{"paper":{"title":"Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.data-an","physics.geo-ph","stat.ML"],"primary_cat":"eess.SP","authors_text":"A. A. Delorey, B. Yuan, C. N. L. Gammans, G. D. Guthrie, J. D. Webster, M. K. Mudunuru, O. E. Marcillo, P. A. Johnson, P. M. Roberts, S. Karra, Y. J. Tan","submitted_at":"2018-10-01T15:47:34Z","abstract_excerpt":"We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimay\\'{o} geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of $\\mathrm{CO}_2$-rich fluids from depth, which has relevance to leak monitoring in a $\\mathrm{CO}_2$ sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.01488","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}