Update README.md

README.md

@@ -1,39 +1,41 @@
 ---
 license: other
-pretty_name: Lithology Sequence Identification Benchmark
+pretty_name: "Lithology Sequence Identification Benchmark"
 task_categories:
-- other
+  - other
 tags:
-- geology
-- geoscience
-- well-logs
-- petrophysics
-- lithology
-- LAS
-- benchmark
+  - geology
+  - geoscience
+  - well-logs
+  - petrophysics
+  - lithology
+  - LAS
+  - benchmark
 configs:
-- config_name: logs
-  data_files:
-  - split: train
-    path: data/logs/train.parquet
-  - split: validation
-    path: data/logs/validation.parquet
-  - split: test
-    path: data/logs/test.parquet
-- config_name: wells
-  data_files:
-  - split: train
-    path: data/wells.parquet
+  - config_name: logs
+    data_files:
+      - split: train
+        path: data/logs/train.parquet
+      - split: validation
+        path: data/logs/validation.parquet
+      - split: test
+        path: data/logs/test.parquet
+  - config_name: wells
+    data_files:
+      - split: train
+        path: data/wells.parquet
 ---
 
 <div align="center">
 
 # Lithology Sequence Identification Benchmark
 
-**Evaluation-only benchmark: predict the lithological sequence of a whole well from raw wireline logs**
+**Evaluation-only benchmark for reconstructing the lithological sequence of a complete well from raw wireline logs**
+
+A fixed-well benchmark for testing whether machine-learning and AI systems can infer continuous lithological intervals from conventional petrophysical measurements.
 
 [![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)
@@ -42,107 +44,1117 @@ configs:
 
 ---
 
-## Task
+## Overview
+
+The **Lithology Sequence Identification Benchmark** evaluates models on a practical subsurface interpretation problem:
+
+> **Given the complete wireline-log suite from a well, can a model reconstruct the ordered sequence of lithological intervals across the logged depth?**
+
+Unlike conventional point-wise classification benchmarks, the task is not simply to assign a lithology label to every individual depth sample.
 
-Given **one complete well log** (curves sampled every 0.5 ft), output the **ordered list of continuous
-lithological intervals** across depth:
+The expected prediction is a **geologically meaningful sequence of contiguous depth intervals**, for example:
 
 ```json
-[{"top_depth_ft": 2150.0, "base_depth_ft": 2191.5, "lithology": "SHALE"},
- {"top_depth_ft": 2191.5, "base_depth_ft": 2230.0, "lithology": "LIMESTONE"},
- {"top_depth_ft": 2230.0, "base_depth_ft": 2246.5, "lithology": "UNKNOWN/MIXED"}]
-```
-
-Intervals must be contiguous and cover the logged interval. Classes: `SHALE`, `SANDSTONE`, `LIMESTONE`, `DOLOMITE`, `ANHYDRITE`, `SALT`, `COAL`, `UNKNOWN/MIXED`.
-`UNKNOWN/MIXED` marks depths where the available logs do not support a reliable single-lithology call.
-
-> **Labels are withheld.** The public release contains only the QC'd logs and well metadata. Reference intervals are retained privately by the maintainers for offline scoring of submitted predictions.
-
-| | |
-|---|---|
-| Wells | 245 (train 166 / validation 25 / test 54 — split by well) |
-| Depth samples | 1,963,283 |
-| Reference intervals | 49,830 |
-| Label-quality tiers | A: 167, B: 78 |
-| Config fingerprint | `7b440d013c0a08a1` |
-| Reference fingerprint | `92e77d413b4c86d4` |
-
-## Files / configs
-
-| Config | Content | Role |
-|---|---|---|
-| `logs` | `well_id, depth_ft, GR, RHOB, NPHI, PEF, DT, RT, CALI` (QC'd, `NaN` = curve absent) | **model input** |
-| `logs` | `well_id, depth_ft, GR, RHOB, NPHI, PEF, DT, RT, CALI` (QC'd, `NaN` = curve absent) | **model input** |
-| `wells` | one row per well: split, tier, curves present, baselines, interval count | metadata |
-| `intervals` / `depthwise` | **withheld** – private reference held by maintainers | evaluation only |
-
-`label_config.json` holds every threshold used; `curve_specs.json` the mnemonic rules; `manifest.json` versions and hashes.
-
-## How the reference labels are produced (deterministic, no randomness, no machine learning)
-
-1. **Curve detection** — each LAS curve is matched to a standard curve by mnemonic alias → regex → description keywords → unit, so
-   `RHOZ`, `DEN`, `RHOB` all become `RHOB`, `ILD`/`LLD`/`AT90`/conductivity become `RT`, etc. Ties are broken by data coverage then name.
-2. **Unit standardisation** — metres→feet, kg/m³���g/cc, %→fraction, µs/m→µs/ft, mm→in, conductivity→resistivity.
