{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T01:37:00Z","timestamp":1790386620415,"version":"4.1.0"},"reference-count":72,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"2","funder":[{"DOI":"10.13039\/100008902","name":"Los Alamos National Laboratory","doi-asserted-by":"publisher","award":["20170103DR"],"award-info":[{"award-number":["20170103DR"]}],"id":[{"id":"10.13039\/100008902","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008902","name":"Los Alamos National Laboratory","doi-asserted-by":"publisher","award":["DE-AC52-06NA25396"],"award-info":[{"award-number":["DE-AC52-06NA25396"]}],"id":[{"id":"10.13039\/100008902","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008902","name":"Los Alamos National Laboratory","doi-asserted-by":"publisher","award":["20180621ECR"],"award-info":[{"award-number":["20180621ECR"]}],"id":[{"id":"10.13039\/100008902","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM\/ASA J. Uncertainty Quantification"],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:p>Fractures form the main pathways for flow of fluids and transport of constituents carried by these fluids in fractured subsurface media. The majority of flow and transport occurs in the primary subnetwork or backbone, which is a subset of the fracture network. Understanding characteristics of the fracture network flow and transport backbones is thus of great importance towards improving efficiency of subsurface applications, such as aquifer management, hydrocarbon extraction, and long- term storage of spent nuclear fuel. We propose a method for identifying these backbones that also quantifies uncertainty over all possible backbones. Our approach treats the backbone identification problem as a probabilistic clustering problem. We develop a probabilistic generative model to simulate backbones that are induced acyclic flow networks. A user-controlled parameter determines the size of the identified backbones, with small backbones having good agreement with early particle arrival times and larger backbones having good agreement with early and late particle arrival times. We demonstrate our method on a representative fracture network with 264 fractures. Our method discovers flow channelization in backbones roughly 10%--50% the size of the original network, supporting previous experiments and discrete fracture network simulations. To the best of our knowledge, our method is the first backbone identification method to quantify uncertainty over backbones.<\/jats:p>","DOI":"10.1137\/19m1279265","type":"journal-article","created":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T10:10:38Z","timestamp":1587550238000},"page":"573-600","source":"Crossref","is-referenced-by-count":11,"title":["A Probabilistic Clustering Approach for Identifying Primary Subnetworks of Discrete Fracture Networks with Quantified Uncertainty"],"prefix":"10.1137","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4681-091X","authenticated-orcid":true,"given":"Dave","family":"Osthus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey D.","family":"Hyman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satish","family":"Karra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nishant","family":"Panda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gowri","family":"Srinivasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2020,4,22]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1029\/91WR01404"},{"key":"atypb2","unstructured":"H. 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