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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n2g9wpq5.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero nx-fh\" data-layout=\"faculty\" data-ground=\"none\" data-ah=\"home\" aria-labelledby=\"nx-ast-title\"><div class=\"nx-container\"><div class=\"nx-fh-grid\"><div class=\"nx-hero-text nx-ah-text\"><p class=\"nx-ah-kicker\"><span>Risk Academy, a Nexortium programme<\/span><span>Faculty of Risk Science and Methods<\/span><\/p><p class=\"nx-ah-accent\"><svg class=\"nx-ah-motif\" data-motif=\"bands\" viewBox=\"0 0 44 16\" aria-hidden=\"true\" focusable=\"false\"><path data-k=\"b\" d=\"M1 5C12 3 30 2 43 1M1 11C12 13 30 14 43 15\"\/><path d=\"M1 8C12 8 30 8 43 8\"\/><\/svg><span>The shared methods of risk analysis, with their limits stated<\/span><\/p><h1 id=\"nx-ast-title\" class=\"nx-hero-title nx-ah-title\">Faculty of Risk Science and Methods<\/h1><p class=\"nx-tagline nx-ah-lede\">Every risk figure rests on a rate, a distribution, a sample and a decision rule, and each carries an uncertainty that has to be written down.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"#nx-sec-maps\">Open the twin builder <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"#nx-sec-departments\">See the departments<\/a><\/div><\/div><div class=\"nx-fh-con\"><p class=\"nx-fh-con-h\"><span class=\"nx-tw-path\"><span>risksacademy.com<\/span><span>risk-science<\/span><strong>faculty<\/strong><\/span><\/p><div class=\"nx-fh-con-b\"><figure class=\"nx-ra-sig\" data-motif=\"bands\" data-scope=\"faculty\"><svg class=\"nx-ra-sig-svg\" viewBox=\"0 0 480 520\" role=\"img\" aria-label=\"Faculty of Risk Science and Methods: 4 departments and 17 disciplines, drawn as uncertainty bands. 3 disciplines with open courses; primary courses 3 open, 19 in preparation and 6 approved for authoring. 01 Department of Risk Management: Enterprise risk management and risk frameworks (0 open, 3 in preparation, 0 approved); Operational risk and business continuity (0 open, 2 in preparation, 0 approved); Model risk and validation (0 open, 1 in preparation, 0 approved); Assurance, internal control and risk culture (0 open, 1 in preparation, 0 approved). 02 Department of Quantitative Risk Analysis: Quantitative risk analysis and statistics (0 open, 4 in preparation, 2 approved); Catastrophe, exposure and vulnerability modelling (0 open, 2 in preparation, 0 approved); Risk indicators and composite indices (0 open, 1 in preparation, 0 approved); Probabilistic forecasting and verification (0 open, 0 in preparation, 0 approved); AI and machine learning for risk analysis (0 open, 0 in preparation, 0 approved). 03 Department of Decision, Behaviour and Communication: Uncertainty and decision making (1 open, 1 in preparation, 1 approved); Scenario analysis and stress testing (1 open, 0 in preparation, 0 approved); Risk communication and risk literacy (0 open, 1 in preparation, 0 approved); Risk perception and behavioural science (0 open, 0 in preparation, 1 approved); Expert judgement and elicitation (0 open, 0 in preparation, 0 approved). 04 Department of Geospatial and Risk Data Science: Geospatial analysis, Earth observation and GeoAI (1 open, 2 in preparation, 0 approved); Risk data governance, provenance and licensing (0 open, 1 in preparation, 0 approved); Risk information standards and interoperability (0 open, 0 in preparation, 2 approved).\" focusable=\"false\" data-nx-z=\"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\"><title>Faculty of Risk Science and Methods: 4 departments and 17 disciplines, drawn as uncertainty bands. 3 disciplines with open courses; primary courses 3 open, 19 in preparation and 6 approved for authoring. 01 Department of Risk Management: Enterprise risk management and risk frameworks (0 open, 3 in preparation, 0 approved); Operational risk and business continuity (0 open, 2 in preparation, 0 approved); Model risk and validation (0 open, 1 in preparation, 0 approved); Assurance, internal control and risk culture (0 open, 1 in preparation, 0 approved). 02 Department of Quantitative Risk Analysis: Quantitative risk analysis and statistics (0 open, 4 in preparation, 2 approved); Catastrophe, exposure and vulnerability modelling (0 open, 2 in preparation, 0 approved); Risk indicators and composite indices (0 open, 1 in preparation, 0 approved); Probabilistic forecasting and verification (0 open, 0 in preparation, 0 approved); AI and machine learning for risk analysis (0 open, 0 in preparation, 0 approved). 03 Department of Decision, Behaviour and Communication: Uncertainty and decision making (1 open, 1 in preparation, 1 approved); Scenario analysis and stress testing (1 open, 0 in preparation, 0 approved); Risk communication and risk literacy (0 open, 1 in preparation, 0 approved); Risk perception and behavioural science (0 open, 0 in preparation, 1 approved); Expert judgement and elicitation (0 open, 0 in preparation, 0 approved). 04 Department of Geospatial and Risk Data Science: Geospatial analysis, Earth observation and GeoAI (1 open, 2 in preparation, 0 approved); Risk data governance, provenance and licensing (0 open, 1 in preparation, 0 approved); Risk information standards and interoperability (0 open, 0 in preparation, 2 approved).<\/title><\/svg><figcaption class=\"nx-ra-sig-cap\"><span><span class=\"nx-ra-sig-t\">Risk Science and Methods: departments, disciplines and courses.<\/span> Drawn as uncertainty bands.<\/span><ul class=\"nx-ra-key\"><li data-k=\"live\">Open, 3<\/li><li data-k=\"prep\">In preparation, 19<\/li><li data-k=\"appr\">Approved for authoring, 6<\/li><\/ul><span class=\"nx-ra-sig-src\">From the Academy atlas and the curriculum: primary courses only; numbers mark departments.<\/span><\/figcaption><\/figure><\/div><a class=\"nx-fh-go\" href=\"#nx-sec-maps\"><span class=\"nx-fh-go-k\">Twin builder and simulator<\/span><span class=\"nx-fh-go-t\">The central Apennines: L'Aquila, Amatrice and Norcia<\/span><span class=\"nx-fh-go-m\">4 twin generators, 8 layers, 4 instruments<\/span><\/a><\/div><\/div><dl class=\"nx-ah-figs\" data-n=\"3\"><div><dt>disciplines in 4 departments<\/dt><dd>17<\/dd><\/div><div><dt>courses open now, 19 in preparation<\/dt><dd>3<\/dd><\/div><div><dt>reference earthquakes, M6.2 to M6.6<\/dt><dd>3<\/dd><\/div><\/dl><p class=\"nx-ah-fignote\">Twin figures for the central Apennines: L'Aquila, Amatrice and Norcia, counted from its layers at their stated dates.<\/p><nav class=\"nx-fh-jump\" aria-label=\"On this page\"><a href=\"#nx-sec-maps\">Twin builder<\/a><a href=\"#nx-sec-departments\">Departments<\/a><a href=\"#nx-sec-disciplines\">Disciplines<\/a><a href=\"#nx-sec-frontiers\">Frontiers<\/a><a href=\"#nx-sec-hub\">Hub<\/a><a href=\"#nx-sec-programmes\">Where to start<\/a><a href=\"#nx-sec-courses\">Courses<\/a><a href=\"#nx-sec-standards\">Standards<\/a><\/nav><\/div><\/section><section id=\"nx-sec-maps\" class=\"nx-as\" data-as=\"maps\" aria-labelledby=\"nx-sec-maps-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-maps-h\" class=\"nx-as-title\">Twin builder and simulator.