-3. **Quality control** — sentinel/null masking, physical-range masking, flat-line (dead tool) masking, Hampel spike removal, resampling to a
-   fixed grid (bin-mean for fine logs, gap-limited interpolation for coarse), low-coverage curve rejection, caliper-based washout flag that
-   down-weights density/neutron/PEF/sonic evidence.
-4. **Multi-log fuzzy scoring** — per depth, each lithology is scored from every *available* feature (gamma index normalised per well,
-   RHOB, limestone-scale NPHI, PEF, DT, log resistivity, neutron–density separation) through trapezoidal membership functions and
-   configurable weights. Missing curves reduce evidence; they never crash the method. Contradicted lithologies are penalised.
-5. **UNKNOWN/MIXED gate** — assigned when available-evidence weight, best score, or the margin over the runner-up falls below configured limits.
-6. **Segmentation** — depth-wise calls are median/mean smoothed, then runs thinner than the minimum bed thickness are iteratively absorbed
-   into the better-fitting neighbour and equal neighbours merged, yielding geological intervals instead of log noise.
-
-Lithology plateau ranges used (full trapezoids in `label_config.json`):
-
-| Lithology | GRI | RHOB | NPHI | PEF | DT | LOGRT | ND_SEP |
-|---|---|---|---|---|---|---|---|
-| SHALE | 0.65 – 2.5 | 2.2 – 2.65 | 0.25 – 0.45 | 2.5 – 3.8 | 85 – 140 | -0.2 – 0.7 | 0.05 – 0.22 |
-| SANDSTONE | -1 – 0.25 | 2.15 – 2.65 | -0.04 – 0.18 | 1.6 – 2.3 | 52 – 90 | 0 – 2.2 | -0.12 – -0.02 |
-| LIMESTONE | -1 – 0.25 | 2.35 – 2.72 | -0.01 – 0.18 | 4.7 – 5.5 | 46 – 72 | 0.9 – 3 | -0.02 – 0.03 |
-| DOLOMITE | -1 – 0.28 | 2.65 – 2.9 | 0.02 – 0.2 | 2.9 – 3.5 | 43 – 65 | 0.8 – 3 | 0.06 – 0.16 |
-| ANHYDRITE | -1 – 0.12 | 2.92 – 3 | -0.04 – 0.03 | 4.8 – 5.4 | 48 – 53 | 2.6 – 5 | 0.12 – 0.22 |
-| SALT | -1 – 0.12 | 2 – 2.15 | -0.04 – 0.04 | 4.4 – 4.9 | 65 – 70 | 2.6 – 5 | -0.45 – -0.25 |
-| COAL | -1 – 0.55 | 1.2 – 1.7 | 0.45 – 0.85 | 0.2 – 1.5 | 110 – 160 | 1.3 – 3.5 | -0.25 – 0.25 |
-
-
-## Evaluation
+[
+  {
+    "top_depth_ft": 2150.0,
+    "base_depth_ft": 2191.5,
+    "lithology": "SHALE"
+  },
+  {
+    "top_depth_ft": 2191.5,
+    "base_depth_ft": 2230.0,
+    "lithology": "LIMESTONE"
+  },
+  {
+    "top_depth_ft": 2230.0,
+    "base_depth_ft": 2246.5,
+    "lithology": "UNKNOWN/MIXED"
+  }
+]
+```
+
+Predicted intervals must be:
+
+* ordered by depth;
+* contiguous;
+* non-overlapping;
+* and collectively cover the logged interval.
+
+The benchmark therefore evaluates both **lithology identification** and **boundary placement / sequence reconstruction**.
+
+---
+
+# Benchmark Statistics
+
+| Property              |                                Value |
+| --------------------- | -----------------------------------: |
+| Total wells           |                              **245** |
+| Training wells        |                              **166** |
+| Validation wells      |                               **25** |
+| Test wells            |                               **54** |
+| Depth samples         |                        **1,963,283** |
+| Reference intervals   |                           **49,830** |
+| Label-quality Tier A  |                        **167 wells** |
+| Label-quality Tier B  |                         **78 wells** |
+| Sampling interval     |                           **0.5 ft** |
+| Lithology classes     |                                **8** |
+| Evaluation type       | **Well-level / sequence prediction** |
+| Config fingerprint    |                   `7b440d013c0a08a1` |
+| Reference fingerprint |                   `92e77d413b4c86d4` |
+
+The train, validation, and test sets are split **by well**, rather than by individual depth samples.
+
+This is important because adjacent samples from the same well are highly correlated. A random depth-level split could allow information from the same geological environment to appear in both training and test data and would therefore produce an overly optimistic estimate of generalisation.
+
+---
+
+# Task Definition
+
+## Input
+
+Each benchmark example consists of one complete well log sampled at approximately **0.5 ft**.
+
+The standard input curves are:
+
+```text
+GR
+RHOB
+NPHI
+PEF
+DT
+RT
+CALI
+```
+
+Not every well contains every curve.
