Distributed Volcanic Fields (DVFs), consisting of clusters of monogenetic edifices (i.e., created in a single eruption), are one of the most common
\nexpressions of volcanism on planetary surfaces across the solar system. Often associated with by mafic rock compositions on Earth, DVFs record a wide
\nvariety of eruptive styles and are very useful when interrogating the eruptive history of a region, helping establish recurrence intervals, volumetric
\nresurfacing rates and chemical evolution of regional magmatic systems. Morphometric studies ca n be conducted to extract morphometric parameters of
\nindividual vents (e.g., height-to-width ratio, crater depth, average slope, drainage density) from high-resolution digital elevation models (DEMs) and link
\nthem to syn- and post-eruptive processes. Existing studies often focus on targeted locations and use disparate DEM types with variable spatial
\nresolution, making direct comparison between individual DVFs difficult and limiting the scope of their interpretations. In this project, we will be using a
\ncustom automated algorithm to extract morphometric data from DEMs from a large number of individual vents in Holocene volcanic fields on Earth.
\nSpecifically, we will use EarthDEM mosaiced data, which are produced from high-resolution satellite imagery to >35 different volcanic fields all over
\nthe world. Using this extensive database, we will identify quantitative and meaningful links between observed morphology and eruptive and erosional
\nprocesses, in conjunction with other information such as geochemical, petrological and mineralogical data, relative and absolute ages, eruptive styles,
\netc. We will generate of a global model for geomorphic aging of cinder cones on Earth, which can be used to estimate the relative age of individual vents
\nwhen no other data is available.<\/p>\n
The student will learn to access, download and manipulate EarthDEM high-resolution models. They will familiarize themselves with the automated
\nalgorithm and help set up a pipeline for batch processing of large amounts of data using NAU\u2019s High performance Computer cluster, Monsoon. They will
\ncompile the data to create a comprehensive master database, adding the non-morphometric data for each volcanic field. This database, now searchable
\nusing a variety of parameters (volcanic cluster name, geographic region, major rock type, etc.), will be used to perform multi-parameter statistical
\nanalysis (e.g., principal component analysis or PCA) to identify parameters that are linked to one another. Depending on progress, the student will assist
\nin the definition of a quantitative predictive model to estimate the absolute and\/or relative geomorphic age of volcanic vents, using only morphometric
\nparameters. Several models may need to be produced to reflect Earth\u2019s large diversity of climates.<\/p>\n
We expect this work will lead to the creation of a database of morphometric parameters for distributed volcanic fields on Earth. This database will
\nconstitute the basis for statistical analysis and investigation of eruptive processes and will grow as individual volcanic fields are analyzed. Depending on
\nstudent progress, we expect this work will lead to: (1) a conference abstract presenting the results of the study (pending available funds, the student will
\nbe able to present those results at the conference); and (2) the publication of a scientific paper in a peer-reviewed journal (on which the student will be an
\nauthor), detailing the results of the morphometric study and clustering analyses on several volcanic fields on Earth.<\/p>\n","protected":false},"excerpt":{"rendered":"
Project 8 Distributed Volcanic Fields (DVFs), consisting of clusters of monogenetic edifices (i.e., created in a single eruption), are one of the most common expressions of volcanism on planetary surfaces across the solar system. Often associated with by mafic rock compositions on Earth, DVFs record a wide variety of eruptive styles and are very useful […]<\/p>\n","protected":false},"author":575,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","ring_central_script_selection":"","footnotes":""},"class_list":["post-2231","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/pages\/2231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/users\/575"}],"replies":[{"embeddable":true,"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/comments?post=2231"}],"version-history":[{"count":4,"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/pages\/2231\/revisions"}],"predecessor-version":[{"id":2316,"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/pages\/2231\/revisions\/2316"}],"wp:attachment":[{"href":"https:\/\/in.nau.edu\/nasa-spacegrant\/wp-json\/wp\/v2\/media?parent=2231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}