<\/h2><p class=\"nx-as-lede\">The faculty's twin of L'Aquila on the planet map, the globe and in 3D. Pick a generator, switch layers, run the instruments; every layer prints its provider and date.<\/p><\/div><div class=\"nx-as-body\"><div class=\"nx-ra-map nx-tw\" data-ra-map=\"risk-science\" data-ra-map-od=\"J\/suhpDmyEM6vt\/FeKzBtfv97U9AlgeKOZNVS1zHFVo=\" data-ra-map-state=\"idle\" data-ra-map-mode=\"learn\" data-ra-view=\"map\" data-tw-pip=\"twin\"><div class=\"nx-tw-head nx-ra-map-bar\" role=\"toolbar\" aria-label=\"Twin builder controls\"><p class=\"nx-tw-path\"><span>risk-science<\/span><span>l'aquila<\/span><strong>twin builder<\/strong><\/p><div class=\"nx-ra-tb-search\" data-tw-search=\"true\"><\/div><span class=\"nx-ra-map-seg\" role=\"group\" aria-label=\"View\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"view-map\" aria-pressed=\"true\">Planet map<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"view-globe\" aria-pressed=\"false\">Globe<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"view-twin\" aria-pressed=\"false\">3D twin<\/button><\/span><span class=\"nx-ra-map-seg\" role=\"group\" aria-label=\"Extent\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"anchor\" aria-pressed=\"true\" disabled>L'Aquila<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"world\" aria-pressed=\"false\" disabled>World<\/button><\/span><span class=\"nx-ra-map-seg\" role=\"group\" aria-label=\"Mode\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"learn\" aria-pressed=\"true\" disabled>Learn<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"expert\" aria-pressed=\"false\" disabled>Expert<\/button><\/span><span class=\"nx-tw-more\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"share\" disabled>Share and cite<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"tools\" disabled>Expert tools<\/button><\/span><\/div><div class=\"nx-tw-gens nx-ra-tb-gens\" role=\"group\" aria-label=\"Twin generators\"><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"shake\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Field<\/span><span class=\"nx-ra-tb-gt\">Event shaking field<\/span><span class=\"nx-ra-tb-gp\">ShakeMap intensity of the 2009 and 2016 main shocks.<\/span><\/button><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"sample\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Sampling<\/span><span class=\"nx-ra-tb-gt\">Hazard sampling field<\/span><span class=\"nx-ra-tb-gp\">A density field of the live USGS catalogue in view: where events sample the hazard this week.<\/span><\/button><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"lead\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Rings<\/span><span class=\"nx-ra-tb-gt\">Warning lead-time rings<\/span><span class=\"nx-ra-tb-gp\">Lead time an alert leaves around each reference event.<\/span><\/button><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"exp\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Exposure<\/span><span class=\"nx-ra-tb-gt\">Exposure under the events<\/span><span class=\"nx-ra-tb-gp\">GPW population density under the reference events.<\/span><\/button><\/div><div class=\"nx-tw-body\"><div class=\"nx-ra-map-views\"><div class=\"nx-ra-map-stage\"><div class=\"nx-ra-map-idle\"><button type=\"button\" class=\"nx-btn\" data-variant=\"secondary\" data-ra-map-act=\"load\">Load the map<\/button><\/div><\/div><div class=\"nx-ra-map-twin\"><div class=\"nx-ah\" data-ah=\"twin\"><div class=\"nx-ast\" data-ast=\"twin\" data-ast-state=\"flat\" data-ast-faculty=\"risk-science\" data-ast-mode=\"metres\" data-ast-od=\"KwWwtf3193b19APMOHPUbXw4RChtn3Zh8FJe1r+skOI=\" data-ast-data=\"ocHQ7mLOaEtl4b+N+wMxxmuM4FHvHPjlBnmWBZzsKAk=\" data-ast-exag=\"1.5\"><div class=\"nx-ast-stage\" tabindex=\"0\" role=\"region\" aria-label=\"Digital twin of The central Apennines: L'Aquila, Amatrice and Norcia\" aria-describedby=\"nx-ast-twin-help\"><figure class=\"nx-ra-sig nx-ast-flat\" data-scope=\"twin\" id=\"nx-ast-fig\"><svg class=\"nx-ra-sig-svg nx-ast-svg\" viewBox=\"0 0 1200 1133\" role=\"img\" aria-label=\"The central Apennines: L'Aquila, Amatrice and Norcia: a flat plan of the faculty twin. Terrain 24 to 2,805 m; 3 reference earthquakes, M6.2 to M6.6; Earthquakes M4.5 and above, past week (live): live, loads in the twin; USGS ShakeMap intensity contours: live, loads in the twin. Contours every 500 metres from the elevation grid; reference earthquakes as marks.\" focusable=\"false\" 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\"><title>The central Apennines: L'Aquila, Amatrice and Norcia: a flat plan of the faculty twin. Terrain 24 to 2,805 m; 3 reference earthquakes, M6.2 to M6.6; Earthquakes M4.5 and above, past week (live): live, loads in the twin; USGS ShakeMap intensity contours: live, loads in the twin. Contours every 500 metres from the elevation grid; reference earthquakes as marks.<\/title><\/svg><figcaption class=\"nx-ra-sig-cap\"><span><span class=\"nx-ra-sig-t\">The central Apennines: L'Aquila, Amatrice and Norcia, Italy.<\/span> Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.<\/span><ul class=\"nx-ra-key nx-ast-key\"><li data-k=\"terrain\">Terrain 24 to 2,805 m<\/li><li data-k=\"event\">3 reference earthquakes, M6.2 to M6.6<\/li><li data-k=\"live-quakes-live\">Earthquakes M4.5 and above, past week (live): live, loads in the twin<\/li><li data-k=\"live-shakemap\">USGS ShakeMap intensity contours: live, loads in the twin<\/li><\/ul><span class=\"nx-ra-sig-src\">Credits and dates per layer are listed under the stage. Relief in the twin is exaggerated \u00d71.5 for legibility.<\/span><\/figcaption><\/figure><p class=\"nx-ast-help\" id=\"nx-ast-twin-help\">Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and - zoom, 0 resets, V steps through the views, Escape clears.<\/p><\/div><div class=\"nx-ast-bar nx-ah-hud\" role=\"toolbar\" aria-label=\"Twin controls\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"open\" data-variant=\"primary\">Open the 3D twin<\/button><span class=\"nx-ah-status\" data-live=\"true\"><span class=\"nx-ah-dot\" aria-hidden=\"true\"><\/span>2 live layers, checked 17 Oct 2026<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"reset\" disabled>Reset view<\/button><span class=\"nx-ast-group\" aria-label=\"View\" data-ast-views=\"true\" data-ast-default=\"a-instrument\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"style\" data-ast-style=\"a-instrument\" aria-pressed=\"true\" disabled>Drawing<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"style\" data-ast-style=\"b-daylight\" aria-pressed=\"false\" disabled>Photo<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"style\" data-ast-style=\"c-holograph\" aria-pressed=\"false\" disabled>Holograph<\/button><\/span><details class=\"nx-ah-menu\" data-k=\"places\"><summary><span>Places<\/span><span class=\"nx-ah-n\">4<\/span><\/summary><div class=\"nx-ah-pop\"><span class=\"nx-ast-group\" aria-label=\"Views\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"laquila\" disabled>L'Aquila 