Anisotropic induced polarization modeling with neural networks and effective medium theory
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916518965542912 |
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| author | Bérubé, Charles L. Gagnon, Jean-Luc |
| author_facet | Bérubé, Charles L. Gagnon, Jean-Luc |
| contents | Accurately interpreting induced polarization (IP) data that reflects the inherent anisotropy of the Earth's crust requires anisotropic IP models. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) model effectively simulates the IP signatures of rocks containing polarizable minerals. A pivotal element of the GEMTIP model is calculating the depolarization tensor elements, an intensive task for anisotropic rocks because one must numerically solve six parametric integrals for each mineral inclusion. This study aims to streamline anisotropic IP simulations by extending the GEMTIP framework and introducing a machine learning approach to estimate the depolarization tensors. The theoretical contributions of this research are two-fold: (1) we augment the GEMTIP model to encompass anisotropic background conductivity and triaxial ellipsoidal inclusions, and (2) we reformulate the depolarization integrals to normalize their input and output variables, facilitating their estimation by neural networks. Validation against analytical solutions for spherical and spheroidal inclusions corroborates the accuracy of the neural network. Analyzing the neural network model, we find that the relationship between chargeability and polarizable inclusion content is increasingly uncertain for increasingly anisotropic rocks. A similar observation applies to the relationship between critical frequency and host rock conductivity. Moreover, the depolarization tensors are, on average, 56 % sensitive to inclusion anisotropy and 44 % sensitive to host rock conductivity anisotropy. Remarkably, our neural network drastically accelerates GEMTIP simulations--up to 100,000 times faster than numerical integration--without substantially sacrificing accuracy. This advancement is promising for efficient rock-scale IP modeling in complex and anisotropic geological settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anisotropic induced polarization modeling with neural networks and effective medium theory Bérubé, Charles L. Gagnon, Jean-Luc Geophysics Accurately interpreting induced polarization (IP) data that reflects the inherent anisotropy of the Earth's crust requires anisotropic IP models. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) model effectively simulates the IP signatures of rocks containing polarizable minerals. A pivotal element of the GEMTIP model is calculating the depolarization tensor elements, an intensive task for anisotropic rocks because one must numerically solve six parametric integrals for each mineral inclusion. This study aims to streamline anisotropic IP simulations by extending the GEMTIP framework and introducing a machine learning approach to estimate the depolarization tensors. The theoretical contributions of this research are two-fold: (1) we augment the GEMTIP model to encompass anisotropic background conductivity and triaxial ellipsoidal inclusions, and (2) we reformulate the depolarization integrals to normalize their input and output variables, facilitating their estimation by neural networks. Validation against analytical solutions for spherical and spheroidal inclusions corroborates the accuracy of the neural network. Analyzing the neural network model, we find that the relationship between chargeability and polarizable inclusion content is increasingly uncertain for increasingly anisotropic rocks. A similar observation applies to the relationship between critical frequency and host rock conductivity. Moreover, the depolarization tensors are, on average, 56 % sensitive to inclusion anisotropy and 44 % sensitive to host rock conductivity anisotropy. Remarkably, our neural network drastically accelerates GEMTIP simulations--up to 100,000 times faster than numerical integration--without substantially sacrificing accuracy. This advancement is promising for efficient rock-scale IP modeling in complex and anisotropic geological settings. |
| title | Anisotropic induced polarization modeling with neural networks and effective medium theory |
| topic | Geophysics |
| url | https://arxiv.org/abs/2402.11313 |