---
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