Archeomagnetism in the use of brick dating lanos
The errors that occur at different stages of the archaeomagnetic calibration process are combined using a Bayesian hierarchical modelling.The archaeomagnetic data obtained from archaeological structures such as hearths, kilns or sets of bricks and tiles, exhibit considerable experimental errors and are generally more or less well dated by archaeological context, history or chronometric methods (14C, TL, dendrochronology, etc.).On Wednesday July 5 between - GMT we’ll be busy making things better.You’ll still be able to search, browse and read our articles, but you won’t be able to register, edit your account, purchase content, or activate tokens or eprints during that period.The integration of dating methods in building archaeology has resulted in an advance in the qualitative and quantitative information available for the study of the history of architecture and building techniques.To examine the question of the origin of post-Roman ceramic building materials, archaeological studies of early medieval buildings in France and England have been combined with scientific dating methods and applied to their component tiles and bricks.The developments of selected archaeomagnetic studies in other European countries are quoted and referenced.
In order to illustrate the model and the inference method used, we will present results based on French, Bulgarian and Austrian datasets recently published.
However, there is an interest to extend the record of the geomagnetic field into the past and to combine the results with theoretical reversal models.
Nowadays, the detailed mechanism of the magnetic field is still not yet completely clear, in particular the reversal process when the strength of the geomagnetic field is considerably reduced.
The model also includes penalized cubic splines for estimating the univariate, spherical or three-dimensional curves for the secular variation of the geomagnetic field (inclination, declination, intensity) over time at a local place.
The mean smooth curve we obtain, with its posterior Bayesian envelop provides an adaptation to the effects of variability in the density of reference points over time.