README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
tags:
  - seismic
  - geophysics
  - oil-and-gas
  - exploration
  - reservoir-engineering
  - avo-analysis
  - reservoir-characterization
  - dhi
  - subsurface
  - synthetic-data
pretty_name: OIL-001 — Synthetic Seismic Survey Dataset (Sample)
size_categories:
  - 10K<n<100K
---

# OIL-001 — Synthetic Seismic Survey Dataset (Sample)

**XpertSystems.ai Synthetic Data Platform · SKU: OIL001-SAMPLE · Version 1.0.0**

This is a **free preview** of the full **OIL-001 — Synthetic Seismic Survey
Dataset** product. It contains roughly **~25% of the full dataset** at
identical schema, physics modeling, and seismic interpretation features,
so you can evaluate fit before licensing the full product.

| File                          | Rows (sample) | Rows (full)   | Description                                  |
|-------------------------------|---------------|---------------|----------------------------------------------|
| `seismic_traces.csv`          | ~28,800      | ~100,000      | Per-trace amplitude + attributes (25 cols)   |
| `horizon_catalog.csv`         | ~5,850        | ~30,000       | Interpreted horizons with rock physics (27 cols) |
| `survey_geometry.csv`         | ~3            | ~6            | Per-survey geometry & acquisition (23 cols)  |
| `interpretation_summary.csv`  | ~3            | ~6            | Per-survey interpretation KPIs (18 cols)     |

## Dataset Summary

OIL-001 is a **full seismic simulation engine** producing realistic 2D/3D
seismic waveforms with subsurface structure interpretation labels — the kind
of data exploration geophysicists, reservoir engineers, and seismic AI
companies (Bluware, Earth Science Analytics, OspreyData, Geoteric) build
their models on, but synthetic and free under CC-BY-NC for research.

**Physics modeling**:

- **Convolutional seismic model**: reflectivity series × wavelet (Ricker or
  zero-phase)
- **Aki-Richards approximation**: angle-dependent reflection coefficients
  for AVO analysis
- **Gassmann fluid substitution**: mineral/dry-frame/fluid moduli per layer
- **Gardner's relation**: density from Vp by lithology
- **NMO (Normal Moveout)**: velocity-based gather flattening for CDP stack
- **Hilbert transform**: instantaneous amplitude, phase, frequency attributes
- **Negative-pressure coherence**: fault-zone similarity attribute

**Survey acquisition modeling**:

- 3D land, 3D marine, 2D 2D-towed-streamer survey types
- Configurable inline/crossline grid spacing
- 9 offset panels (0°-40° in 5° steps) for AVO gathers
- CDP fold, sample interval (2 ms = 500 Hz), 4-second TWT record length

**Subsurface structure model** (8 structure labels):

- Anticline crest / Anticline flank / Syncline
- Fault plane / Fault shadow
- Salt flank
- Stratigraphic trap
- Flat background

**DHI (Direct Hydrocarbon Indicator) injection** (6 DHI labels):

- No DHI (most traces)
- Bright spot (amplitude anomaly indicating gas)
- Dim spot (amplitude reduction indicating oil)
- Flat spot (fluid contact reflection)
- Polarity reversal (Class IIp AVO response)
- AVO anomaly (Class III gas response)

**8 lithology types**:

sandstone (reservoir), shale (seal), limestone, dolomite, salt, anhydrite,
volcanic, basement

**5 fluid types** (with Gassmann fluid substitution):

brine, oil, gas, gas_condensate, tight_dry

**Seismic noise injection** (7 noise types):

- Surface multiple reflections
- Interbed multiple reflections
- Ambient random noise
- Source interference
- Coupling variation
- Statics anomaly
- Clean (no noise)

**Per-trace seismic attributes** (industry-standard interpretation features):

- Raw and processed amplitudes
- Instantaneous frequency, phase, amplitude (Hilbert transform)
- Envelope (dB)
- Coherence score (fault-zone similarity)
- Curvature (k1 principal curvature)
- AVO intercept and gradient (Aki-Richards)
- Noise type and flag

**Per-horizon rock physics**:

- Vp, Vs, Vp/Vs ratio
- Density (g/cc, Gardner-derived)
- Acoustic impedance
- Porosity %, water saturation %
- Net pay, dip angle, dip azimuth

**Prospect risking** (per-horizon):

- PGOS (Probability of Geological Success) — typical industry 0.20-0.40
- Trap closure area (km²)
- Reservoir quality class
- Fault association

