Update README.md

README.md

@@ -15,11 +15,11 @@ tags:
   - subsurface
   - formation-evaluation
 size_categories:
-  - 1K<n<10K
+  - "1K<n<10K"
 ---
 
 [![GitHub](https://img.shields.io/badge/GitHub-Nora--Research--Lab-181717?logo=github)](https://github.com/Nora-Research-Lab)
-[![Hugging Face](https://img.shields.io/badge/HuggingFace-NoraResearchLab-FFD21E?logo=huggingface&logoColor=yellow)](https://huggingface.co/NoraResearchLab)
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-NoraResearchLab-FFD21E?logo=huggingface\&logoColor=yellow)](https://huggingface.co/NoraResearchLab)
 [![LinkedIn](https://img.shields.io/badge/LinkedIn-NORA%20Research%20Lab-0A66C2?logo=linkedin)](https://www.linkedin.com/company/nora-research-lab)
 [![X](https://img.shields.io/badge/X-@noraresearchlab-000000?logo=x)](https://x.com/noraresearchlab)
 [![Website](https://img.shields.io/badge/Website-noraresearchlab.site-2ea44f)](https://noraresearchlab.site)
@@ -28,9 +28,9 @@ size_categories:
 
 A supervised training dataset for machine learning and AI systems that learn to identify **lithology from well-log data**.
 
-This dataset contains **exactly 400 wells** with standardized wireline-log measurements and corresponding lithological labels.
+The dataset contains **400 wells** with standardized wireline-log measurements and corresponding lithological labels.
 
-**Training dataset:**  
+**Training dataset:**
 https://huggingface.co/datasets/NoraResearchLab/Lithology-Training-Dataset
 
 ---
@@ -43,14 +43,14 @@ The core task is:
 
 The dataset is designed for:
 
-- Lithology classification
-- Well-log interpretation
-- Sequence modelling
-- Lithological interval segmentation
-- Petrophysical machine learning
-- Geoscience AI
-- Subsurface representation learning
-- Benchmark and model development
+* Lithology classification
+* Well-log interpretation
+* Sequence modelling
+* Lithological interval segmentation
+* Petrophysical machine learning
+* Geoscience AI
+* Subsurface representation learning
+* Benchmark and model development
 
 This dataset is intended for **training and model development**.
 
@@ -60,14 +60,14 @@ It is separate from the independent evaluation benchmark.
 
 ## Dataset Size
 
-| Property | Value |
-|---|---:|
-| Total wells | **400** |
-| Lithological classes | **8** |
-| Primary data type | Wireline well logs |
-| Label type | Rule-derived supervised labels |
-| Sampling | Standardized depth grid |
-| Primary task | Lithology identification |
+| Property             |                          Value |
+| -------------------- | -----------------------------: |
+| Total wells          |                        **400** |
+| Lithological classes |                          **8** |
+| Primary data type    |             Wireline well logs |
+| Label type           | Rule-derived supervised labels |
+| Sampling             |        Standardized depth grid |
+| Primary task         |       Lithology identification |
 
 ---
 
@@ -75,19 +75,47 @@ It is separate from the independent evaluation benchmark.
 
 The dataset contains:
 
-- `SHALE`
-- `SANDSTONE`
-- `LIMESTONE`
-- `DOLOMITE`
-- `ANHYDRITE`
-- `SALT`
-- `COAL`
-- `UNKNOWN/MIXED`
+* `SHALE`
+* `SANDSTONE`
+* `LIMESTONE`
+* `DOLOMITE`
+* `ANHYDRITE`
+* `SALT`
+* `COAL`
+* `UNKNOWN/MIXED`
 
 `UNKNOWN/MIXED` represents intervals where the available log evidence does not provide sufficient confidence for assigning one of the principal lithological classes.
 
 ---
 
+# Dataset Structure
+
+The repository contains multiple Parquet tables representing different levels of the dataset.
+
+These tables **do not share an identical schema** and should therefore be loaded independently.
+
+```text
+data/
+├── logs/
+│   ├── train.parquet
+│   ├── validation.parquet
+│   └── test.parquet
+│
+├── depthwise/
+│   ├── train.parquet
+│   ├── validation.parquet
+│   └── test.parquet
+│
+└── intervals/
+    ├── train.parquet
+    ├── validation.parquet
+    └── test.parquet
+```
+
+The exact files available in the repository should be treated as the authoritative source for the current release.
+
+---
+
 ## Dataset Configurations
 