+
+Missing curves are represented as:
+
+```text
+NaN
+```
+
+A model should therefore be capable of operating with incomplete curve suites.
+
+---
+
+## Output
+
+The model must return an ordered sequence of lithological intervals:
+
+```text
+top_depth_ft
+base_depth_ft
+lithology
+```
+
+The permitted lithology classes are:
+
+```text
+SHALE
+SANDSTONE
+LIMESTONE
+DOLOMITE
+ANHYDRITE
+SALT
+COAL
+UNKNOWN/MIXED
+```
+
+`UNKNOWN/MIXED` is an explicit class rather than an error state.
+
+It represents depths where the available wireline evidence does not provide sufficient support for a reliable single-lithology interpretation.
+
+---
+
+# Why Sequence Identification?
+
+Traditional machine-learning approaches to lithology prediction often formulate the problem as:
+
+```text
+wireline measurements at depth
+        ↓
+lithology class
+```
+
+This produces a label for every depth sample.
+
+The present benchmark instead treats the well as a **sequence reconstruction problem**:
+
+```text
+Complete well logs
+        ↓
+depth-wise geological evidence
+        ↓
+lithology transitions
+        ↓
+continuous geological intervals
+        ↓
+ordered lithological sequence
+```
+
+This distinction matters because a useful subsurface interpretation must identify not only **what lithology occurs**, but also:
+
+* where an interval begins;
+* where it ends;
+* how thick it is;
+* what lithology occurs above and below it;
+* and whether the evidence is sufficiently strong to make a confident call.
+
+A model that produces correct labels but places boundaries several feet away from the reference intervals can therefore receive different results from a model that accurately reconstructs both lithology and boundaries.
+
+---
+
+# Lithology Classes
+
+The benchmark contains eight possible output classes.
+
+| Class           | Description                                                                     |
+| --------------- | ------------------------------------------------------------------------------- |
+| `SHALE`         | Fine-grained, generally clay-rich sedimentary interval                          |
+| `SANDSTONE`     | Predominantly clastic sand-sized sedimentary interval                           |
+| `LIMESTONE`     | Predominantly carbonate interval dominated by calcite                           |
+| `DOLOMITE`      | Carbonate interval with dolomitic characteristics                               |
+| `ANHYDRITE`     | Evaporite interval dominated by anhydrite                                       |
+| `SALT`          | Evaporite interval dominated by halite                                          |
+| `COAL`          | Coal-bearing interval                                                           |
+| `UNKNOWN/MIXED` | Insufficient or conflicting evidence for a reliable single-class interpretation |
+
+The classes are intended for benchmark evaluation and should not be interpreted as exhaustive geological descriptions of every formation or facies present in the source wells.
+
+---
+
+# Dataset Configurations
+
+The dataset is organised into two principal configurations.
+
+| Config  | Contents                                  | Purpose             |
+| ------- | ----------------------------------------- | ------------------- |
+| `logs`  | QC'd depth-indexed wireline measurements  | Model input         |
+| `wells` | Well-level metadata and split information | Metadata / analysis |
+
+The reference lithology intervals are **not publicly released**.
+
+They are retained privately by the benchmark maintainers for evaluation.
+
+---
+
+# Files
+
+### `logs`
+
+Contains the processed wireline-log observations.
+
+Expected fields include:
+
+```text
+well_id
+depth_ft
+GR
+RHOB
+NPHI
+PEF
+DT
+RT
+CALI
+```
+
+Missing measurements are represented using `NaN`.
+
+The data are quality-controlled and standardised so that models can operate on a consistent representation despite differences between the original LAS files.
+
+---
+
+### `wells`
+
+Contains one record per well.
+
+The metadata include information such as:
+
+```text
+well_id
+split
+label_quality_tier
+curves_present
+baseline_information
+reference_interval_count
+```
+
+This configuration is intended for dataset inspection, stratified analysis, and benchmark bookkeeping.
+
+---
+
+### Private reference data
+
+The maintainers retain the reference lithology representation used for scoring.
+
+This includes the equivalent of:
+
+```text
+intervals
+depthwise labels
+```
+
+These files are intentionally withheld from the public release.
+
+The purpose is to prevent direct optimisation against the evaluation labels.
+
+---
+
+# Train / Validation / Test Split
+
+The benchmark uses a **well-level split**:
+
+```text
+Train:       166 wells
+Validation:   25 wells
+Test:         54 wells
+```
+
+A well and all of its depth samples belong to exactly one split.
+
+This prevents the same well from contributing correlated observations to both training and evaluation.
+
+The public benchmark therefore tests whether a model can generalise to **previously unseen wells**, rather than merely interpolate between nearby depth samples from wells it has already observed.
+
+---
+
+# Reference Label Construction
+
+The reference labels are generated through a deterministic petrophysical interpretation pipeline.
+
+They are not produced using machine learning.
