--- license: "other" pretty_name: "Lithology Training Dataset" task_categories: - other tags: - geology - geoscience - well-logs - petrophysics - lithology - LAS - wireline-logs - supervised-learning - subsurface - formation-evaluation size_categories: - "1K Given a sequence of well-log measurements across depth, predict the lithology present at each depth interval and/or reconstruct the corresponding lithological sequence. 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 This dataset is intended for **training and model development**. 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 | --- ## Lithology Classes The dataset contains: * `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` Depth-indexed wireline-log measurements. Typical structure: ```text well_id depth_ft GR RHOB NPHI 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