Mantle viscosity inference: a comparison between simulated annealing and neighbourhood algorithm inversion methods

Astronomy and Astrophysics – Astronomy

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

3

Mantle Viscosity, Neighbourhood Algorithm, Postglacial Rebound, Simulated Annealing

Scientific paper

Two direct search methods, simulated annealing and neighbourhood algorithm, are applied to the inversion of the viscosity profile of the mantle using relative sea level time-histories for the Hudson Bay region. In problems characterized by a low-dimensional model space (Nd= 2 in this study), the two inversion methods show comparable performances. When a larger number of dimensions is involved (specifically Nd= 6), we directly show that simulated annealing is less effective than neighbourhood algorithm in overcoming the obstacles that are found in the model space when our specific data set is employed. This study confirms that modifications of the conventional Monte Carlo inversion method, such as simulated annealing and neighbourhood algorithm, are viable tools to determine the viscosity profile of the mantle, which, until recently, has been mainly tackled by means of linearized techniques.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Mantle viscosity inference: a comparison between simulated annealing and neighbourhood algorithm inversion methods does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Mantle viscosity inference: a comparison between simulated annealing and neighbourhood algorithm inversion methods, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Mantle viscosity inference: a comparison between simulated annealing and neighbourhood algorithm inversion methods will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1058335

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.