+
+The pipeline is designed to convert heterogeneous raw LAS data into a consistent, reproducible lithological reference representation.
+
+The process consists of:
+
+```text
+Raw LAS files
+      ↓
+Curve identification
+      ↓
+Unit standardisation
+      ↓
+Quality control
+      ↓
+Depth-grid standardisation
+      ↓
+Multi-log lithology scoring
+      ↓
+UNKNOWN/MIXED confidence gate
+      ↓
+Sequence smoothing
+      ↓
+Minimum-bed-thickness processing
+      ↓
+Final lithological intervals
+```
+
+No randomness is used in the reference-label generation process.
+
+---
+
+# Step 1 — Curve Detection
+
+Different wells may use different mnemonics for equivalent measurements.
+
+The benchmark therefore maps raw LAS curves to standard benchmark channels using a deterministic hierarchy:
+
+```text
+mnemonic alias
+      ↓
+regular-expression matching
+      ↓
+description keywords
+      ↓
+unit information
+```
+
+For example, density curves can appear under names such as:
+
+```text
+RHOB
+RHOZ
+DEN
+```
+
+and are mapped to:
+
+```text
+RHOB
+```
+
+Likewise, resistivity-related curves may appear under names such as:
+
+```text
+ILD
+LLD
+AT90
+```
+
+or conductivity representations and are standardised into the benchmark's `RT` representation where appropriate.
+
+When multiple candidate curves satisfy the matching rules, ties are resolved using data coverage and then curve naming.
+
+The complete mapping logic is defined by:
+
+```text
+curve_specs.json
+```
+
+---
+
+# Step 2 — Unit Standardisation
+
+The source LAS files contain measurements using different units.
+
+The preprocessing system converts measurements to consistent benchmark units.
+
+Examples include:
+
+| Original representation | Standard representation |
+| ----------------------- | ----------------------- |
+| metres                  | feet                    |
+| kg/m³                   | g/cc                    |
+| percentage              | fraction                |
+| µs/m                    | µs/ft                   |
+| millimetres             | inches                  |
+| conductivity            | resistivity             |
+
+The objective is to ensure that the downstream interpretation rules operate in a consistent numerical space.
+
+---
+
+# Step 3 — Quality Control
+
+Several quality-control operations are applied before lithology scoring.
+
+These include:
+
+### Null and sentinel masking
+
+Known null values and sentinel values are converted to missing observations.
+
+### Physical-range filtering
+
+Measurements outside physically plausible ranges are masked.
+
+### Flat-line detection
+
+Extended flat-line behaviour is detected as a possible dead-tool or failed-tool condition.
+
+Such measurements are prevented from contributing misleading evidence.
+
+### Hampel spike filtering
+
+Isolated extreme spikes are detected and removed using a Hampel-style robust outlier procedure.
+
+### Depth resampling
+
+Logs are resampled onto a common fixed depth grid.
+
+Fine-resolution curves use bin averaging.
+
+Coarser measurements can use gap-limited interpolation where appropriate.
+
+### Coverage filtering
+
+Curves with insufficient usable coverage are rejected for that well or interval.
+
+### Washout detection
+
+Caliper measurements are used to identify potential borehole washout.
+
+When washout is detected, density, neutron, PEF, and sonic evidence can be down-weighted because these measurements may become less representative of the formation.
+
+---
+
+# Step 4 — Multi-Log Lithology Scoring
+
+At every usable depth, each lithology receives a score based on the available petrophysical evidence.
+
+The scoring system can incorporate:
+
+* gamma-ray index;
+* bulk density;
+* neutron porosity;
+* photoelectric factor;
+* sonic travel time;
+* logarithmic resistivity;
+* neutron-density separation;
+* and other configured evidence terms.
+
+Each feature contributes through configurable membership functions.
+
+The system uses **fuzzy/trapezoidal membership functions** rather than requiring a measurement to fall inside a single hard threshold.
+
+This allows gradual transitions between lithological interpretations.
+
+For example, a density measurement can provide partial support for multiple lithologies rather than producing an immediate binary decision.
+
+---
+
+# Missing Curves
+
+The benchmark explicitly supports incomplete log suites.
+
+If a curve is absent:
+
+```text
+NaN
+```
+
+is propagated through the scoring process.
+
+Missing data do not automatically invalidate a sample.
+
+Instead, the evidence contribution of the missing feature is removed from the total available evidence.
+
+This means a well with:
+
+```text
+GR
+RHOB
+NPHI
+RT
+```
+
+can still receive an interpretation even if:
+
+```text
+PEF
+DT
+CALI
+```
+
+are unavailable.
+
+However, fewer available curves generally produce greater uncertainty.
+
+---
+
+# Step 5 — UNKNOWN/MIXED Gate
+
+The system does not force every depth into one of the seven specific lithology classes.
+
+A depth can instead be assigned:
+
+```text
+UNKNOWN/MIXED
+```
+
+when the available evidence is insufficient.