2009<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"amatrice\" disabled>Amatrice and Norcia 2016<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"gran-sasso\" disabled>Gran Sasso<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"whole\" disabled>The whole box<\/button><\/span><\/div><\/details><details class=\"nx-ah-menu\" data-k=\"layers\"><summary><span>Layers<\/span><span class=\"nx-ah-n\">4<\/span><\/summary><div class=\"nx-ah-pop\"><span class=\"nx-ast-group\" aria-label=\"Layers\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"overlay\" aria-pressed=\"false\" hidden disabled>Analysis overlay<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"terrain\" data-ast-delivery=\"inline\" aria-pressed=\"true\" disabled><span class=\"nx-ah-sw\" data-f=\"terrain\" aria-hidden=\"true\"><\/span><span>Terrain<\/span><\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"events\" data-ast-delivery=\"inline\" aria-pressed=\"true\" disabled><span class=\"nx-ah-sw\" data-f=\"points\" aria-hidden=\"true\"><\/span><span>Reference earthquakes<\/span><\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"quakes-live\" data-ast-delivery=\"browser-direct\" aria-pressed=\"false\" disabled><span class=\"nx-ah-sw\" data-f=\"live\" aria-hidden=\"true\"><\/span><span>Earthquakes M4.5 and above<\/span><span class=\"nx-ah-tag\">Live<\/span><\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"shakemap\" data-ast-delivery=\"browser-direct\" aria-pressed=\"false\" disabled><span class=\"nx-ah-sw\" data-f=\"live\" aria-hidden=\"true\"><\/span><span>USGS ShakeMap intensity contours<\/span><span class=\"nx-ah-tag\">Live<\/span><\/button><\/span><\/div><\/details><span class=\"nx-ast-group nx-ah-time\" aria-label=\"Time\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"replay\" disabled>Replay the events<\/button><\/span><button type=\"button\" class=\"nx-ast-btn nx-ah-tomap\" data-ast-act=\"to-map\" aria-controls=\"nx-sec-maps\"><span class=\"nx-ah-pin\" aria-hidden=\"true\"><\/span>Open in the map<\/button><details class=\"nx-ast-cred nx-ah-info\"><summary aria-label=\"Credits and data dates, The central Apennines: L'Aquila, Amatrice and Norcia\"><span class=\"nx-ah-i\" aria-hidden=\"true\">i<\/span><span class=\"nx-ah-it\">Credits<\/span><\/summary><div class=\"nx-ah-pop\"><ul class=\"nx-ast-credits\"><li data-ast-layer=\"terrain\"><strong>Terrain<\/strong> Surface height in whole metres on the plan's grid of cells about 455 by 605 metres; the box rises from 24 m in the Tiber valley to 2,805 m on the Gran Sasso. <span class=\"nx-ast-cr\">Copernicus DEM GLO-30 (c) DLR e.V. 2010-2014 and (c) Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA. read 2026-10-17.<\/span> <span class=\"nx-ast-est\">Estimate: A digital surface model: trees and roofs are in the heights. Relief exaggeration is printed on the stage.<\/span><\/li><li data-ast-layer=\"events\"><strong>Reference earthquakes<\/strong> The three reference main shocks (L'Aquila 2009, Amatrice 2016, Norcia 2016): origin, magnitude, depth, time and review status from the USGS detail record, the hypocentre drawn below the ground. <span class=\"nx-ast-cr\">U.S. Geological Survey Earthquake Hazards Program. read 2026-10-17.<\/span> <span class=\"nx-ast-est\">Estimate: Reported solutions as reviewed by the USGS; magnitudes and depths carry the USGS stated uncertainties, which the event page links rather than restates.<\/span><\/li><li data-ast-layer=\"quakes-live\"><strong>Earthquakes M4.5 and above, past week (live)<\/strong> Reviewed and automatic seismic solutions of the past week inside the box, with magnitude and depth, read from the world feed and filtered to the box in your browser; the distribution fitter reads the same world feed for its sample. <span class=\"nx-ast-cr\">U.S. Geological Survey; reported detections, automatic solutions preliminary; never a warning. Live: this page requests earthquake.usgs.gov when the layer is opened; the data date is read from metadata.generated and printed with it.<\/span> <span class=\"nx-ast-est\">Estimate: Reported detections; automatic solutions are preliminary; never a warning.<\/span><\/li><li data-ast-layer=\"shakemap\"><strong>USGS ShakeMap intensity contours<\/strong> The ShakeMap MMI contours of each reference event draped on the terrain, read from the cont_mmi.json file the event's detail record names: what one realisation of the sampler's field looked like. <span class=\"nx-ast-cr\">U.S. Geological Survey ShakeMap and U.S. Geological Survey PAGER (preliminary, automatically generated; see the USGS disclaimers), with the product version and time. Live: this page requests earthquake.usgs.gov when the layer is opened; the data date is read from properties.products.shakemap[0].updateTime and printed with it.<\/span> <span class=\"nx-ast-est\">Estimate: Preliminary and automatically generated; PAGER levels are USGS estimates with uncertainty, never a warning or a loss figure of the Academy.<\/span><\/li><\/ul><p class=\"nx-ast-why\">Why this place: One box of about 82 by 77 kilometres holds the 2009 L'Aquila (M6.3), 2016 Amatrice (M6.2) and Norcia (M6.6) earthquakes, the Gran Sasso massif and the normal-fault basins between them. It is the reference case for every method the faculty teaches: magnitude-frequency fitting on a dense catalogue, return periods, probabilistic hazard, model validation, and risk communication after the L'Aquila trial of 2012, which turned on how probabilities were communicated. Data quality: Copernicus DEM GLO-30 covers the relief in two tiles, the USGS detail records of the three main shocks are reviewed, and the live feed is open. It connects to the quantitative-risk-analysis, catastrophe-exposure-modelling, uncertainty-decision, risk-communication-literacy and geospatial-geoai disciplines and to the spatial intelligence pages of the ecosystem sites.<\/p><\/div><\/details><\/div><div class=\"nx-ast-read\" data-ast-out=\"read\" aria-live=\"polite\" hidden><\/div><p class=\"nx-ast-note\" aria-live=\"polite\">Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.<\/p><\/div><\/div><\/div><button type=\"button\" class=\"nx-tw-swap\" data-ra-map-act=\"swap\" aria-label=\"Swap the main view and the inset\"><span class=\"nx-tw-swap-k\" data-tw-swap=\"true\">3D twin<\/span><span class=\"nx-tw-swap-v\">Swap<\/span><\/button><div class=\"nx-ra-tb-insp nx-tw-insp\" aria-live=\"polite\"><\/div><\/div><aside class=\"nx-tw-dock\" data-nx-tabs=\"dock\" aria-label=\"Simulator: instruments, layers and sources\"><div class=\"nx-tw-tabs\" role=\"tablist\" aria-label=\"Simulator panels\"><button type=\"button\" class=\"nx-tw-tab\" role=\"tab\" id=\"nx-tw-t-inst\" aria-controls=\"nx-tw-p-inst\" aria-selected=\"true\" tabindex=\"0\" data-key=\"inst\">Instruments<span>4<\/span><\/button><button type=\"button\" class=\"nx-tw-tab\" role=\"tab\" id=\"nx-tw-t-layers\" aria-controls=\"nx-tw-p-layers\" aria-selected=\"false\" tabindex=\"-1\" data-key=\"layers\">Layers<span>8<\/span><\/button><button type=\"button\" class=\"nx-tw-tab\" role=\"tab\" id=\"nx-tw-t-src\" aria-controls=\"nx-tw-p-src\" aria-selected=\"false\" tabindex=\"-1\" data-key=\"src\">Sources<\/button><\/div><div class=\"nx-tw-pane\" role=\"tabpanel\" id=\"nx-tw-p-inst\" aria-labelledby=\"nx-tw-t-inst\"><div class=\"nx-ast-inst\" data-ast-insts=\"risk-science\"><div class=\"nx-ast-card\" data-ast-inst=\"distribution-fitter\" data-state=\"estimate\"><h3 class=\"nx-h4\">Distribution fitter (magnitude-frequency)<\/h3><p class=\"nx-p\">The Gutenberg-Richter relation fitted by maximum likelihood to a sample of magnitudes, with the cumulative magnitude-frequency curve, the b-value and its standard error, and a completeness magnitude the learner moves to watch the fit move.