u0022edit-body-content\u0022 name=\u0022body_content\u0022 rows=\u00225\u0022 cols=\u002260\u0022 class=\u0022form-textarea ecl-text-area\u0022\u003E\u003Ch2\u003EHow speech recognition techniques are helping to predict volcanoes\u2019 behaviour\u003C\/h2\u003E\u003Cp\u003EMore than 29 million people globally live within 10km of a volcano, and understanding volcanoes\u2019 behaviour \u2013 and being able to predict when they are going to erupt or spew ash into the air \u2013 is vital for safeguarding people\u2019s wellbeing.\u003C\/p\u003E\u003Cp\u003EHowever, \u003Ca href=\u0022https:\/\/theconversation.com\/why-cant-we-predict-when-a-volcano-will-erupt-53898\u0022 target=\u0022_blank\u0022 rel=\u0022noopener noreferrer\u0022\u003Epredicting volcano behaviour is difficult\u003C\/a\u003E, especially if they have been dormant, and monitoring them can be challenging since taking samples or deploying equipment poses physical dangers. And while theoretical models may approximate how a particular volcano behaves given its location, geological makeup and the behaviour of the Earth\u2019s magma underneath it (amongst other things), there are still many unknown variables \u2013 and every volcano is unique.\u003C\/p\u003E\u003Cp\u003ENow a researcher at the University of Granada in Spain, Dr Zuccarello is aiming to automatically analyse volcanic activities to develop early-warning models that could save the lives of people living near volcanoes.\u003C\/p\u003E\u003Cp\u003EIn the last decade, data collection methods have improved significantly, with new and more sensitive equipment, and researchers now have access to an unprecedented deluge of data. For example, they can access real-time information on how the Earth shakes in the vicinity of the volcano (seismic activity), the propagation of sound waves from deep within the Earth, and the chemicals present inside the volcano and how they are changing.\u003C\/p\u003E\u003Cp\u003EVolcano observatories need to analyse large quantities of data in a short period of time. \u2018There is a need for faster and error-free techniques to gather such data,\u2019 Dr Zuccarello said.\u003C\/p\u003E\u003Cp\u003EHis \u003Ca href=\u0022https:\/\/cordis.europa.eu\/project\/id\/798480\u0022 target=\u0022_blank\u0022 rel=\u0022noopener noreferrer\u0022\u003EVOLCANOWAVES\u003C\/a\u003E project, which includes researchers based in Spain, the United Kingdom, Italy, Mexico, and Argentina, uses machine learning to identify patterns in the seismic activity around a volcano in an effort to predict when, or if it will erupt. In particular, Dr Zuccarello is looking at the low-frequency events, such as volcanic tremors, which are usually linked to the movement of magma within a volcano\u2019s plumbing.\u003C\/p\u003E\u003Cp\u003E\u003Cblockquote class=\u0022tw-text-center tw-text-blue tw-font-bold tw-text-2xl lg:tw-w-1\/2 tw-border-2 tw-border-blue tw-p-12 tw-my-8 lg:tw-m-12 lg:tw--ml-16 tw-float-left\u0022\u003E\n \u003Cspan class=\u0022tw-text-5xl tw-rotate-180\u0022\u003E\u201c\u003C\/span\u003E\n \u003Cp class=\u0022tw-font-serif tw-italic\u0022\u003E\u2018Speech and seismic signals share important properties.\u2019\u003C\/p\u003E\n \u003Cfooter\u003E\n \u003Ccite class=\u0022tw-not-italic tw-font-normal tw-text-sm tw-text-black\u0022\u003EDr Guillermo Cort\u00e9s, University of Udine, Italy \u003C\/cite\u003E\n \u003C\/footer\u003E\n\u003C\/blockquote\u003E\n\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESpeech recognition\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EIn the last decade, the application of machine learning to pattern identification has been integral in speech recognition, but researchers are now using it to forecast volcanoes\u2019 behaviour. \u2018Although these fields vary significantly in terms of context and source, the object of the analysis is the same \u2013 the study of their harmonics over time in search of patterns,\u2019 Dr Zuccarello said.\u003C\/p\u003E\u003Cp\u003EThe project\u2019s main output will be a set of algorithms \u2013 set to be complete when the project ends later this year \u2013 and he hopes that they will be used widely in the scientific community to monitor volcanoes on a day-to-day basis.\u003C\/p\u003E\u003Cp\u003E\u2018Speech and seismic signals share important properties,\u2019 said Dr Guillermo Cort\u00e9s, a specialist in signal processing and machine learning at the University of Udine in Italy. He ran a project called \u003Ca href=\u0022https:\/\/cordis.europa.eu\/project\/id\/749249\u0022 target=\u0022_blank\u0022 rel=\u0022noopener noreferrer\u0022\u003EVULCAN.ears\u003C\/a\u003E, which also used speech recognition technology to understand what volcanoes are saying.