+
+The gate considers factors such as:
+
+* total available evidence weight;
+* absolute score of the best lithology;
+* difference between the best and second-best lithology;
+* configured confidence thresholds.
+
+Conceptually:
+
+```text
+Strong evidence
+      ↓
+Specific lithology
+
+Weak / contradictory evidence
+      ↓
+UNKNOWN/MIXED
+```
+
+This is intended to prevent the benchmark from treating every ambiguous petrophysical response as a confidently identified lithology.
+
+---
+
+# Step 6 — Sequence Segmentation
+
+Raw depth-wise classifications can contain short oscillations caused by measurement noise or borderline scores.
+
+The reference-generation process therefore applies controlled smoothing and segmentation.
+
+The procedure includes:
+
+1. median/mean smoothing of depth-wise calls;
+2. detection of short runs;
+3. iterative absorption of intervals below the configured minimum bed thickness;
+4. assignment of short intervals to the better-fitting neighbouring lithology;
+5. merging of adjacent intervals with the same lithology.
+
+The final output is therefore a sequence of continuous geological intervals rather than a noisy label at every individual sample.
+
+---
+
+# Lithology Reference Signatures
+
+The benchmark uses configurable default petrophysical signatures.
+
+The principal plateau ranges are:
+
+| Lithology | GRI      | RHOB      | NPHI       | PEF     | DT      | LOGRT    | ND_SEP      |
+| --------- | -------- | --------- | ---------- | ------- | ------- | -------- | ----------- |
+| SHALE     | 0.65–2.5 | 2.2–2.65  | 0.25–0.45  | 2.5–3.8 | 85–140  | -0.2–0.7 | 0.05–0.22   |
+| SANDSTONE | -1–0.25  | 2.15–2.65 | -0.04–0.18 | 1.6–2.3 | 52–90   | 0–2.2    | -0.12–-0.02 |
+| LIMESTONE | -1–0.25  | 2.35–2.72 | -0.01–0.18 | 4.7–5.5 | 46–72   | 0.9–3    | -0.02–0.03  |
+| DOLOMITE  | -1–0.28  | 2.65–2.9  | 0.02–0.2   | 2.9–3.5 | 43–65   | 0.8–3    | 0.06–0.16   |
+| ANHYDRITE | -1–0.12  | 2.92–3    | -0.04–0.03 | 4.8–5.4 | 48–53   | 2.6–5    | 0.12–0.22   |
+| SALT      | -1–0.12  | 2–2.15    | -0.04–0.04 | 4.4–4.9 | 65–70   | 2.6–5    | -0.45–-0.25 |
+| COAL      | -1–0.55  | 1.2–1.7   | 0.45–0.85  | 0.2–1.5 | 110–160 | 1.3–3.5  | -0.25–0.25  |
+
+The complete membership functions, thresholds, weights, and other interpretation parameters are stored in:
+
+```text
+label_config.json
+```
+
+These ranges should be understood as **generic petrophysical signatures**, not universal geological laws.
+
+---
+
+# Evaluation
+
+The public test logs do not include their reference lithology labels.
+
+Researchers can therefore submit predictions generated from the held-out wells and evaluate them against the private reference set maintained by the benchmark authors.
+
+A typical model interface is:
+
+```python
+def my_model(well_curves_df):
+    # Return:
+    # top_depth_ft
+    # base_depth_ft
+    # lithology
+    ...
+```
+
+The expected output is a DataFrame with:
+
+```text
+top_depth_ft
+base_depth_ft
+lithology
+```
+
+For example:
+
+```python
+prediction = pd.DataFrame([
+    {
+        "top_depth_ft": 2150.0,
+        "base_depth_ft": 2191.5,
+        "lithology": "SHALE"
+    },
+    {
+        "top_depth_ft": 2191.5,
+        "base_depth_ft": 2230.0,
+        "lithology": "LIMESTONE"
+    },
+    {
+        "top_depth_ft": 2230.0,
+        "base_depth_ft": 2246.5,
+        "lithology": "UNKNOWN/MIXED"
+    }
+])
+```
+
+---
+
+# Evaluation Metrics
+
+The benchmark evaluates multiple aspects of model performance.
+
+## 1. Depth-weighted / pooled lithology accuracy
+
+Measures the proportion of evaluated depth samples for which the predicted lithology agrees with the reference.
+
+This captures overall depth coverage but can be dominated by thick intervals.
+
+---
+
+## 2. Depth macro-F1
+
+Macro-F1 gives greater importance to performance across individual lithology classes rather than allowing the most common lithologies to dominate the score.
+
+This is particularly relevant when lithologies have highly unequal thickness distributions.
+
+---
+
+## 3. Confident-reference evaluation
+
+Metrics can additionally be calculated only on reference depths where the underlying reference system has sufficient confidence.
+
+This separates:
+
+```text
+performance against all reference interpretations
+```
+
+from:
+
+```text
+performance against high-confidence reference interpretations
+```
+
+---
+
+# Boundary F1
+
+Lithology identification alone does not fully measure sequence reconstruction.
