# Earthquakenpp Eval

> This benchmark evaluates the forecasting capability of neural spatio-temporal point processes (NPPs) on earthquake sequences. It probes how well models capture the joint temporal and spatial intensity of seismic events compared to traditional seismological baselines like ETAS. Use when the user wants to benchmark on EarthquakeNPP (ComCat, QTM_SaltonSea, QTM_SanJac, White, SCEDC), or asks about evaluating this task. Reports temporal log-likelihood.

- Skill: `qhjqhj00/earthquakenpp-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/earthquakenpp-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/earthquakenpp-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/earthquakenpp-eval

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# earthquakenpp-eval

> EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes — Stockman, Lawson, and Werner (2024) (arXiv:2410.08226, 2024)

## What this evaluates

This benchmark evaluates the forecasting capability of neural spatio-temporal point processes (NPPs) on earthquake sequences. It probes how well models capture the joint temporal and spatial intensity of seismic events compared to traditional seismological baselines like ETAS.

## Datasets

- **EarthquakeNPP (ComCat, QTM_SaltonSea, QTM_SanJac, White, SCEDC)** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `temporal log-likelihood` **(primary)** — range: other
  - Sum of the log of the predicted temporal intensity function evaluated at each observed event time in the test set. Higher values indicate better temporal forecasting performance.
- `spatial log-likelihood` — range: other
  - Sum of the log of the predicted spatial probability density function evaluated at each observed event location in the test set. Higher values indicate better spatial forecasting performance.

## Input / output format

**Input**: Historical spatio-temporal earthquake event sequences (timestamps and coordinates) up to a defined split point, used to train or condition the model.

**Output**: Predicted conditional intensity function (or spatial density) over the test period, evaluated as log-likelihood scores per event.

## Scoring recipe

```python
def compute_log_likelihood(test_events, model):
    total_ll = 0.0
    for event in test_events:
        t, x = event.time, event.location
        intensity = model.predict_intensity(t, x, history)
        total_ll += np.log(intensity)
    return total_ll
```

## Common pitfalls

- The ETAS baseline incorporates earthquake magnitude dependence in its intensity function, which is explicitly excluded from the NPP implementations in this benchmark.
- Validation data is combined with training data for fitting ETAS, whereas NPPs follow a strict train/val/test split, creating an asymmetry in model calibration.
- NSTPP lacks memory of prior events, so its likelihood evaluation on test events does not benefit from historical context like the other NPPs or ETAS.

## Evidence (verbatim from paper)

> Figures 2 and 3 report the temporal and spatial log-likelihood scores of all models on the EarthquakeNPP datasets. The ETAS model achieves the highest temporal and spatial log-likelihood across all datasets, with some NPP models achieving comparable temporal performance on ComCat, QTM_SaltonSea, QTM_SanJac, and White catalogs.

## Citation

```bibtex
@misc{stockman2024earthquakenpp,
  title={EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes},
  author={Stockman, Lawson, and Werner (2024)},
  year={2024},
  note={arXiv:2410.08226}
}
```

- arXiv: 2410.08226