Update README.md

README.md

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 ---
 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