| --- |
| license: mit |
| task_categories: |
| - feature-extraction |
| tags: |
| - geophysics |
| - electromagnetic |
| - inversion |
| - surrogate-model |
| - prior-falsification |
| size_categories: |
| - 1KB<n<11GB |
| --- |
| |
| # 3D EM Inversion - Pre-generated Prior Datasets |
|
|
| ## Overview |
| This dataset contains the pre-generated prior data utilized for **3D Electromagnetic (EM) Prior Falsification and Stochastic Inversion with MCMC**. Due to the substantial file size (~1.29 GB), these large NumPy binary files (`.npy`) are hosted here on Hugging Face to supplement the main GitHub repository. |
|
|
| These files serve as the input/output training data or pre-computed structures for building a surrogate model to accelerate 3D EM geophysical inversions. |
|
|
| ## File Structure & Descriptions |
|
|
| Inside this dataset, you will find the following core files: |
|
|
| * **`EMsigma_padded.npy` (962.3 MB):** The padded 3D electrical conductivity ($\sigma$) models used for forward modeling or training the surrogate network. |
| * **`EMsigma_core.npy` (324 MB):** The core region of the 3D conductivity models, excluding padding boundaries, optimized for inversion/falsification analysis. |
| * **`dpred.npy` (1.82 MB):** Predicted data responses corresponding to the prior models. |
| * **`Hyper_Param.npy` (64.1 KB):** Hyperparameters and configuration settings used during the prior generation process. |
| |
| --- |
| |
| ## Usage & Integration with GitHub |
| |
| To use these files in your local environment alongside the source code, please clone the GitHub repository and place these files in the designated directory. |
| |
| ### Recommended Directory Structure: |
| ```text |
| EM_Surrogate_Inv3D/ |
| └── Prior falsification/ |
| ├── 3D EM prior falsification.ipynb |
| └── Generated prior/ <-- Create this folder if it doesn't exist |
| ├── EMsigma_padded.npy |
| ├── EMsigma_core.npy |
| ├── dpred.npy |
| └── Hyper_Param.npy |
