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Advancing correlation-based Bayesian modeling of the geomagnetic field to include paleomagnetic sediment records

Global reconstructions of the geomagnetic core-field on millennial timescales have broad applications e.g. for studying characteristics of the geodynamo process and Earth's core dynamics; for comparison to numerical dynamo simulations; as a dating tool for sediment cores, volcanic material and archaeological artifacts; or to study cosmogenic isotope production rates. These models are inferred from two classes of data: Data from materials with thermoremanent magnetization, such as volcanic rocks, bricks or burnt clay fragments from archaeological sites, and data from marine or lacustrine sediments with embedded magnetic particles. In an ongoing project, a novel Bayesian and correlation-based modeling method for the archeomagnetic core-field was developed. This method provides robust and realistic uncertainty estimates, together with an improved field model. However, the method is based only on the former class of data. This is due to challenges associated with modeling sediment records:

  1. In sediment records of the magnetic field, intensity and declination are only known relative to the rest of the core. A calibration is necessary.
  2. Data along a single core cannot be considered independent and their temporal errors are correlated.
  3. Sediment records inevitably present smoothed versions of magnetic field variations.

The aim of this project is to overcome these challenges and to extend the developed correlation-based modeling method to be applicable to sediment records as well. This includes the Bayesian modeling of dating uncertainties and sedimentation related processes, such as smoothing. The extended framework will not only allow the exploitation of all available data for the Holocene in a consistent manner, but also to apply the method further back in time, when fewer thermoremanent records are available and the database is dominated by sediments.

Time Frame

2022 – 2024


Deutsche Forschungsgemeinschaft

Principal Investigators

  • M. Holschneider (University of Potsdam)
  • M. Korte (GFZ)


  • Maximilian Schanner (University of Potsdam),
  • Lukas Bohsung (GFZ)


  • Dr. Maxwell Brown (University of Minnesota)


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