 ### `logs`
@@ -106,4 +134,428 @@ PEF
 DT
 RT
 CALI
-...
+...
+```
+
+This table is intended to provide the primary input features for machine-learning models.
+
+---
+
+### `depthwise`
+
+Depthwise supervised labels and derived information associated with individual log samples.
+
+Typical structure may include:
+
+```text
+well_id
+depth_ft
+lithology_pointwise
+score
+...
+```
+
+This configuration is useful for:
+
+* Pointwise lithology classification
+* Sequence modelling
+* Depthwise prediction
+* Label analysis
+* Training supervised models
+
+---
+
+### `intervals`
+
+Lithological intervals represented as continuous depth ranges.
+
+Typical structure:
+
+```text
+well_id
+interval_id
+top_depth_ft
+base_depth_ft
+thickness_ft
+lithology
+...
+```
+
+This configuration is useful for:
+
+* Lithological sequence reconstruction
+* Interval segmentation
+* Continuous geological interpretation
+* Comparing predicted and reference intervals
+
+---
+
+# Recommended Loading Method
+
+Because the repository contains **multiple heterogeneous Parquet tables**, the recommended approach is to load each Parquet file directly.
+
+### Python
+
+```python
+import pandas as pd
+
+BASE_URL = (
+    "https://huggingface.co/datasets/"
+    "NoraResearchLab/Lithology-Training-Dataset/"
+    "resolve/main/data"
+)
+
+# Logs
+logs_train = pd.read_parquet(
+    f"{BASE_URL}/logs/train.parquet"
+)
+
+logs_validation = pd.read_parquet(
+    f"{BASE_URL}/logs/validation.parquet"
+)
+
+logs_test = pd.read_parquet(
+    f"{BASE_URL}/logs/test.parquet"
+)
+
+# Depthwise labels
+depthwise_train = pd.read_parquet(
+    f"{BASE_URL}/depthwise/train.parquet"
+)
+
+depthwise_validation = pd.read_parquet(
+    f"{BASE_URL}/depthwise/validation.parquet"
+)
+
+depthwise_test = pd.read_parquet(
+    f"{BASE_URL}/depthwise/test.parquet"
+)
+
+# Lithological intervals
+intervals_train = pd.read_parquet(
+    f"{BASE_URL}/intervals/train.parquet"
+)
+
+intervals_validation = pd.read_parquet(
+    f"{BASE_URL}/intervals/validation.parquet"
+)
+
+intervals_test = pd.read_parquet(
+    f"{BASE_URL}/intervals/test.parquet"
+)
+```
+
+Install the required packages with:
+
+```bash
+pip install pandas pyarrow
+```
+
+This method reads the Parquet files directly from the Hugging Face repository without requiring the repository to be converted into a single Hugging Face `Dataset` object.
+
+---
+
+# Why `load_dataset()` Is Not Recommended
+
+The repository contains different table types with different schemas.
+
+For example, `logs` and `depthwise` are primarily **depthwise/pointwise data**, while `intervals` contains **range-based interval records**.
+
+Their structures are therefore different.
+
+Attempting:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "NoraResearchLab/Lithology-Training-Dataset"
+)
+```
+
+may cause Hugging Face Datasets to attempt to construct a unified schema across the repository's Parquet files.
+
+This can result in errors such as:
+
+```text
+CastError:
+Couldn't cast ...
+because column names don't match
+```
+
+This is not an indication that the underlying Parquet files are invalid. The issue is that the repository contains multiple heterogeneous tabular structures.
+
+**Load each table independently instead.**
+
+---
+
+# Loading Only One Configuration
+
+If you only need the well-log features for model training, you do not need to download or load the other tables.
+
+For example:
+
+```python
+import pandas as pd
+
+url = (
+    "https://huggingface.co/datasets/"
+    "NoraResearchLab/Lithology-Training-Dataset/"
+    "resolve/main/data/logs/train.parquet"
+)
+
+logs_train = pd.read_parquet(url)
+
+print(logs_train.shape)
+print(logs_train.head())
+```
+
+For the depthwise labels:
+
+```python
+import pandas as pd
+
+url = (
+    "https://huggingface.co/datasets/"
+    "NoraResearchLab/Lithology-Training-Dataset/"
+    "resolve/main/data/depthwise/train.parquet"
+)
+
+depthwise_train = pd.read_parquet(url)
+
+print(depthwise_train.shape)
+print(depthwise_train.head())
+```
+
+For lithological intervals:
+
+```python
+import pandas as pd
+
+url = (
+    "https://huggingface.co/datasets/"
+    "NoraResearchLab/Lithology-Training-Dataset/"
+    "resolve/main/data/intervals/train.parquet"
+)
+
+intervals_train = pd.read_parquet(url)
+
+print(intervals_train.shape)
+print(intervals_train.head())
+```
+
+---
+
+# Typical Training Workflow
+
+A typical lithology-classification workflow can use the `logs` and `depthwise` tables together.
+
+```python
+import pandas as pd
+
+BASE_URL = (
+    "https://huggingface.co/datasets/"
+    "NoraResearchLab/Lithology-Training-Dataset/"
+    "resolve/main/data"
+)
+
+logs = pd.read_parquet(
+    f"{BASE_URL}/logs/train.parquet"