<\/p><div class=\"nx-ast-inst-sec\" data-ast-tool=\"distribution-fitter\" data-ast-mags=\"[6.3,6.2,6.6]\"><p class=\"nx-ast-note\">The box's catalogue layer waits for its register row, so the fitter runs on the past week's world feed of M4.5 and above (one request to the plan's fixed URL on your press, never polled here) and on the three reference events for the method. Move the completeness magnitude and watch the fit move; the standard error is printed with every value.<\/p><div class=\"nx-ast-cuts\" role=\"group\" aria-label=\"Sample\" data-ast-choice=\"fit\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-choice-v=\"feed\" aria-pressed=\"false\">Fit the past week's world feed<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"mc\" data-min=\"4.5\" data-max=\"6.5\" data-step=\"0.1\" data-unit=\"Mw\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-mc\">Completeness magnitude<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Completeness magnitude: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-mc\" aria-valuemin=\"4.5\" aria-valuemax=\"6.5\" aria-valuenow=\"4.5\" aria-valuetext=\"4.5 Mw\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"mc\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-mc\">4.5 Mw<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Completeness magnitude: more\">+<\/button><\/div><svg class=\"nx-ast-chart\" data-ast-chart=\"mfd\" viewBox=\"0 0 760 220\" role=\"img\" aria-labelledby=\"nx-ast-mfd-rs-t\"><title id=\"nx-ast-mfd-rs-t\">Cumulative magnitude-frequency distribution of 3 magnitudes: log count of events at or above each magnitude, with the Gutenberg-Richter fit b = 1.04 (standard error 0.6) above the completeness magnitude 6.<\/title><text class=\"nx-ast-ch-t\" x=\"46.0\" y=\"212\" text-anchor=\"middle\">M6.0<\/text><text class=\"nx-ast-ch-t\" x=\"279.3\" y=\"212\" text-anchor=\"middle\">M6.5<\/text><text class=\"nx-ast-ch-t\" x=\"512.7\" y=\"212\" text-anchor=\"middle\">M7.0<\/text><text class=\"nx-ast-ch-t\" x=\"746.0\" y=\"212\" text-anchor=\"middle\">M7.5<\/text><text class=\"nx-ast-ch-t\" x=\"42\" y=\"18\" text-anchor=\"end\">3<\/text><text class=\"nx-ast-ch-t\" x=\"42\" y=\"190\" text-anchor=\"end\">1<\/text><circle class=\"nx-ast-ch-p\" cx=\"46.0\" cy=\"106.0\" r=\"2.5\"\/><circle class=\"nx-ast-ch-p\" cx=\"92.7\" cy=\"106.0\" r=\"2.5\"\/><circle class=\"nx-ast-ch-p\" cx=\"139.3\" cy=\"106.0\" r=\"2.5\"\/><circle class=\"nx-ast-ch-p\" cx=\"186.0\" cy=\"137.0\" r=\"2.5\"\/><circle class=\"nx-ast-ch-p\" cx=\"232.7\" cy=\"190.0\" r=\"2.5\"\/><circle class=\"nx-ast-ch-p\" cx=\"279.3\" cy=\"190.0\" r=\"2.5\"\/><circle class=\"nx-ast-ch-p\" cx=\"326.0\" cy=\"190.0\" r=\"2.5\"\/><line class=\"nx-ast-ch-x\" x1=\"46.0\" y1=\"106.0\" x2=\"746.0\" y2=\"190.0\"\/><\/svg><p class=\"nx-ast-out\" aria-live=\"polite\" data-ast-result=\"distribution-fitter\"><strong data-ast-out=\"title\">Reference events<\/strong> <span data-ast-out=\"sum\">Three main shocks, M6.2 to M6.6: the estimator is shown on them for the method only; a fit needs a complete catalogue.<\/span><\/p><div class=\"nx-table-wrap\"><table class=\"nx-table nx-ast-table\" data-stack=\"true\" aria-label=\"Distribution fitter readings\"><thead><tr><th scope=\"col\">Reading<\/th><th scope=\"col\">Value<\/th><\/thead><tbody data-ast-out=\"fit-rows\"><tr><td data-label=\"\">Magnitudes in the extract<\/td><td data-label=\"\">M6.3, M6.2, M6.6<\/td><\/tr><tr><td data-label=\"Reading\">Completeness magnitude used<\/td><td data-label=\"Value\">M6.0 (the extract holds main shocks only)<\/td><\/tr><tr><td data-label=\"Reading\">Maximum-likelihood b-value<\/td><td data-label=\"Value\">1.04, standard error 0.6 with n = 3: an illustration of the estimator only, three events say nothing about the regional b-value<\/td><\/tr><tr><td data-label=\"Reading\">Inter-event times<\/td><td data-label=\"Value\">7.4 years, 0.2 years (too few for an exponential fit)<\/td><\/tr><\/tbody><\/table><\/div><\/div><p class=\"nx-ast-model\">A fit to a sample; its standard error is printed. The box's catalogue layer is pending, so the fitter runs on the past week's world feed of M4.5 and above, read once on the learner's press, and shows the estimator on the three reference events for the method only. <span class=\"nx-ast-est\">A fit to a sample, with its standard error. Catalogue pending: the past week's world feed stands in. Method: Gutenberg, B. and Richter, C. F. (1944), Frequency of earthquakes in California, Bulletin of the Seismological Society of America 34; Aki, K. (1965), Maximum likelihood estimate of b in the formula log N = a - bM and its confidence limits, Bulletin of the Earthquake Research Institute 43; Utsu, T. (1966), A statistical significance test of the difference in b-value between two earthquake groups, Journal of Physics of the Earth 14.<\/span><\/p><\/div><div class=\"nx-ast-card\" data-ast-inst=\"return-period\" data-state=\"estimate\"><h3 class=\"nx-h4\">Return-period calculator<\/h3><p class=\"nx-p\">The annual rate of events at or above a threshold magnitude from the Gutenberg-Richter relation, the return period and the probability of at least one event in an exposure time under a Poisson process, with the rate, the b-value, the threshold and the time on sliders.<\/p><div class=\"nx-ast-inst-sec\" data-ast-tool=\"return-period\"><p class=\"nx-ast-note\">The rate, the b-value, the threshold and the exposure time are on sliders; the rate and b-value start as declared teaching values (not estimates for the box) and take the fitter's values when a fit is made. The calculator shows the difference between a return period and the probability of at least one event in a lifetime.