{Wavelet}-based reconstruction for {1D} fold inference: from least-squares to {Bayesian} uncertainty.

@inproceedings{Mengelle2026RM,
 abstract = {This study investigates the reconstruction of one-dimensional geological signals from elevation data using a wavelet-based parametric representation. The signal is expressed as a sum of Ricker wavelets whose parameters are estimated through a nonlinear inverse problem. Two inference strategies are considered: deterministic least-squares optimization and Bayesian inference using Markov Chain Monte Carlo sampling. First results on synthetic datasets show the ability of wavelet representation to effectively captures multi-scale signal structures while revealing the non-uniqueness of the decomposition. The Bayesian framework additionally provides a natural way to quantify uncertainty in the reconstructed profiles.},
 author = {Mengelle, Axel and Caumon, Guillaume and Grose, Lachlan},
 booktitle = {2026 {RING} meeting},
 language = {en},
 pages = {141--157},
 publisher = {ASGA},
 title = {{Wavelet}-based reconstruction for {1D} fold inference: from least-squares to {Bayesian} uncertainty.},
 year = {2026}
}