## Calibrated Validation Results

Sample validation results across 10 seismic-domain KPIs:

| Metric | Observed | Target | Source | Verdict |
|--------|----------|--------|--------|---------|
| n_surveys_represented | 3 | 3 | Sample survey count | ✓ PASS |
| n_survey_types | 2 | 2 | 2D + 3D + 4D coverage | ✓ PASS |
| n_basins_represented | 3 | 3 | Geographic diversity | ✓ PASS |
| dominant_frequency_hz | 37.00 | 38.00 | Standard seismic dominant freq | ✓ PASS |
| snr_db_mean | 21.03 | 22.00 | Industry SNR target (post-stack) | ✓ PASS |
| horizon_pick_confidence | 0.861 | 0.880 | SEG interpreter confidence | ✓ PASS |
| horizon_continuity_index | 0.868 | 0.830 | Lateral continuity benchmark | ✓ PASS |
| fault_detection_confidence | 0.813 | 0.790 | Coherence-based fault picking | ✓ PASS |
| sandstone_porosity_pct_mean | 16.93 | 17.50 | Industry sandstone reservoir φ | ✓ PASS |
| n_horizon_labels_represented | 8 | 3 | Multi-class horizon diversity | ✓ PASS |

*Note: This dataset is designed for **seismic interpretation AI training** —
buyers building horizon-picking auto-trackers, fault-detection neural
networks, DHI classification models, or AVO inversion solvers can use these
labels for supervised training. The full product includes 6 surveys with
larger 3D grids and full structural diversity.*

## Schema Highlights

### `seismic_traces.csv` (primary file, 25 columns)

**Trace identification & geometry**:

| Column                | Type   | Description                                  |
|-----------------------|--------|----------------------------------------------|
| survey_id, gather_id  | int    | Survey and CDP gather IDs                    |
| cdp_x, cdp_y          | float  | CDP location (meters)                        |
| cdp_inline, cdp_crossline | int | 3D grid coordinates                        |
| offset_m              | float  | Source-receiver offset (m)                   |
| two_way_time_ms       | float  | Two-way time (ms)                            |

**Amplitudes & attributes**:

| Column                       | Type    | Description                              |
|------------------------------|---------|------------------------------------------|
| amplitude_raw                | float   | Pre-processing amplitude                 |
| amplitude_processed          | float   | After deconv + statics + multiples       |
| frequency_hz                 | float   | Instantaneous frequency                  |
| instantaneous_phase_deg      | float   | Hilbert phase (degrees)                  |
| instantaneous_amplitude      | float   | Hilbert envelope                         |
| envelope_db                  | float   | Envelope in dB                           |
| coherence_score              | float   | Coherence (0-1, fault zones low)         |
| curvature_k1                 | float   | Principal curvature                      |
| avo_intercept, avo_gradient  | float   | Aki-Richards AVO A, B                    |

**Labels**:

| Column                  | Type    | Description                                  |
|-------------------------|---------|----------------------------------------------|
| horizon_label           | string  | 8 horizon labels (basement, reservoir, etc.) |
| structure_label         | string  | 8 structure labels (anticline, fault, salt)  |
| dhi_label               | string  | 6 DHI labels                                 |
| noise_flag, noise_type  | string  | Noise contamination flag                     |
| fault_proximity_m       | float   | Distance to nearest fault                    |
| reservoir_quality_flag  | string  | Reservoir quality class                      |

### `horizon_catalog.csv` (27 columns)

Per-horizon rock physics + structural interpretation:

| Column                       | Description                                  |
|------------------------------|----------------------------------------------|
| horizon_name, horizon_label  | Named horizon + 8-class label                |
| two_way_time_ms, depth_m     | TWT and depth                                |
| dip_angle_deg, dip_azimuth   | Structural dip                               |
| interval_velocity_ms         | Interval Vp (m/s)                            |
| acoustic_impedance_mpa       | Acoustic impedance                           |
| vp_ms, vs_ms                 | Vp, Vs (m/s)                                 |
| density_gcc                  | Density (g/cc, Gardner)                      |
| porosity_pct                 | Porosity (%)                                 |
| water_saturation_pct         | Water saturation (%)                         |
| lithology_type               | 8 lithology classes                          |
| fluid_type                   | 5 fluid types                                |
| trap_style                   | Trap classification                          |
| dhi_class, dhi_label         | DHI class + 6-class label                    |
| reservoir_quality_class      | Reservoir quality                            |
| net_pay_m                    | Net pay thickness                            |
| fault_id                     | Associated fault                             |
| prospect_pgos                | Probability of Geological Success            |

### `survey_geometry.csv` (23 columns)

Per-survey acquisition geometry: survey_type, basin_name, n_inlines,
n_crosslines, cdp_spacing, fold, source_type, water_depth, acquisition
environment, processing vintage, migration type, SNR, dominant frequency.