+
+A model can correctly identify the lithologies but place boundaries incorrectly.
+
+Boundary F1 therefore evaluates whether predicted lithological transitions occur close to reference boundaries.
+
+The benchmark reports boundary F1 at:
+
+```text
+±2.5 ft
+±5 ft
+±10 ft
+```
+
+A predicted boundary is considered a match when it falls within the corresponding tolerance of a reference boundary.
+
+This provides progressively more permissive measures of geological boundary localisation.
+
+---
+
+# Sequence Similarity
+
+The benchmark also evaluates the similarity of the ordered lithological sequence.
+
+The sequence metric is based on:
+
+```text
+1 − normalised edit distance
+```
+
+between the predicted and reference lithology sequences.
+
+For example:
+
+```text
+Reference:
+SHALE → SANDSTONE → LIMESTONE → DOLOMITE
+
+Prediction:
+SHALE → SANDSTONE → LIMESTONE → DOLOMITE
+```
+
+has identical sequence structure.
+
+A prediction such as:
+
+```text
+SHALE → LIMESTONE → DOLOMITE
+```
+
+has a different sequence even if the model correctly identifies several of the individual lithologies.
+
+This metric therefore captures **sequence-level agreement** rather than simply point-wise classification.
+
+---
+
+# Recommended Evaluation Table
+
+Researchers should report performance separately for the complete test set and, where applicable, high-confidence reference depths.
+
+A recommended format is:
+
+| Model   | Depth Macro-F1 | Accuracy | Boundary F1 ±2.5 ft | Boundary F1 ±5 ft | Boundary F1 ±10 ft | Sequence Similarity |
+| ------- | -------------: | -------: | ------------------: | ----------------: | -----------------: | ------------------: |
+| Model A |              — |        — |                   — |                 — |                  — |                   — |
+| Model B |              — |        — |                   — |                 — |                  — |                   — |
+
+Per-class metrics are also encouraged, particularly for:
+
+```text
+SANDSTONE
+LIMESTONE
+DOLOMITE
+ANHYDRITE
+SALT
+COAL
+```
+
+because these classes can be substantially less frequent than shale.
+
+---
+
+# Baselines
+
+The benchmark is designed to support comparison between several classes of approaches, including:
+
+* rule-based petrophysical interpretation;
+* classical machine-learning classifiers;
+* gradient-boosted models;
+* recurrent sequence models;
+* temporal convolutional networks;
+* Transformer-based sequence models;
+* hybrid physics/ML approaches;
+* foundation-model or multimodal approaches.
+
+A useful baseline should operate only on information available to the model at inference time and should not use the withheld reference labels.
+
+---
+
+# Evaluation Example
+
+A simplified evaluation workflow is:
 
 ```python
 from datasets import load_dataset
 import pandas as pd
-logs = pd.concat([load_dataset("NoraResearchLab/Lithology-Sequence-Benchmark", "logs", split=s).to_pandas() for s in ["test"]])
-ref  = pd.concat([load_dataset("NoraResearchLab/Lithology-Sequence-Benchmark", "intervals", split=s).to_pandas() for s in ["test"]])
 
-# my_model(well_curves_df) -> DataFrame[top_depth_ft, base_depth_ft, lithology]
-# summary, per_well = evaluate_model(my_model, logs, ref)      # evaluate_model / metrics live in the builder notebook
+logs = pd.concat([
+    load_dataset(
+        "NoraResearchLab/Lithology-Sequence-Benchmark",
+        "logs",
+        split=s
+    ).to_pandas()
+    for s in ["test"]
+])
+
+# Reference labels are privately maintained.
+# Public users submit predictions for scoring.
+
+# my_model(well_curves_df) ->
+# DataFrame[
+#   top_depth_ft,
+#   base_depth_ft,
+#   lithology
+# ]
+
+# summary, per_well = evaluate_model(
+#     my_model,
+#     logs,
+#     reference
+# )
 ```
 
-Reported metrics: pooled **depth macro-F1 / accuracy** (also on confident-reference depths only), **boundary F1** at ±2.5/5/10 ft, and
-**sequence similarity** (1 − normalised edit distance of the ordered lithology sequence).
+The exact evaluation implementation and private reference data are maintained separately from the public test inputs.
 
-## Important limitations — please read
+---
 
-- **These are rule-derived (silver-standard) labels, not core- or cutting-verified lithology.** They measure agreement with a transparent
-  petrophysical interpretation of the logs; they are not independent ground truth. A model with access to the same curves can in principle
-  learn the rules, so strong scores indicate rule recovery, not proven geological accuracy.
-- Default signatures are generic clean-matrix / textbook ranges, **not calibrated to Kansas**. Boundaries shift by a few feet because of smoothing.