\u003C\/p\u003E\u003Cp\u003EDr Cort\u00e9s and colleagues developed a real-time volcano monitoring system, which automatically detects and labels volcanic \u2018events\u2019 in the data streams coming from monitoring stations that detect seismic signals. This system then creates catalogues of activity in order to find patterns of behaviour.\u003C\/p\u003E\u003Cp\u003EDr Roberto Carniel, a geophysicist at the University of Udine and the project\u2019s scientific supervisor, says: \u2018The arrival of machine learning and applied deep-learning techniques is uncovering new solutions for old problems. (Now) it is easier to mix results from several monitoring areas involving the study of seismic signals, infrasonic signals, magnetic signals, geochemical analysis of gases and fluids, deformation, thermal and video cameras, to produce more robust and reliable predictions.\u2019\u003C\/p\u003E\u003Cp\u003E\u003Cfigure role=\u0022group\u0022 class=\u0022@alignleft@\u0022\u003E\n\u003Cimg alt=\u0022With 29 million people around the world living within 10km of a volcano, predicting when they are going to erupt is vital for safeguarding people\u2019s wellbeing. Image credit - Pexels\/pixabay, licenced under CC0\u0022 height=\u0022853\u0022 src=\u0022\/research-and-innovation\/sites\/default\/files\/hm\/IMCEUpload\/light-night-evening-fire-68645.jpg\u0022 title=\u0022Image credit - Pexels\/pixabay, licenced under CC0\u0022 width=\u00221280\u0022\u003E\n\u003Cfigcaption class=\u0022tw-italic tw-mb-4\u0022\u003EWith 29 million people around the world living within 10km of a volcano, predicting when they are going to erupt is vital for safeguarding people\u2019s wellbeing. Image credit - Pexels\/pixabay, licenced under CC0\u003C\/figcaption\u003E\n\u003C\/figure\u003E\n\u003C\/p\u003E\u003Cp\u003EThe team developed a volcanic seismic recognition system based on supervised machine learning, in which they analysed data that had already been labelled by other experts, teaching the software to identify volcano events such as volcanic tremors, ashfall, or explosions within the volcano. This approach is similar to finding words in a conversation, labelling their parts of speech and finding the patterns of language unique to each volcano.\u003C\/p\u003E\u003Cp\u003EThis is a break from the classic methods for building catalogues of volcano behaviour, Dr Cort\u00e9s says. These methods involve the automatic detection of events and manual classification by experts. \u2018Usually they perform this task on a daily basis, which could be too slow in a situation involving a population at risk due to an unexpected eruption,\u2019 he said.\u003C\/p\u003E\u003Cp\u003ETime can be of the essence when it comes to volcanoes, particularly in the event of ashfall, collapses and landslides, he says. In those cases, \u2018the detection and classification in real-time operation is critical\u2019 in order to reduce decision-making time if nearby communities need to be evacuated.\u003C\/p\u003E\u003Cp\u003EDr Cort\u00e9s\u2019 ultimate aim was to develop a system that is universal and volcano-independent that could be easily embedded at any volcano observatory. To build this, the researchers have created a universal database from dozens of volcanoes around the world and used their machine-learning techniques to build universal models. A preliminary version of this \u003Ca href=\u0022https:\/\/zenodo.org\/record\/3594897\u0022 target=\u0022_blank\u0022 rel=\u0022noopener noreferrer\u0022\u003Eis available online\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003EHowever, for Dr Carniel, what\u2019s important now is that volcanic observatories around the world take the work forward. \u2018They are the real key to advancing the volcano-independent idea, installing the volcanic seismic recognition system in their own observatories, sharing resources, and giving valuable feedback,\u2019 he said.\u003C\/p\u003E\u003Cp\u003EThese observatories are, after all, the front line of countries\u2019 efforts to protect their citizens from the volcanoes within their borders \u2013 and scientists need to be able to hear volcanoes\u2019 whispers to predict when they are going to start shouting.\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EThe research in this article was funded by the EU. 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