### `interpretation_summary.csv` (18 columns)

Per-survey interpretation KPIs: horizon_pick_confidence, fault_confidence,
horizon_continuity_index, multiple_contamination_pct, etc.

## Suggested Use Cases

- **Auto horizon picking** — train CNNs to follow horizons across 3D
- **Fault detection** — train coherence/curvature-based fault networks
- **DHI classification** — 6-class hydrocarbon indicator prediction
- **AVO inversion** — predict elastic properties from intercept/gradient
- **Salt body segmentation** — geometric salt detection
- **Structural label segmentation** — 8-class structural interpretation
- **Lithology prediction** — 8-class lithology from seismic attributes
- **Fluid type prediction** (with Gassmann substitution targets)
- **Reservoir quality scoring** from acoustic impedance and porosity
- **Multiple suppression** — deep-learning-based denoise training
- **Statics correction** — anomaly detection at trace level
- **Wavelet estimation & deconvolution** — Ricker/zero-phase fitting
- **Velocity model building** — interval velocity prediction from traces
- **Time-to-depth conversion** — TWT-depth modeling
- **Prospect screening** — PGOS prediction from seismic features
- **Trap closure area estimation** from structural attributes
- **Insurtech-style geophysical AI training** without proprietary survey data
- **University geophysics curriculum** — synthetic teaching corpus

## Loading the Data

```python
import pandas as pd

traces   = pd.read_csv("seismic_traces.csv")
horizons = pd.read_csv("horizon_catalog.csv")
geometry = pd.read_csv("survey_geometry.csv")
interp   = pd.read_csv("interpretation_summary.csv")

# Multi-class horizon label target (8 classes)
y_horizon = traces["horizon_label"]

# Multi-class structural interpretation target (8 classes)
y_structure = traces["structure_label"]

# Multi-class DHI prediction target (6 classes)
y_dhi = traces["dhi_label"]

# Binary noise contamination
y_noise = traces["noise_flag"]

# Regression: AVO inversion targets
y_avo_a = traces["avo_intercept"]
y_avo_b = traces["avo_gradient"]

# Multi-class lithology from horizon (8 classes)
y_lithology = horizons["lithology_type"]

# Multi-class fluid type from horizon (5 classes)
y_fluid = horizons["fluid_type"]

# Regression: reservoir porosity
y_porosity = horizons["porosity_pct"]

# Regression: PGOS prospect risking
y_pgos = horizons["prospect_pgos"]

# Build 3D seismic cube for ML
cube = traces.pivot_table(
    index=["cdp_inline", "cdp_crossline"],
    columns="two_way_time_ms",
    values="amplitude_processed",
    aggfunc="mean"
)
```

## License

This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial
research and evaluation). The **full production dataset** is licensed
commercially — contact XpertSystems.ai for licensing terms.

## Full Product

The full OIL-001 dataset includes **6 surveys** with **120×80 3D grids**
each, full structural diversity (anticlines, synclines, salt diapirs,
stratigraphic traps), 14-layer velocity models with Gassmann substitution,
and full DHI/AVO anomaly catalogs. Calibrated to SEG seismic interpretation
standards, Aki-Richards AVO theory, and Gassmann fluid substitution theory.

📧  **pradeep@xpertsystems.ai**
🌐  **https://xpertsystems.ai**

## Citation

```bibtex
@dataset{xpertsystems_oil001_sample_2026,
  title  = {OIL-001: Synthetic Seismic Survey Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil001-sample}
}
```

## Generation Details

- Generator version : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-16 22:41:59 UTC
- Surveys           : 3 × (40 inlines × 30 crosslines × 9 offsets × 14 layers)
- Wavelet           : Ricker, 38 Hz dominant
- Physics           : Aki-Richards AVO + Gassmann + Gardner + Hilbert
- Calibration basis : SEG seismic interpretation standards
- Overall validation: 100.0 / 100  (grade A+)