yahya021/earthquakes-stpp

Earthquakes JP STPP Benchmark Dataset

A benchmark-ready Spatio-Temporal Point Process (STPP) dataset derived from earthquake event data in Japan (1990–2019), following the official split semantics of the Neural STPP paper.

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Dataset Description

Each record represents a sequence of earthquake events within a weekly time window in Japan. Events capture the time and location (epicenter) of each earthquake above the magnitude threshold.

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Source Format

Raw data was stored in a NumPy .npz archive (earthquakes_jp.npz).
Each key is a date string of the form YYYYMMDD (e.g. 19900101), marking the start of the weekly window. Keys are spaced 7 days apart.

Each key maps to a (N, 3) float64 array with columns [t, x, y].

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Sequence Unit

One .npz key = one sequence (weekly window).
No new windowing or segmentation was applied.

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Event Schema

Field	Type	Description
t	float	Days elapsed since the start of the weekly window (range ≈ 0–30)
x	float	Longitude of earthquake epicenter (Japan range ≈ 122.6 to 146.1)
y	float	Latitude of earthquake epicenter (Japan range ≈ 23.3 to 44.2)

Values are exported as-is — no normalization applied.

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File Structure

earthquakes-stpp/
├── train.jsonl        # 950 sequences
├── val.jsonl          # 50 sequences
├── test.jsonl         # 50 sequences
├── splits.json        # {"train": [...], "val": [...], "test": [...]}
├── dataset_meta.json  # Task/schema metadata
└── README.md

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JSONL Row Schema

{
  "sequence_id": "19900101",
  "events": [
    {"t": 0.38, "x": 131.2, "y": 33.4},
    ...
  ]
}

Example (Python)

import json

with open("train.jsonl") as f:
    for line in f:
        seq = json.loads(line)
        sid    = seq["sequence_id"]
        events = seq["events"]
        t = [e["t"] for e in events]
        x = [e["x"] for e in events]
        y = [e["y"] for e in events]

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Source & License

• Source data: USGS / Japan Meteorological Agency earthquake catalog, Japan region 1990–2019
• Processing code reference: facebookresearch/neural_stpp (MIT License)