Stead Distance Prediction Eval

---
name: stead-distance-prediction-eval
description: This benchmark evaluates whether deep learning models can accurately predict the epicentral distance of an earthquake from single-station ground motion waveforms. It specifically probes whether models learn intrinsic seismic features or merely exploit highly correlated auxiliary signals like P/S wave arrival times. Use when the user wants to benchmark on Stanford Earthquake Dataset (STEAD), or asks about evaluating this task. Reports Mean Absolute Error (MAE).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2403.07569
  bibtex_key: caglar2024groundmotion
  confidence: medium
---

# stead-distance-prediction-eval

> Exploring Challenges in Deep Learning of Single-Station Ground Motion Records — Çağlar et al. (2024) (arXiv:2403.07569, 2024)

## What this evaluates

This benchmark evaluates whether deep learning models can accurately predict the epicentral distance of an earthquake from single-station ground motion waveforms. It specifically probes whether models learn intrinsic seismic features or merely exploit highly correlated auxiliary signals like P/S wave arrival times.

## Datasets

- **Stanford Earthquake Dataset (STEAD)** — total ?; splits: train (-1), test (-1)

## Metrics

- `Mean Absolute Error (MAE)` **(primary)** — range: other
  - L1 loss computed as the average absolute difference between predicted epicentral distance and true epicentral distance. The true distance is calculated using the haversine formula in kilometers.

## Input / output format

**Input**: Three-channel ground motion time-series signals, optionally concatenated with P/S wave arrival time information.

**Output**: A single scalar value representing the predicted epicentral distance in kilometers.

## Scoring recipe

```python
def compute_mae(predictions, true_distances_km):
    # true_distances_km are precomputed via haversine formula
    mae = np.mean(np.abs(predictions - true_distances_km))
    return mae
```

## Common pitfalls

- Models may achieve low error by simply learning the P-S arrival time difference rather than extracting features from the waveform itself.
- Epicentral distance must be computed using the haversine formula on spherical coordinates, not Euclidean distance.

## Evidence (verbatim from paper)

> For distance prediction, mean absolute error (L1 loss) is chosen and directly calculated using the haversine distance in kilometers between the epicenter and the recording station.

## Citation

```bibtex
@misc{caglar2024groundmotion,
  title={Exploring Challenges in Deep Learning of Single-Station Ground Motion Records},
  author={Çağlar et al. (2024)},
  year={2024},
  note={arXiv:2403.07569}
}
```

- arXiv: 2403.07569