Name
Abstract | ||
|---|---|---|
The conventional, L2-norm-based, regularization term in electromagnetic (EM) inversions implements smooth constraints on model complexity in the space domain, which can smoothen the boundaries of complex underground structures. To improve the resolution of 3-D frequency-domain airborne EM (AEM) inversions, we propose a new algorithm for sparse-regularized inversion based on the shearlet transform. Unlike traditional methods that invert the model parameters in the space domain, we first transform the 3-D resistivity model into the frequency domain and then invert the sparse coefficients using an L1-norm measure to ensure the sparseness of the solution. Finally, we transform the shearlet coefficients back to the space domain to update the model. The shearlet transform has inherent multiscale and multidirectional properties, making it capable of effectively extracting complex geometries such as curved boundaries. We adopt the finite-difference method and the iteratively reweighted least-squares scheme for our 3-D AEM modeling and inversions and apply the x201C;moving footprintx201D; technique to speed up the inversion. Tests using synthetic data show that sparse-regularized inversion based on the shearlet transform can obtain more-focused inversion results than conventional smoothness-constrained inversions based on the L2-norm. Tests using field survey data also reveal that the new method can achieve more realistic underground structures. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3061160 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Wavelet transforms, Frequency-domain analysis, Data models, Solid modeling, Geology, Computational modeling, Atmospheric modeling, 3-D inversion, airborne electromagnetic (AEM), frequency domain, shearlet transform, sparse regularized | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Su | 1 | 0 | 0.34 |
| Changchun Yin | 2 | 0 | 0.34 |
| Yunhe Liu | 3 | 0 | 0.34 |
| Xiuyan Ren | 4 | 0 | 0.34 |
| Bo Zhang | 5 | 4 | 19.80 |
| Changkai Qiu | 6 | 0 | 0.34 |
| Bin Xiong | 7 | 0 | 0.68 |
| Vikas Chand Baranwal | 8 | 0 | 0.34 |