-- Neutron logs are assumed limestone-scale unless the mnemonic says otherwise. Heavy mud, casing, salt-section washouts and bad tools can mislead any log-only method.
-- Sandstone-vs-limestone-vs-dolomite separation is weaker when PEF/DT are absent (tier B wells), which increases `UNKNOWN/MIXED`.
+# Data Leakage Considerations
 
-## Source data & attribution
+This benchmark is explicitly designed to prevent direct access to the evaluation labels.
+
+The public release contains:
+
+```text
+QC'd logs
++
+well metadata
+```
 
-Logs come from the public digital wireline-log archives of the **Kansas Geological Survey (KGS)**, University of Kansas
-(https://www.kgs.ku.edu/Magellan/Logs/). KGS distributes data "as is" without warranty; consult the KGS terms and disclaimer before
-downstream use. This is an independent derivative work, not endorsed by the KGS.
+but does not contain:
 
-## Maintainer
+```text
+test lithology intervals
+test depth-wise labels
+```
+
+Researchers should not attempt to reconstruct the private reference labels from benchmark metadata or implementation details and should not use the private reference representation during model development.
+
+Because the reference labels are generated by a deterministic rule-based system, a sufficiently detailed reproduction of the reference pipeline could potentially approximate the scoring target. Researchers using such an approach should clearly disclose that methodology.
+
+---
+
+# Label Quality
+
+The reference data contain two quality tiers:
+
+```text
+Tier A: 167 wells
+Tier B: 78 wells
+```
+
+The tiers reflect differences in the quality and completeness of the available wireline evidence.
+
+Tier A wells generally provide stronger multi-log evidence.
+
+Tier B wells may have more limited curve availability, increasing ambiguity in lithology discrimination.
+
+This is particularly relevant for distinguishing:
+
+```text
+SANDSTONE
+LIMESTONE
+DOLOMITE
+```
+
+when PEF or sonic information is unavailable.
+
+---
+
+# Important Limitations
+
+## Silver-standard reference labels
+
+The benchmark labels are **rule-derived silver-standard labels**.
+
+They are not equivalent to independently verified geological ground truth from:
+
+* core;
+* cuttings;
+* thin sections;
+* petrographic analysis;
+* formation-tester measurements;
+* or a geologist's independent interpretation.
+
+The benchmark therefore measures agreement with a transparent, deterministic petrophysical interpretation system.
+
+A high score demonstrates that a model can reproduce the benchmark's reference interpretation.
+
+It does **not**, by itself, establish that the model has achieved independently verified geological accuracy.
+
+---
+
+## Generic petrophysical signatures
+
+The default lithology signatures are based on generic clean-matrix / textbook-style petrophysical ranges.
+
+They are not calibrated specifically to every formation represented in the source archive.
+
+Formation-specific mineralogy, pore-fluid properties, compaction, diagenesis, borehole conditions, and logging-tool characteristics can shift observed responses.
+
+---
+
+## Neutron scale assumptions
+
+Neutron measurements are interpreted on a limestone-equivalent scale unless the source mnemonic indicates another interpretation.
+
+This can introduce ambiguity when different logging conventions or environmental corrections are present.
+
+---
+
+## Borehole effects
+
+Poor hole conditions can affect:
+
+* density;
+* neutron;
+* PEF;
+* sonic;
+* and other measurements.
+
+Caliper-based washout detection reduces the contribution of affected curves, but it cannot completely eliminate borehole-related uncertainty.
+
+---
+
+## Missing curves
+
+Not every well contains the complete curve suite.
+
+The benchmark therefore evaluates models under realistic missing-feature conditions.
+
+A model that requires every possible curve will have limited applicability across the complete benchmark.
+
+---
+
+## Synthetic / rule-derived interpretation
+
+Although the source measurements are real wireline logs, the reference lithology sequence is produced algorithmically.
+
+The benchmark should therefore be regarded as an evaluation of:
+
+```text
+wireline logs → benchmark lithology sequence
+```
+
+rather than:
+
+```text
+wireline logs → independently verified geological truth
+```
+
+---
+
+# Source Data
+
+The underlying wireline logs originate from the public digital wireline-log archives of the:
+
+**Kansas Geological Survey (KGS), University of Kansas**
+
+Source:
+
+https://www.kgs.ku.edu/Magellan/Logs/
+
+The benchmark is an independent derivative work and is **not endorsed by the Kansas Geological Survey or the University of Kansas**.
+
+Users should review the applicable KGS terms, disclaimers, and conditions before using the underlying data or derivative products in downstream commercial or research applications.
+
+---
+
+# Reproducibility and Versioning
+
+Several files are used to make the benchmark pipeline auditable and reproducible.
+
+### `label_config.json`
+
+Contains the lithology membership functions, scoring weights, confidence thresholds, segmentation parameters, and other reference-generation settings.
+
+### `curve_specs.json`
+
+Contains the curve mnemonic aliases, matching rules, unit rules, and standardisation logic.