<\/p><div class=\"nx-ast-slider\" data-ast-slider=\"rate\" data-min=\"0.1\" data-max=\"10\" data-step=\"0.1\" data-unit=\"per year\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-rate\">Annual rate of M4.5 and above<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Annual rate of M4.5 and above: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-rate\" aria-valuemin=\"0.1\" aria-valuemax=\"10\" aria-valuenow=\"0.2\" aria-valuetext=\"0.2 per year\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"rate\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-rate\">0.2 per year<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Annual rate of M4.5 and above: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"b\" data-min=\"0.6\" data-max=\"1.4\" data-step=\"0.05\" data-unit=\"per magnitude unit\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-b\">b-value<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"b-value: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-b\" aria-valuemin=\"0.6\" aria-valuemax=\"1.4\" aria-valuenow=\"1\" aria-valuetext=\"1 per magnitude unit\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"b\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-b\">1 per magnitude unit<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"b-value: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"thr\" data-min=\"4.5\" data-max=\"7.5\" data-step=\"0.1\" data-unit=\"Mw\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-thr\">Threshold magnitude<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Threshold magnitude: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-thr\" aria-valuemin=\"4.5\" aria-valuemax=\"7.5\" aria-valuenow=\"6\" aria-valuetext=\"6 Mw\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"thr\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-thr\">6 Mw<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Threshold magnitude: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"years\" data-min=\"1\" data-max=\"100\" data-step=\"1\" data-unit=\"years\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-years\">Exposure time<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Exposure time: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-years\" aria-valuemin=\"1\" aria-valuemax=\"100\" aria-valuenow=\"50\" aria-valuetext=\"50 years\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"years\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-years\">50 years<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Exposure time: more\">+<\/button><\/div><p class=\"nx-ast-out\" aria-live=\"polite\" data-ast-result=\"return-period\"><strong data-ast-out=\"title\">Poisson model<\/strong> <span data-ast-out=\"sum\">With 0.2 events of M4.5 and above a year and b = 1, M6 and above has a return period of 158 years and a 27 percent chance of occurring at least once in 50 years (declared teaching values).<\/span><\/p><div class=\"nx-table-wrap\"><table class=\"nx-table nx-ast-table\" data-stack=\"true\" aria-label=\"Return-period readings\"><thead><tr><th scope=\"col\">Reading<\/th><th scope=\"col\">Value<\/th><\/thead><tbody data-ast-out=\"rp-rows\"><tr><td data-label=\"\">Annual rate of M at or above the threshold<\/td><td data-label=\"\">0.006 per year<\/td><\/tr><tr><td data-label=\"Reading\">Return period<\/td><td data-label=\"Value\">158 years<\/td><\/tr><tr><td data-label=\"Reading\">Probability of at least one in 50 years<\/td><td data-label=\"Value\">27 percent<\/td><\/tr><tr><td data-label=\"Reading\">Probability in one year<\/td><td data-label=\"Value\">0.6 percent<\/td><\/tr><\/tbody><\/table><\/div><\/div><p class=\"nx-ast-model\">A Poisson teaching model: clustering (aftershocks) is not modelled, and the rate and b-value are declared teaching values until the fitter supplies a fit from a sample of thirty or more; not a hazard assessment for any site. <span class=\"nx-ast-est\">A Poisson teaching model, without clustering. Not a hazard assessment for any site. Method: Cornell, C. A. (1968), Engineering seismic risk analysis, Bulletin of the Seismological Society of America 58(5), 1583-1606.<\/span><\/p><\/div><div class=\"nx-ast-card\" data-ast-inst=\"monte-carlo\" data-state=\"estimate\"><h3 class=\"nx-h4\">Monte Carlo sampler (the uncertainty field)<\/h3><p class=\"nx-p\">Synthetic earthquakes drawn from the magnitude-frequency model over the box, the intensity at every hexagonal cell from a published prediction equation with its sigma, and the probability per cell that an intensity threshold is reached in the exposure time, drawn on the terrain with its sampling error.<\/p><div class=\"nx-ast-inst-sec\" data-ast-tool=\"monte-carlo\" data-ast-dmin=\"4.4\" data-ast-dmax=\"8.8\"><p class=\"nx-ast-note\">Change the prior and resample: the field is redrawn on the terrain of the open twin as hexagonal cells of 1.4 km side (about the H3 resolution 7 area), with the sampling error in the read-out; the ShakeMap contours of the three reference events show what one realisation looked like.<\/p><div class=\"nx-ast-slider\" data-ast-slider=\"mcb\" data-min=\"0.6\" data-max=\"1.4\" data-step=\"0.05\" data-unit=\"per magnitude unit\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-mcb\">b-value<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"b-value: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-mcb\" aria-valuemin=\"0.6\" aria-valuemax=\"1.4\" aria-valuenow=\"1\" aria-valuetext=\"1 per magnitude unit\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"mcb\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-mcb\">1 per magnitude unit<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"b-value: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"mmax\" data-min=\"5.5\" data-max=\"7.5\" data-step=\"0.1\" data-unit=\"Mw\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-mmax\">Maximum magnitude<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Maximum magnitude: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-mmax\" aria-valuemin=\"5.5\" aria-valuemax=\"7.5\" aria-valuenow=\"7\" aria-valuetext=\"7 Mw\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"mmax\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-mmax\">7 Mw<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Maximum magnitude: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"mcrate\" data-min=\"0.1\" data-max=\"10\" data-step=\"0.1\" data-unit=\"per year\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-mcrate\">Annual rate of M4.5 and above<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Annual rate of M4.5 and above: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-mcrate\" aria-valuemin=\"0.1\" aria-valuemax=\"10\" aria-valuenow=\"0.2\" aria-valuetext=\"0.2 per year\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"mcrate\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-mcrate\">0.2 per year<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Annual rate of M4.5 and above: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"i0\" data-min=\"4\" data-max=\"9\" data-step=\"1\" data-unit=\"MMI\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-i0\">Intensity threshold<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Intensity threshold: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-i0\" aria-valuemin=\"4\" aria-valuemax=\"9\" aria-valuenow=\"7\" aria-valuetext=\"7 MMI\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"i0\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-i0\">7 MMI<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Intensity