Artificial intelligence helps predict volcanic eruptions

first_imgOver the past few years, with the launch of the European Space Agency’s satellites Sentinel 1A and Sentinel 1B, the field of volcanology has received frequent, repeated views of how the ground shifts around the world’s volcanoes. The Sentinel 1 satellites use a technique called radar interferometry, which compares radar signals sent to and reflected from Earth to track changes in the planet’s surface. The method isn’t new, but, uniquely, the Sentinel 1 satellites revisit each spot on the planet once every 6 days, and the Sentinel team releases those high-resolution observations rapidly. A research group in the United Kingdom called the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) had already begun to create a database of these ground-movement snapshots, called interferograms, for the world’s volcanoes. Overlaying this database with automated detection seemed natural given the success machine learning has had in other forms of pattern detection, says Hooper, who works with COMET.Changes in ground motion typically reflect magma shifting beneath the volcano and do not perfectly predict eruptions. But unlike thermal hot spots or ash plumes, which can be automatically detected with weather satellites, land shifts can help predict eruptions, not simply indicate their occurrence. “Deformation doesn’t always mean eruption,” Hooper says. “But there are few cases where we don’t have an eruption without deformation.”First, the teams had to teach their algorithms not to confuse atmospheric shifts for ground motion, something interferograms are prone to do. To do that, Hooper’s team settled on a technique called independent component analysis, which learns to break apart a signal into different pieces: such as stratified atmosphere or short-term turbulence, along with ground shifts in a volcano’s caldera or flank. The technique allows them to catch both brand-new ground motions, or changes in rate, both of which can be signs of pending eruption.Meanwhile, another COMET team led by Juliet Biggs, a volcanologist at the University of Bristol in the United Kingdom, has built a second algorithm using a increasingly popular form of artificial intelligence called convolutional neural networks, which use layers of biologically inspired “neurons” to break apart features of images into ever-more-abstract pools, learning how to tell, for example, cats from dogs. The researchers first trained their neural network using raw interferograms from Envisat, Sentinel’s precursor, for which they had existing examples of eruptions. Although the algorithm had some success on an analysis of 30,000 Sentinel interferograms, it still produced too many false positives. There were simply too few examples to learn from, says Fabien Albino, a volcanologist who works with Biggs at Bristol. “For machine learning, 100 is nothing. They want thousands and thousands.”To overcome that problem, Biggs and her colleagues create a synthetic data set of computer-simulated eruptions, generated for a few known physical patterns. These synthetic data dropped the fraction of false positives from some 60% to 20%, as they reported today at the AGU meeting. That trend will only continue to get better as more Sentinel examples are poured into the algorithm, Albino says. “The system is just going to tune like Google, [inputting] millions of cats and dogs, and afterward the system knows. It doesn’t have to learn anymore. It’s stable.”Although some continued technical hiccups on COMET’s volcano database have prevented the teams from running their algorithms close to real time on all volcanoes, Hooper has run their technique on select spots, including the volcanic peaks known as Sierra Negra and Wolf on the Galápagos Islands. Both erupted this past year, and Hooper’s program caught both as their unrest started, he reported yesterday at the meeting.The two algorithms are complementary; the neural network, for example, cannot catch very slow changes in deformation, but the independent component analysis can. So it’s likely that COMET’s warning system will use both, Hooper says. For now, the challenge is speeding up how quickly COMET can pull the radar data from Sentinel into its database. Although these data are available from Sentinel within a few hours, it still takes several weeks for them to fully transfer. It’s painstaking work, Hooper says. “We thought we’d be further along.”Still, the work looks exactly what the world needs, Poland says. “It’s an impressive first step,” he says. “It could absolutely revolutionize detecting these events.” Country * Afghanistan Aland Islands Albania Algeria Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia, Plurinational State of Bonaire, Sint Eustatius and Saba Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Congo, the Democratic Republic of the Cook Islands Costa Rica Cote d’Ivoire Croatia Cuba Curaçao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and McDonald Islands Holy See (Vatican City State) Honduras Hungary Iceland India Indonesia Iran, Islamic Republic of Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Korea, Democratic People’s Republic of Korea, Republic of Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macao Macedonia, the former Yugoslav Republic of Madagascar Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mayotte Mexico Moldova, Republic of Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Norway Oman Pakistan Palestine Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Qatar Reunion Romania Russian Federation Rwanda Saint Barthélemy Saint Helena, Ascension and Tristan da Cunha Saint Kitts and Nevis Saint Lucia Saint Martin (French part) Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Sint Maarten (Dutch part) Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Islands South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syrian Arab Republic Taiwan Tajikistan Tanzania, United Republic of Thailand Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Vietnam Virgin Islands, British Wallis and Futuna Western Sahara Yemen Zambia Zimbabwe New algorithms processing satellite data automatically caught the ground motion before the eruption of Wolf Volcano in the Galápagos Islands. Click to view the privacy policy. Required fields are indicated by an asterisk (*) Lucas Bustamante/Minden Pictures By Paul VoosenDec. 11, 2018 , 4:00 PMcenter_img Artificial intelligence helps predict volcanic eruptions Satellites are providing torrents of data about the world’s active volcanoes, but researchers have struggled to turn them into a global prediction of volcanic risks. That may soon change with newly developed algorithms that can automatically tease from that data signals of volcanic risk, raising the prospect that within a couple years scientists could develop a global volcano warning system.Without such tools, geoscientists simply can’t keep up with information pouring out the satellites, says Michael Poland, the scientist-in-charge of the U.S. Geological Survey’s Yellowstone Volcano Observatory in Vancouver, Washington, who was not involved in either study. “The volume of data is overwhelming,” he says.Andrew Hooper, a volcanologist at the University of Leeds in the United Kingdom who led the development of one method, says the new algorithms should benefit the roughly 800 million people who live near volcanoes. “About 1400 volcanoes have potential to erupt above the sea,” he says. “About 100 are monitored. The vast majority aren’t.” Both methods were presented this week in Washington, D.C., at the semiannual meeting of the American Geophysical Union (AGU). Sign up for our daily newsletter Get more great content like this delivered right to you! Country Emaillast_img

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