README.md

---
pretty_name: Extreme Environment Generator
dataset_name: Extreme_Environment_Generator
annotations_creators:
- no-annotation
language_creators:
- no-annotation
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- generated
task_categories:
- time-series-forecasting
task_ids:
- multivariate-time-series-forecasting
---

# DBbun / Extreme_Environment_Generator

## Table of Contents
- [Dataset Summary](#dataset-summary)  
- [Environments](#environments)  
- [Dataset Structure](#dataset-structure)  
  - [State Table](#state-true)  
  - [Sensor Table](#sensors)  
  - [Failure Table](#failures)  
  - [Layer Profile Table](#layer-profile)  
  - [Environment Config Table](#environment-config)  
  - [Summary Table](#summary)  
- [Sensors](#detailed-sensor-descriptions)  
- [State Fields](#detailed-state-fields)  
- [Use Cases](#use-cases)  
- [Limitations](#limitations)  
- [License](#license)  
- [Citation](#citation)  


## Dataset Summary

**Extreme_Environment_Generator** is a large synthetic dataset produced by a configurable Python simulator. It generates multi-sensor time-series data from extreme physical environments inspired by:

- deep-Earth subsurface  
- subsea environments beneath oceans  
- deep ice-world and icy-planet structures  

Each generated scenario includes:

- the true physical state of the environment over time  
- corrupted/noisy sensor measurements  
- layer and material profiles  
- environmental configuration  
- rare failure events  
- scenario-level summary statistics  

The dataset is designed for machine learning research, including:

- multivariate forecasting  
- sensor fusion  
- anomaly and rare-event detection  
- robustness testing under noise, drift, and missing data  
- reinforcement learning in extreme synthetic environments  
- foundation model pretraining for time series  


## Environments

### 1. Deep Subsurface
A high-pressure, high-temperature environment inspired by Earth’s crust, mantle, outer core, and inner core.  
Includes 5 fixed layers from 0 km to 6371 km depth.

### 2. Subsea
Includes water, sediment, and crust layers, followed by deep subsurface structure.  
Lower temperatures and pressures in early depth stages, transitioning to mantle-like profiles.

### 3. Ice World
Inspired by icy moons and exoplanets.  
Includes multi-kilometer ice shells, ice–rock transition, and deep interior structure.

All three environments use the same schema, allowing direct cross-environment comparison.


## Dataset Structure

### state_true
Ground-truth physical state at every time step.

| Field | Description | Unit |
|-------|-------------|------|
| scenario_id | scenario index | — |
| time_sec | time since start | seconds |
| depth_km | simulated depth | km |
| true_temperature_C | real internal temperature | °C |
| true_pressure_GPa | real pressure | GPa |
| true_density_kg_m3 | real density | kg/m³ |
| true_phase | solid/liquid/plastic | — |
| true_seismic_noise | baseline seismic activity | arbitrary |
| true_vibration_level | structural vibration | arbitrary |
| stability_score | stability (0–1) | — |
| rare_event_flag | True if an extreme event occurred | bool |


### sensors
Corrupted sensor readings derived from state_true.

| Sensor | Field Prefix | Description |
|--------|--------------|-------------|
| Temperature | temperature_ | noisy, drifting temperature |
| Pressure | pressure_ | noisy pressure with dropout |
| Accelerometer | accel_ | acceleration in g |
| Seismic | seismic_ | seismic noise + dropout |
| Quantum Resonance | quantum_resonance_ | fictional high-sensitivity sensor |
| Neutrino Flux | neutrino_flux_ | fictional deep-environment reading |
| Spin Coherence | spin_coherence_ | fictional material spin measurement |
| Gravity Wave | gravity_wave_ | fictional micro-gravity perturbations |

All sensors include controlled noise, drift, and dropout behavior.


### failures
Rare catastrophic or near-catastrophic events.

| Field | Description |
|--------|-------------|
| scenario_id | scenario index |
| time_sec | event time |
| depth_km | event depth |
| event_type | “thermal_spike”, “pressure_surge”, etc. |
| severity | 0–1 normalized severity |


### layer_profile
Static description of each environment’s internal layers.

| Field | Description |
|--------|-------------|
| scenario_id | scenario index |
| layer_id | layer index |
| name | layer name |
| depth_start_km | start depth |
| depth_end_km | end depth |
| material_name | material |
| density_kg_m3 | perturbed density |
| phase | solid/plastic/liquid |


### environment_config
The full configuration of each scenario (planet, sensors, perturbations, etc.).

### summary
High-level statistics per scenario, including max temperature, average pressure, number of failures, and stability metrics.


## Detailed Sensor Descriptions

### Temperature Sensor
- Gaussian noise in °C  
- Linear drift over time  
- Occasional dropout  

### Pressure Sensor
- Multiplicative noise (%)  
- Dropout probability  
- Tracks large pressure gradients  

### Accelerometer
- Measures vibration and acceleration  
- Useful for instability detection  

### Seismic Sensor
- Low-frequency seismic noise  
- Sensitive to layer boundaries and rare events  

### Quantum Resonance Sensor (fictional)
- Extremely small fluctuations  
- Useful for ML models requiring additional modality  

### Neutrino Flux Sensor (fictional)
- Sensitive to density changes  
- Inspired by high-energy particle flux  

### Spin Coherence Sensor (fictional)
- Measures microscopic oscillatory behavior  
- Correlates weakly with phase transitions  

### Gravity-Wave Sensor (fictional)
- Very small signal  
- Produces ultra-low-level noise  
- Adds complexity to multimodal fusion tasks  


## Detailed State Fields

### true_temperature_C
Real physical temperature at depth, following the environment’s temperature profile.

### true_pressure_GPa
Real pressure increasing with depth.

### true_density_kg_m3
Density of the current layer (with ±5% random perturbation).

### true_phase
Categorical:
- solid  
- plastic  
- liquid  

### true_seismic_noise
Base seismic level.

### true_vibration_level
Simulated mechanical vibration.

### stability_score
A scalar between 0 and 1 representing the local stability of the environment.

### rare_event_flag
Boolean indicating whether a rare catastrophic event occurred at that time step.


## Use Cases

- Multimodal sensor fusion  
- Long-horizon forecasting  
- Rare event detection  
- Model robustness evaluation  
- Foundation-model pretraining for time series  
- Reinforcement learning in synthetic physical environments  
- Synthetic data research  
- Instrumentation testbeds  


## Limitations

- Physics is approximate and synthetic  
- Sensor noise is parameterized, not empirically derived  
- Rare events are artificially triggered  
- Layer boundaries are simplified  


## Citation

- Kartoun, U. (2025). Extreme Environment Generator — Synthetic extreme-environment dataset for multimodal time-series research. DBbun LLC.