English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In a previous paper, we introduced a framework for carrying outpetrophysically and geologically guided geophysical inversions. In thatframework, petrophysical and geological information is modelled with a GaussianMixture Model (GMM). In the inversion, the GMM serves as a prior for thegeophysical model. The formulation was confined to problems in which a singlephysical property model was sought, with a single geophysical dataset. In thispaper, we extend that framework to jointly invert multiple geophysical datasetsthat depend on multiple physical properties. The petrophysical and geologicalinformation is used to couple geophysical surveys that, otherwise, rely onindependent physics. This requires advancements in two areas. First, anextension from a univariate to a multivariate analysis of the petrophysicaldata, and their inclusion within the inverse problem, is necessary. Second, weaddress the practical issues of simultaneously inverting data from multiplesurveys and finding a solution that acceptably reproduces each one, along withthe petrophysical and geological information. To illustrate the efficacy of ourapproach and the advantages of carrying out multi-physics inversions, we invertsynthetic gravity and magnetic data associated with a kimberlite deposit. Thekimberlite pipe contains two distinct facies embedded in a host rock. Invertingthe datasets individually leads to a binary geological model: background orkimberlite. A multi-physics inversion, with petrophysical information,differentiates between the two main kimberlite facies of the pipe. Through thisexample, we also highlight the capabilities of our framework to work withinterpretive geologic assumptions when minimal quantitative information isavailable. In those cases, the dynamic updates of the Gaussian Mixture Modelallow us to perform multi-physics inversions by learning a petrophysical model.

Petrophysically and geologically guided multi-physics inversion using a dynamic Gaussian mixture model