+)
+
+labels = pd.read_parquet(
+    f"{BASE_URL}/depthwise/train.parquet"
+)
+```
+
+The two tables can then be related using their common identifiers, typically:
+
+```text
+well_id
+depth_ft
+```
+
+For example:
+
+```python
+training_data = logs.merge(
+    labels,
+    on=["well_id", "depth_ft"],
+    how="inner"
+)
+```
+
+Before training, users should inspect the resulting schema and verify that the expected feature and target columns are present:
+
+```python
+print(training_data.shape)
+print(training_data.columns.tolist())
+print(training_data.head())
+```
+
+The exact columns should always be determined from the released Parquet files rather than assumed from this README.
+
+---
+
+# Working With Individual Wells
+
+The dataset is organized around complete wells. A model can therefore be trained using individual depth sequences rather than treating every row as an independent observation.
+
+Example:
+
+```python
+well_id = logs["well_id"].iloc[0]
+
+well_logs = logs[
+    logs["well_id"] == well_id
+].sort_values("depth_ft")
+
+print(well_logs)
+```
+
+This is particularly important for sequence-based approaches such as:
+
+* LSTM/GRU models
+* Temporal convolutional networks
+* Transformers
+* Sequence-to-sequence models
+* Depthwise segmentation models
+
+---
+
+# Train / Validation / Test Splits
+
+The dataset provides separate splits where available:
+
+```text
+train
+validation
+test
+```
+
+Users should preserve these splits when developing models.
+
+Do not randomly mix rows from the same well across training and test sets unless the experimental design explicitly requires this.
+
+For geological sequence modelling, **well-level separation** is important because neighboring depth samples from the same well are highly correlated.
+
+---
+
+# Relationship to the Lithology Sequence Identification Benchmark
+
+This training dataset is intended for **model development and training**.
+
+It is separate from the:
+
+**Lithology Sequence Identification Benchmark**
+
+The benchmark is designed to evaluate whether a model can reconstruct the lithological sequence of a complete well from raw wireline measurements.
+
+The intended workflow is:
+
+```text
+Lithology Training Dataset
+            │
+            ▼
+      Model Training
+            │
+            ▼
+     Model Development
+            │
+            ▼
+  Lithology Sequence Identification
+        Benchmark
+            │
+            ▼
+       Independent
+        Evaluation
+```
+
+Models should not use benchmark evaluation wells as training data.
+
+---
+
+# Data Loading Notes
+
+### Remote Parquet
+
+`pandas.read_parquet()` can read the Parquet files directly from their Hugging Face HTTPS URLs when the appropriate Parquet engine is installed.
+
+Recommended dependencies:
+
+```bash
+pip install pandas pyarrow
+```
+
+### Hugging Face Resolve URLs
+
+The general URL pattern is:
+
+```text
+https://huggingface.co/datasets/{USER}/{REPOSITORY}/resolve/{BRANCH}/{PATH}
+```
+
+For this dataset:
+
+```text
+https://huggingface.co/datasets/NoraResearchLab/Lithology-Training-Dataset/resolve/main/data/
+```
+
+Individual files can then be addressed directly:
+
+```text
+data/logs/train.parquet
+data/depthwise/train.parquet
+data/intervals/train.parquet
+```
+
+---
+
+# Important Usage Recommendation
+
+**Do not assume that every Parquet file in the repository has the same schema.**
+
+Treat each configuration as a separate table:
+
+```text
+logs       → depthwise wireline measurements
+depthwise  → pointwise labels / derived depthwise information
+intervals  → continuous lithological intervals
+```
+
+Load only the tables required for the task you are performing.
+
+---
+
+## License and Usage
+
+This dataset is provided under the license specified in the repository metadata.
+
+Users are responsible for reviewing the applicable dataset provenance, source-data licenses, and restrictions before redistribution or commercial use.
+
+The dataset is intended for research, machine-learning development, and geoscience AI applications.
+
+---
+
+## Citation
+
+If you use this dataset in research, benchmarking, or a software project, please cite the NORA Research Lab dataset repository:
+
+**NORA Research Lab — Lithology Training Dataset**
+
+Hugging Face:
+https://huggingface.co/datasets/NoraResearchLab/Lithology-Training-Dataset
+
+---
+
+## Organization
+
+**NORA Research Lab**
+
+Building intelligence for the real world.
+
+* GitHub: https://github.com/Nora-Research-Lab
+* Hugging Face: https://huggingface.co/NoraResearchLab
+* LinkedIn: https://www.linkedin.com/company/nora-research-lab
+* X: https://x.com/noraresearchlab
+* Website: https://noraresearchlab.site