threshold: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"n\" data-min=\"200\" data-max=\"4000\" data-step=\"200\" data-unit=\"events\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-n\">Samples<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Samples: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-n\" aria-valuemin=\"200\" aria-valuemax=\"4000\" aria-valuenow=\"2000\" aria-valuetext=\"2000 events\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"n\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-n\">2000 events<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Samples: more\">+<\/button><\/div><div class=\"nx-ast-cuts\" role=\"group\" aria-label=\"Sampler\" data-ast-choice=\"sample\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-choice-v=\"draw\" aria-pressed=\"false\">Draw samples<\/button><\/div><svg class=\"nx-ast-chart nx-ast-svg\" data-ast-chart=\"field\" viewBox=\"0 0 640 635\" role=\"img\" aria-labelledby=\"nx-ast-field-rs-t\" data-nx-z=\"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\"><title id=\"nx-ast-field-rs-t\">Probability that intensity 7 is reached at least once in 50 years, per hexagonal cell of 4 km side: highest 0.27, mean 0.2, from 2,000 synthetic events; darker cells are closer to the highest; a teaching surface, not a hazard map.<\/title><\/svg><p class=\"nx-ast-out\" aria-live=\"polite\" data-ast-result=\"monte-carlo\"><strong data-ast-out=\"title\">Baseline field<\/strong> <span data-ast-out=\"sum\">Probability of intensity 7 and above at least once in 50 years: highest cell 0.27, mean 0.2 over 156 cells of 4 km side, from 2,000 synthetic events (teaching surface, not a hazard map).<\/span><\/p><div class=\"nx-table-wrap\"><table class=\"nx-table nx-ast-table\" data-stack=\"true\" aria-label=\"Monte Carlo readings\"><thead><tr><th scope=\"col\">Reading<\/th><th scope=\"col\">Value<\/th><\/thead><tbody data-ast-out=\"mc-rows\"><tr><td data-label=\"\">Source zone<\/td><td data-label=\"\">the whole box, locations uniform; depths uniform between the reference events' depths<\/td><\/tr><tr><td data-label=\"Reading\">Prior<\/td><td data-label=\"Value\">rate 0.2 per year of M4.5 and above, b = 1, maximum magnitude M7, intensity threshold 7, 50 years (declared teaching values)<\/td><\/tr><tr><td data-label=\"Reading\">Samples<\/td><td data-label=\"Value\">2,000 synthetic events, seed fixed at build<\/td><\/tr><tr><td data-label=\"Reading\">Highest cell probability<\/td><td data-label=\"Value\">0.27 (sampling error of the exceedance fraction up to 0.004)<\/td><\/tr><tr><td data-label=\"Reading\">Mean cell probability<\/td><td data-label=\"Value\">0.2<\/td><\/tr><\/tbody><\/table><\/div><\/div><p class=\"nx-ast-model\">A teaching probabilistic surface, not a hazard map: the source zone is the whole box, the rate and the maximum magnitude are declared teaching values on the sliders, and the published GEM map's row is pending. Every parameter is on screen and the sampling error is printed; the ShakeMap contours of the three reference events are drawn beside the field as what one realisation looked like. <span class=\"nx-ast-est\">A teaching surface, not a hazard map. Sampling error printed with every field. Method: Cornell (1968); Allen, T. I., Wald, D. J. and Worden, C. B. (2012), Intensity attenuation for active crustal regions, Journal of Seismology 16; McGuire, R. K. (2004), Seismic Hazard and Risk Analysis, EERI monograph MNO-10.<\/span><\/p><\/div><div class=\"nx-ast-card\" data-ast-inst=\"scenario-tree\" data-state=\"estimate\"><h3 class=\"nx-h4\">Scenario tree<\/h3><p class=\"nx-p\">A decision tree with a decision node (act now or wait) and a chance node (the event comes or not), solved by backward induction for the expected value of each branch and the value of perfect information, with the probability and the consequence values on sliders.<\/p><div class=\"nx-ast-inst-sec\" data-ast-tool=\"scenario-tree\"><p class=\"nx-ast-note\">A two-branch tree with one chance node: move the probability and the three consequence values (relative loss units, 0 to 100) and watch the preferred branch and the value of perfect information change. The probability starts from the calculator; the consequence values are declared teaching values and nothing in the twin supplies them.<\/p><div class=\"nx-ast-slider\" data-ast-slider=\"p\" data-min=\"0\" data-max=\"100\" data-step=\"1\" data-unit=\"percent\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-p\">Probability of the event<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Probability of the event: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-p\" aria-valuemin=\"0\" aria-valuemax=\"100\" aria-valuenow=\"6\" aria-valuetext=\"6 percent\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"p\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-p\">6 percent<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Probability of the event: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"cost\" data-min=\"0\" data-max=\"100\" data-step=\"1\" data-unit=\"units\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-cost\">Cost of acting now<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Cost of acting now: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-cost\" aria-valuemin=\"0\" aria-valuemax=\"100\" aria-valuenow=\"20\" aria-valuetext=\"20 units\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"cost\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-cost\">20 units<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Cost of acting now: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"lossAct\" data-min=\"0\" data-max=\"100\" data-step=\"1\" data-unit=\"units\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-lossAct\">Loss if the event comes after acting<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Loss if the event comes after acting: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-lossAct\" aria-valuemin=\"0\" aria-valuemax=\"100\" aria-valuenow=\"30\" aria-valuetext=\"30 units\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"lossAct\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-lossAct\">30 units<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Loss if the event comes after acting: more\">+<\/button><\/div><div class=\"nx-ast-slider\" data-ast-slider=\"lossWait\" data-min=\"0\" data-max=\"100\" data-step=\"1\" data-unit=\"units\"><span class=\"nx-ast-slider-l\" id=\"nx-ast-sl-lossWait\">Loss if the event comes after waiting<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"-1\" aria-label=\"Loss if the event comes after waiting: less\">-<\/button><span class=\"nx-ast-slider-t\" role=\"slider\" tabindex=\"0\" aria-labelledby=\"nx-ast-sl-lossWait\" aria-valuemin=\"0\" aria-valuemax=\"100\" aria-valuenow=\"100\" aria-valuetext=\"100 units\"><span class=\"nx-ast-slider-f\" data-ast-fill=\"lossWait\"><\/span><span class=\"nx-ast-slider-v\" data-ast-out=\"slider-lossWait\">100 units<\/span><\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-step=\"1\" aria-label=\"Loss if the event comes after waiting: more\">+<\/button><\/div><p class=\"nx-ast-out\" aria-live=\"polite\" data-ast-result=\"scenario-tree\"><strong data-ast-out=\"title\">Backward induction<\/strong> <span data-ast-out=\"sum\">Act now: -21.8; wait: -6; the preferred branch is to wait and perfect information would be worth 3 (declared teaching values).