Welcome to wabi’s documentation!

Installation

WABI is available through PyPI, and may be installed using pip:

$ pip install wabi

Contents

• wabi
  • wabi package
• Notebooks
  • Linear Least-Squares Inversion with Wavelet-Based-regularization
  • Linear Least-Squares Inversion with Tikhonov Regularization
  • Download the examples as notebooks

wavelet-based-inversion

Scale-dependent wavelet-based regularization scheme for geophysical 1D inversion.

This flexible inversion scheme allows to easily obtain blocky, smooth and intermediate inversion models. Different inversion models are obtained by simply changing the wavelet basis. - db1: blocky inversion models - db2-db4: sharper inversion models - db5+: smoother inversion models

Daubechies (db) wavelets are ideal (see Deleersnyder et al, 2021), however, other wavelets can also be used. Simply run pywt.wavelist() to list the available options. The shape of the wavelet basis function (e.g., look here) is an indication of the type of minimum-structure the regularization method will promote.

• Fits within the modular SimPEG framework (see SimPEG website) (see examples)

• Fits within your own inversion codes (see examples with empymod)

How to cite

Deleersnyder, W., Maveau, B., Hermans, T., & Dudal, D. (2021). Inversion of electromagnetic induction data using a novel wavelet-based and scale-dependent regularization term. Geophysical Journal International, 226(3), 1715-1729. DOI: https://doi.org/10.1093/gji/ggab182

Open Access version on ResearchGate

The code

Deleersnyder, W., Thibaut, R., 2022. WBI - Scale-dependent 1D wavelet-based inversion in Python

Questions?

Contact us on GitHub!

• Wouter Deleersnyder

• Robin Thibaut