Multi-ensemble Bayesian Analysis of Inclusion-based Rock-physics Models

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Spikes, Mrinal K. Sen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4613043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract A statistics-based approach to rock physics often includes the calculation of a series of simulations to fit data along with probability associated with the modeling to characterize uncertainty. We present a Bayesian approach to determine the most probable rock-physics model (e.g., inclusion based). Our results also feature combinations of highly probable model inputs from posterior distributions; these combinations result from using of many different sets of input values from a prior distribution where each set corresponds to an ensemble. Exhaustive sampling allows for the calculation of the full posterior distribution for each ensemble. We demonstrate this method using two inclusion-based rock-physics models, the self-consistent and differential effective medium models along with a carbonate laboratory data set. Results indicate that the latter of the two models is the most probable. Analyses of the underlying model inputs indicate multiple but distinct clusters among those inputs. The problem is computationally demanding and requires parallel computation for tractability. Results from this work are applicable to data sets with similar velocity-porosity trends. More generally, the method is applicable to any other data set and relevant rock-physics models of interest. Rock physics Bayesian analysis Informed distributions Posterior combinations Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Aug, 2024 Reviews received at journal 23 Jul, 2024 Reviews received at journal 21 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviews received at journal 25 Jun, 2024 Reviewers agreed at journal 25 Jun, 2024 Reviewers invited by journal 24 Jun, 2024 Editor assigned by journal 24 Jun, 2024 Editor invited by journal 24 Jun, 2024 Submission checks completed at journal 24 Jun, 2024 First submitted to journal 20 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4613043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":325301028,"identity":"353fda40-6e15-41ed-a3e1-b5d263cca52f","order_by":0,"name":"Kyle T. 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