<\/span><\/p><div class=\"nx-table-wrap\"><table class=\"nx-table nx-ast-table\" data-stack=\"true\" aria-label=\"Scenario tree readings\"><thead><tr><th scope=\"col\">Node<\/th><th scope=\"col\">Value<\/th><\/thead><tbody data-ast-out=\"tree-rows\"><tr><td data-label=\"\">Chance node<\/td><td data-label=\"\">an M6 and above event within 10 years, p = 0.06 from the calculator's teaching values<\/td><\/tr><tr><td data-label=\"Node\">Act now (retrofit)<\/td><td data-label=\"Value\">cost 20, loss if the event comes 30: expected value -21.8<\/td><\/tr><tr><td data-label=\"Node\">Wait<\/td><td data-label=\"Value\">no cost, loss if the event comes 100: expected value -6<\/td><\/tr><tr><td data-label=\"Node\">Preferred branch<\/td><td data-label=\"Value\">wait<\/td><\/tr><tr><td data-label=\"Node\">Value of perfect information<\/td><td data-label=\"Value\">3<\/td><\/tr><tr><td data-label=\"Node\">Probability at which the branches tie<\/td><td data-label=\"Value\">0.29<\/td><\/tr><\/tbody><\/table><\/div><\/div><p class=\"nx-ast-model\">A teaching tree on learner inputs: the probability starts from the return-period calculator and the consequence values are declared teaching values in relative loss units; no consequence value is supplied by the twin. <span class=\"nx-ast-est\">A teaching tree on learner inputs. Method: Raiffa, H. (1968), Decision Analysis: Introductory Lectures on Choices under Uncertainty, Addison-Wesley; Howard, R. A. (1966), Decision analysis: applied decision theory, Proceedings of the Fourth International Conference on Operational Research.<\/span><\/p><\/div><\/div><\/div><div class=\"nx-tw-pane\" role=\"tabpanel\" id=\"nx-tw-p-layers\" aria-labelledby=\"nx-tw-t-layers\" hidden><div class=\"nx-tw-layers\" data-tw-layers=\"true\"><p class=\"nx-tw-empty\">8 layers switch here once the map runs.<\/p><\/div><\/div><div class=\"nx-tw-pane\" role=\"tabpanel\" id=\"nx-tw-p-src\" aria-labelledby=\"nx-tw-t-src\" hidden><p class=\"nx-tw-empty\">6 live and observed layers and 2 from the twin. Data: Natural Earth, USGS, GDACS, CIESIN, CIESIN\/Columbia, VIIRS, NASA EONET, U.S. Geological Survey Earthquake Hazards Program, U.S. Geological Survey ShakeMap and U.S. Geological Survey P. Each layer's provider, licence and date are listed here once the map runs.<\/p><div class=\"nx-tw-cred\" data-tw-cred=\"true\"><\/div><\/div><\/aside><\/div><p class=\"nx-ra-map-note\" role=\"status\">The map loads from this site as it nears the view (MapLibre 6.9.1, checked by hash).<\/p><\/div><\/div><\/div><\/section><section id=\"nx-sec-brief-answer\" class=\"nx-as\" data-as=\"intro\" aria-labelledby=\"nx-sec-brief-answer-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-brief-answer-h\" class=\"nx-as-title\">What the faculty teaches.<\/h2><\/div><div class=\"nx-as-body\"><div class=\"nx-as-intro\"><p class=\"nx-as-stand\">On the central Apennines, where L'Aquila 2009, Amatrice 2016 and Norcia 2016 stand on the terrain, the stage carries four methods: a Gutenberg-Richter fit with its standard error, a return-period calculator after Cornell (1968), a Monte Carlo sampler that draws a probability field on hexagonal cells with its sampling error, and a scenario tree solved by backward induction, read under ISO 31000:2018 and ISO\/IEC 31010:2019. A graduate can fit, sample, decide and communicate with the uncertainty on the page.<\/p><div class=\"nx-as-side\"><p class=\"nx-as-brief\">Faculty of Risk Science and Methods groups 17 disciplines into 4 departments. 3 of the 22 courses whose primary discipline is here are open for enrolment and the rest are in preparation; all of them are taught on the Risk Academy main site, so your record stays in one place.<\/p><figure class=\"nx-as-z\"><h3 class=\"nx-as-h3\">A graduate can<\/h3><ul class=\"nx-as-list\"><li>Fit a magnitude-frequency or loss distribution to a sample by maximum likelihood, print its standard error and say when the sample is too small to call the fit an estimate.<\/li><li>Convert a rate into a return period and into the probability of at least one event in a lifetime, and explain to a non-specialist why the two are not the same number.<\/li><li>Build a probabilistic surface by Monte Carlo sampling with every parameter and the sampling error on screen, and say why it is a teaching surface and not a hazard map.<\/li><li>Solve a decision tree by backward induction, compute the value of perfect information and show how a changed probability moves the preferred branch.<\/li><li>Read an index, a model or a dataset against its provenance, licence and validation record and write the finding a board, a regulator or a planner can act on.<\/li><\/ul><\/figure><\/div><\/div><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-departments\" class=\"nx-as\" data-as=\"cards\" aria-labelledby=\"nx-sec-departments-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-departments-h\" class=\"nx-as-title\">4 departments, 17 disciplines.<\/h2><p class=\"nx-as-lede\">Numbered as on the faculty figure at the top of the page. Each card counts the department's disciplines and the courses whose primary discipline is there.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-disciplines\" class=\"nx-as\" data-as=\"tiles\" aria-labelledby=\"nx-sec-disciplines-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-disciplines-h\" class=\"nx-as-title\">Disciplines by status.<\/h2><p class=\"nx-as-lede\">Each discipline once, in its department. A mark is one primary course, filled when it is open.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-frontiers\" class=\"nx-as\" data-as=\"frontiers\" aria-labelledby=\"nx-sec-frontiers-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-frontiers-h\" class=\"nx-as-title\">Where the field is moving.<\/h2><p class=\"nx-as-lede\">Emerging areas, the technologies changing practice and the bodies that set the field's methods, from the faculty's expert review. Named for orientation; none of them endorses the Academy.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-frontier\" class=\"nx-as\" data-as=\"timeline\" aria-labelledby=\"nx-sec-frontier-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-frontier-h\" class=\"nx-as-title\">What is changing now.<\/h2><p class=\"nx-as-lede\">The newest developments cited on the faculty's discipline pages, sources checked on 16 October 2026. Each links to its discipline and its source.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-hub\" class=\"nx-as\" data-as=\"hub\" aria-labelledby=\"nx-sec-hub-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-hub-h\" class=\"nx-as-title\">The faculty hub.<\/h2><p class=\"nx-as-lede\">News and research as they are published, per discipline, with the faculty's other tools.