Joint two-dimensional inversion of magnetotelluric and gravity data using correspondence maps

Joint two-dimensional inversion of magnetotelluric and gravity data using correspondence maps Summary An accurate characterization of subsurface targets relies on the interpretation of multiple geophysical properties and their relationships. There are mainly two links to jointly invert different geophysical parameters: structural and petrophysical relationships. Structural approaches aim at minimizing topological differences and are widely popular since they need only a few assumptions about models. Conversely, methods based on petrophysical links rely mostly on the property values themselves and can provide a strong coupling between models, but they need to be treated carefully because specific direct relationship must be known or assumed. While some petrophysical relationships are widely accepted, it remains the question whether we may be able to detect them directly from the geophysical data. Currently, there is no reported development that takes full advantage of the flexibility of jointly estimating in-situ empirical relationships and geophysical models for a given geological scenario. We thus developed an algorithm for the two dimensional joint inversion of gravity and magnetotelluric data that seeks simultaneously for a density-resistivity relationship optimal for each studied site described trough a polynomial function. The iterative two-dimensional scheme is tested using synthetic and field data from Cerro Prieto, Mexico. The resulting models show an enhanced resolution with an increased structural and petrophysical correlation. We show that by fitting a functional relationship we increased significantly the coupled geological sense of the models at a little cost in terms of data misfit. joint inversion, correspondence maps, gravity, magnetotelluric © The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geophysical Journal International Oxford University Press

Joint two-dimensional inversion of magnetotelluric and gravity data using correspondence maps

Loading next page...
 
/lp/ou_press/joint-two-dimensional-inversion-of-magnetotelluric-and-gravity-data-vaPZsD4kUe
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society.
ISSN
0956-540X
eISSN
1365-246X
D.O.I.
10.1093/gji/ggy195
Publisher site
See Article on Publisher Site

Abstract

Summary An accurate characterization of subsurface targets relies on the interpretation of multiple geophysical properties and their relationships. There are mainly two links to jointly invert different geophysical parameters: structural and petrophysical relationships. Structural approaches aim at minimizing topological differences and are widely popular since they need only a few assumptions about models. Conversely, methods based on petrophysical links rely mostly on the property values themselves and can provide a strong coupling between models, but they need to be treated carefully because specific direct relationship must be known or assumed. While some petrophysical relationships are widely accepted, it remains the question whether we may be able to detect them directly from the geophysical data. Currently, there is no reported development that takes full advantage of the flexibility of jointly estimating in-situ empirical relationships and geophysical models for a given geological scenario. We thus developed an algorithm for the two dimensional joint inversion of gravity and magnetotelluric data that seeks simultaneously for a density-resistivity relationship optimal for each studied site described trough a polynomial function. The iterative two-dimensional scheme is tested using synthetic and field data from Cerro Prieto, Mexico. The resulting models show an enhanced resolution with an increased structural and petrophysical correlation. We show that by fitting a functional relationship we increased significantly the coupled geological sense of the models at a little cost in terms of data misfit. joint inversion, correspondence maps, gravity, magnetotelluric © The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Geophysical Journal InternationalOxford University Press

Published: May 18, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off