+
+### `manifest.json`
+
+Contains dataset version information, file manifests, and cryptographic hashes.
+
+### Configuration fingerprint
+
+```text
+7b440d013c0a08a1
+```
+
+### Reference fingerprint
+
+```text
+92e77d413b4c86d4
+```
+
+These fingerprints allow benchmark users to identify the exact configuration and reference version associated with a reported result.
+
+---
+
+# Reproducibility Principles
+
+For meaningful comparisons, researchers should:
+
+1. Use the fixed test wells.
+2. Do not modify the public test inputs.
+3. Do not train on the withheld reference labels.
+4. Report the benchmark version/configuration.
+5. Report preprocessing performed by the model.
+6. Report how missing curves are handled.
+7. Report whether depth-wise predictions are post-processed into intervals.
+8. Report the exact model checkpoint used for evaluation.
+
+---
+
+# Intended Uses
+
+The benchmark is intended for:
+
+* lithology classification research;
+* automated well-log interpretation;
+* sequence modelling;
+* petrophysical machine learning;
+* geological boundary detection;
+* formation evaluation research;
+* benchmarking missing-log robustness;
+* comparing classical ML and deep-learning approaches;
+* evaluating AI systems for subsurface interpretation.
+
+It can also serve as a controlled test case for research into models that combine numerical sequence understanding with geological reasoning.
+
+---
+
+# Not Intended For
+
+The benchmark should not be used as the sole basis for:
+
+* drilling decisions;
+* reservoir development decisions;
+* formation abandonment decisions;
+* commercial reserve estimation;
+* safety-critical geological interpretation;
+* regulatory reporting;
+* or independent confirmation of geological conditions.
+
+Any operational subsurface application should use appropriately qualified geological and petrophysical review and independently validated reference data.
+
+---
+
+# Recommended Research Questions
+
+The benchmark can support research questions such as:
+
+**Can sequence models outperform independent depth-wise classifiers?**
+
+A model may exploit geological continuity and neighbouring depth information to produce more coherent intervals.
+
+**How much does missing-log robustness affect performance?**
+
+Researchers can compare models across wells with different available curve suites.
+
+**Can models identify thin beds without producing excessive segmentation noise?**
+
+Boundary F1 and sequence similarity provide complementary measures for this problem.
+
+**Can machine-learning models outperform generic petrophysical rules?**
+
+Because the reference labels are generated by a transparent rule system, this question should be interpreted carefully: performance improvements indicate better agreement with the reference construction, not necessarily independently verified geological superiority.
+
+**How well do models generalise across unseen wells?**
+
+The well-level split makes this a central benchmark property.
+
+---
+
+# Citation
+
+If you use the **Lithology Sequence Identification Benchmark** in research, publications, model evaluations, or derivative work, please cite this benchmark and acknowledge the underlying Kansas Geological Survey data source.
+
+### Benchmark
+
+```bibtex
+@misc{nora_lithology_sequence_benchmark_2026,
+  title        = {Lithology Sequence Identification Benchmark},
+  author       = {{NORA Research Lab}},
+  year         = {2026},
+  publisher    = {NORA Research Lab},
+  note         = {Evaluation benchmark for lithological sequence identification from wireline logs}
+}
+```
+
+### Source Data
+
+The underlying wireline logs originate from the public Kansas Geological Survey digital wireline-log archive.
+
+Please consult the source archive and applicable KGS terms for the appropriate attribution and data-use requirements.
+
+---
+
+# Maintainer
 
 **NORA Research Lab**
 
+NORA Research Lab develops datasets, benchmarks, models, and tools for artificial intelligence applied to scientific and real-world domains.
+
 [![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=black)](https://huggingface.co/NoraResearchLab)
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-NoraResearchLab-FFD21E?logo=huggingface\&logoColor=black)](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)
+[![X](https://img.shields.io/badge/X-@noraresearchlab-000000?logo=linkedin)](https://x.com/noraresearchlab)
+
+### Quick Links
+
+[Website](https://noraresearchlab.site) · [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)
+
+---
+
+# Summary
+
+The **Lithology Sequence Identification Benchmark** contains **245 wells and approximately 1.96 million depth samples** for evaluating AI and machine-learning systems that infer lithological sequences from wireline logs.
+
+The central task is:
+
+```text
+Complete wireline logs
+        ↓
+Lithology sequence
+        ↓
+Contiguous depth intervals
+```
+
+The benchmark contains eight possible lithology classes and explicitly supports `UNKNOWN/MIXED` predictions where the available evidence is insufficient.
+
+Unlike a conventional random sample classification dataset, the benchmark is split by **complete wells** and evaluates models at both the **depth level** and the **geological sequence level**.
 
-Quick links: [Website](https://noraresearchlab.site) · [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)
+The withheld reference labels, deterministic reference-generation pipeline, configuration fingerprints, and multiple evaluation metrics are intended to provide a reproducible framework for comparing automated lithology interpretation systems.