Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

A New Approach to Transforming Airborne Electromagnetic Data into Models of Sediment Type.

Created on 22 Sep 2026

Authors

Rebecca Prentice, Rosemary Knight, Peter Kitanidis

Published in

Ground water. Sep 22, 2026. Epub Sep 22, 2026.

Abstract

The availability of 20,000 line-kilometers of airborne electromagnetic (AEM) data and the digitization of driller's logs across the Central Valley of California enables the transformation of AEM measurements of electrical resistivity into sediment type descriptions. A critical challenge is establishing the rock physics transform that relates resistivity to sediment type. Two approaches have been applied in the Central Valley; they differ in the model used to link resistivity to sediment type and in the level of confidence placed in the information in the driller's logs, with the latter determining the level of spatial heterogeneity allowed in the transform parameters. We developed a new methodology that builds on the strengths of each approach. Applied in the Kaweah Subbasin in California, we used resistivity profiles derived from AEM data, sediment type descriptions from driller's logs, and water level data from wells. The methodology incorporates the physics of the AEM measurement and solves for transform parameters with a scale of spatial variability determined through an adjustable hyperparameter that accounts for the presumed quality of the driller's logs. The derived transform was applied to all resistivity data from the AEM survey in the subbasin to generate a model of sediment type. When we compare our model with those generated using established methods, we conclude that incorporating the physics of the measurement provides more accurate estimates of the percentage of coarse-grained materials, and that spatial variability in the transform should be accommodated in a way that reflects the quality of the driller's logs.

PMID:
42770940
Bibliographic data and abstract were imported from PubMed on 22 Sep 2026.

Read full publication at:
Please sign in to see all details.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Reviewers' rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 8
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement