{
  "abstract": "The ensemble smoother with multiple data assimilation (ES-MDA) is an algorithmic framework for the ensemble-based solution of inverse problems in reservoir engineering (and beyond). ES-MDA gradually transitions a prior ensemble to a posterior ensemble. The details of how this transition, or \"multiple data assimilation,\" is implemented defines the accuracy and computational cost of ES-MDA. We show that many popular, adaptive variants of ES-MDA can be understood within a simple three-step framework: inflation proposal, pre-analysis revision, and post-analysis revision. The three steps interact to resolve a trade-off between accuracy (many assimilations with small updates) and efficiency (few assimilations with large updates), inherent to ES-MDA. We then present a new adaptive and accuracy-aware method, ES-MDA-A2, that combines large updates with a \"catch-up\" mechanism that decreases the update size allowing for additional assimilations if the accuracy is low. ES-MDA-A2 requires only two inputs: a targeted accuracy and a maximum number of data assimilations. We test existing and new ES-MDA variants in systematic numerical experiments with a toy model, two electromagnetic inversions with field data, and a subsurface flow reservoir simulation. We find that ES-MDA-A2 resolves the accuracy-efficiency trade-off differently from existing methods, leading to accurate inversions at a reasonable computational cost in all experiments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.16434",
  "author_total": 7,
  "authors": [
    {
      "name": "Kyle Ivey"
    },
    {
      "name": "Matthias Morzfeld"
    },
    {
      "name": "Chaoyi Wang"
    },
    {
      "name": "Christina Morency"
    },
    {
      "name": "Christopher S. Sherman"
    },
    {
      "name": "Robert Mellors"
    },
    {
      "name": "Joshua A. White"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9f1xwfg08j9pr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16434",
  "fulltext_url": null,
  "id": "9f1xwfg08j9pr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16434",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.geo-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.16434",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9f1xwfg08j9pr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:41Z",
      "native_id": "2609.16434",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16434"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Adaptive and accuracy-aware multiple data assimilation in a three step framework",
  "title_text": "Adaptive and accuracy-aware multiple data assimilation in a three step framework",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:41Z",
  "url": "https://research.draconitic.com/paper/9f1xwfg08j9pr",
  "venue": "arXiv",
  "versions": []
}