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"XVNdb9swDPwrhJ+denvZsCEJUHRF14eu2SewvdEyHbORJY2knWa\/fpCdpmnfLJG6O97Ry4ZHcB5VV0V4XAguWqJGC2jQ8HRcFcK6W6hjCo5eFRexWRWPmzfby\/j575vflw8bvHbY4V39rv7zRfbx\/l+7+6APfbW5\/XX1ab8q1st0xmn7BfXJDsX6R0cQaE9qIKSE4roSklASDqZlrilgaKAbegxsKIwBhFIUU2ijwNv30LA6Tp4DaQmEroM9WwdsChoHcTQhNGhUgo\/YQEdCF7DEM0mew66ATqhdFZ1Z0o9VlR1QdNhQf7hwsa\/OLame9C66oa6K9X2iANbRaY6JdNLfDfWywvWySi9taHXRxmjF+joYSRJWgkwBPQbcUk\/BJpDprhXsaR9lpyXcJxI0jgH9XMxd9aDZAQUXg3EY2A4l3MWGznpG9NxML0u4VB0Eg6MSOPNnsPxUon9mdYO3QaiErwMGY0PjkZ7w0B+U53g0V9TYaQlXaKgmMXVUAj2mqIPMEYyDDyRYs2c7QJ+1eQ7bEr5lQA4NO7QoM6SLfYrKRnMhZ7uRWE+vM1NOnxyqcdjO6CTcZoR5vNvpskfXcSDwhBJyZ96ZF\/pL+BkciSEHO8ybQo6VY4Aed5O8744CCsfXM0u222iScBzCxb4fwlHFs4+ejQTd4diVSBylU0tNHY4cB0EPx\/Uq4foxkRg8DM3ZKpBnN6WQR7yhqAmN0Z8Nc41iHcRaScZnETcUL2+P5PlXhm0cc+QTU5I40vQ99Xp2FPQ8lzZKP2OpYWhQmtmBaW9iOkU6bXjV8Lj+Dw==\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-programmes\" class=\"nx-as\" data-as=\"route\" aria-labelledby=\"nx-sec-programmes-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-programmes-h\" class=\"nx-as-title\">Where to start, and where it leads.<\/h2><p class=\"nx-as-lede\">The faculty's entry course, then its flagship pathways and the Academy flagship in Nexus-Based Risk Management.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-courses\" class=\"nx-as\" data-as=\"courses\" aria-labelledby=\"nx-sec-courses-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-courses-h\" class=\"nx-as-title\">3 open, 19 in preparation.<\/h2><p class=\"nx-as-lede\">Courses with their primary discipline in this faculty. Open courses link to the catalogue; the others are listed so you can plan.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-standards\" class=\"nx-as\" data-as=\"cards\" aria-labelledby=\"nx-sec-standards-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-standards-h\" class=\"nx-as-title\">The standards it teaches toward.<\/h2><p class=\"nx-as-lede\">The Nexus Standards subsites whose standards the faculty's disciplines teach, by the Academy's proposed mapping.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-practice\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-practice-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-practice-h\" class=\"nx-as-title\">Where these subjects are practised.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-labs\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-labs-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-labs-h\" class=\"nx-as-title\">Where the faculty practises.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-nexus\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-nexus-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-nexus-h\" class=\"nx-as-title\">Nexus programmes that span faculties.<\/h2><p class=\"nx-as-lede\">Each programme joins disciplines from more than one faculty and is hosted by an institute on the main site; it is listed on every faculty it spans.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><ul class=\"nx-fc nx-fs\"><li><a class=\"nx-fc-a\" href=\"https:\/\/risksacademy.com\/institutes\/technology-society\/\"><span class=\"nx-fc-p\">Technology, Society and Risk<\/span><span class=\"nx-fc-t\">Space and food<\/span><span class=\"nx-fc-s\">Studies how satellite positioning and Earth observation support, and expose, food production and food security monitoring.<\/span><span class=\"nx-fc-x\">3 disciplines in 3 faculties<\/span><\/a><\/li><\/ul><\/figure><\/div><\/div><\/section><section id=\"nx-sec-institutes\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-institutes-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-institutes-h\" class=\"nx-as-title\">Institutes that convene the faculty.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><div class=\"nx-cards\" data-cols=\"3\"><a class=\"nx-card\" href=\"https:\/\/risksacademy.com\/nrm\/\"><span class=\"nx-eyebrow\">Institute<\/span><h3 class=\"nx-h4 nx-mt-2\">Institute for Nexus-Based Risk Management<\/h3><p class=\"nx-p nx-mt-1\">The institute is the cross-faculty home of Nexus-Based Risk Management, the Academy's paradigm.<\/p><\/a><\/div><\/figure><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-terms-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-terms-h\" class=\"nx-as-title\">Terms used on this page.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section class=\"nx-as\" data-as=\"related\" aria-labelledby=\"nx-as-rel\"><div class=\"nx-container\"><h2 id=\"nx-as-rel\" class=\"nx-as-title nx-as-small\">Around Faculty of Risk Science and Methods.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/risksacademy.com\/risk-science\/departments\/\">Departments <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/departments\/risk-management\/\">Department of Risk Management <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/departments\/quantitative-risk\/\">Department of Quantitative Risk Analysis <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/departments\/decision-behaviour-communication\/\">Department of Decision, Behaviour and Communication <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/departments\/geospatial-data\/\">Department of Geospatial and Risk Data Science <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/faculties\/\">All faculties <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/programme\/\">The programme <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-1\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Faculty of Risk Science and Methods<\/span><h2 id=\"nx-close-1\" class=\"nx-section-title\">Start in one department.<\/h2><p class=\"nx-lede\">Each department lists its disciplines, courses and pathways; each discipline its scope, labs, technology and key terms.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/risksacademy.com\/risk-science\/departments\/risk-management\/\">Open Department of Risk Management <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/risksacademy.com\/courses\/\">Browse the course catalogue<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"DefinedTermSet\",\"@id\":\"https:\/\/risksacademy.com\/risk-science\/#terms\",\"name\":\"Key terms: Faculty of Risk Science and 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Methods&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":162,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_buddyx_sub_header_visibility":"","_buddyx_sub_header_title_visibility":"","_hide_show_side_panel":"","_buddyxpro_page_sidebar":"","_buddyxpro_page_disable_header":"","_buddyxpro_page_disable_footer":"","_buddyxpro_page_content_width":"","_buddyxpro_page_header_style":"","_buddyxpro_page_color_mode":"","_buddyxpro_page_loader":"","footnotes":""},"class_list":["post-60","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/pages\/60","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/comments?post=60"}],"version-history":[{"count":0,"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/pages\/60\/revisions"}],"wp:attachment":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/media?parent=60"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}