seismic-sensor

Real-time seismic P-wave detector using a streaming neural network ensemble, deployed to seismic.fib896.com.

How it works

A 3-seed ensemble of StreamingNet models (1D CNN + orbit-permuted Hebbian buffer) listens to live SeedLink feeds from GEOFON and supplementary networks. When N_CONSENSUS stations independently fire within a 60-second window, a detection is logged. Epicenters are estimated via a flat-earth P-wave arrival time inversion (Nelder-Mead). Detections are cross-checked against USGS and EMSC earthquake catalogs.

SeedLink stream → normalize → StreamingNet × 3 seeds → ensemble vote
→ consensus across stations → epicenter localization → catalog lookup → dashboard

Architecture

StreamingNet — 3-channel 1D CNN with an orbit-permuted Hebbian buffer:

Input: (3, 100)  — 3-component seismogram, 1 second at 100 Hz
→ ConvBlock(3→32) → ConvBlock(32→64) → ConvBlock(64→128) → AdaptiveAvgPool
→ orbit-permuted Hebbian buffer (CYCLES=1, DECAY=0.876, STRENGTH=1.429)
→ Linear(128, 2)  — seismic / noise classifier
→ Linear(128, 1)  — magnitude estimator

The buffer permutation is seeded per ensemble member, diversifying the feature basis across seeds. Pre-trained on the STEAD dataset (chunk2) with magnitude-stratified sampling, then fine-tuned on station-collected regional data.

Performance (STEAD holdout, threshold=0.835): 88.0% precision / 95.7% recall
Performance (after regional fine-tune, eval split): 94.4% / 93.2% / 93.2% precision across seeds, mean F1 = 0.919

Stations

Network	Code	Location
GE	APE	Aegean, Greece
GE	MORC	Morava, Czech Republic
GE	KBS	Svalbard, Norway
GE	WLF	Walferdange, Luxembourg
GE	MATE	Matera, Italy
GE	KARP	Karpathos, Greece
CX	PSGCX	Paso Grande, Chile
CX	HMBCX	Humberstone, Chile
DK	GDH	Godhavn, Greenland
DK	SCO	Scoresbysund, Greenland

Primary streams via geofon.gfz-potsdam.de:18000 (GEOFON).

IRIS note: rtserve.iris.washington.edu:18000 was upgraded to RingServer/4.5.6 (SeedLink v4.0 protocol) in 2026. obspy EasySeedLinkClient (v3.1) is incompatible — station SELECT and DATA commands are rejected. IRIS stations (IRIS_STATIONS in fly.toml) currently do not stream successfully; set to empty if you want a clean log.

Deploy

# Deploy to Fly.io (requires fly CLI + authenticated account)
make deploy

# Force full rebuild (e.g. after Dockerfile changes)
make deploy-clean

# Tail live logs
make fly-logs

Training

From scratch (STEAD)

Requires STEAD chunk2:

wget -c 'https://zenodo.org/records/3911667/files/chunk2.hdf5'
wget -c 'https://zenodo.org/records/3911667/files/chunk2.csv'

pip install torch numpy h5py scipy scikit-learn tqdm pandas

python train.py --data /data/training --checkpoints checkpoints/ --epochs 30

GPU strongly recommended. CPU training for 3 seeds × 30 epochs takes several hours.

Regional fine-tuning

collect_regional.py fetches P-wave windows for M5.5+ events at Pacific/Australian stations (IU.NWAO, II.WRAB, IU.MAJO, IU.SNZO) via IRIS FDSN, using IASP91 travel times to window around the P arrival:

python collect_regional.py   # saves .npz files to ./training/

Then fine-tune the existing ensemble on the collected windows:

python train.py --data training --checkpoints checkpoints --epochs 30 --lr 1e-4
# checkpoints/seed_{n}_pretrain.pt backups are written before overwriting

Fine-tuning uses a two-phase schedule: classifier head only for the first half of epochs (higher LR), then all layers unfrozen at 10× lower LR. A class-balanced weighted sampler handles imbalanced positive/negative ratios.

Local development

cp .env.example .env
# edit .env — set SEEDLINK_SERVER, STATIONS, etc.
make dev       # starts via docker compose
make logs      # tail logs
make shell     # bash into container

Configuration

All parameters are set via environment variables (see fly.toml and .env.example):

Variable	Default	Description
SEEDLINK_SERVER	geofon.gfz-potsdam.de:18000	Primary SeedLink server
IRIS_SERVER	rtserve.iris.washington.edu:18000	Secondary SeedLink server (see IRIS note above)
STATIONS	GE.APE,...	Comma-separated station list for primary server
IRIS_STATIONS	IU.COR,...	Comma-separated station list for secondary server
CHANNELS	HHZ,HHN,HHE	Seismic channels
THRESHOLD	0.835	Detection confidence threshold
N_CONSENSUS	4	Stations required to confirm a detection
CONSENSUS_WINDOW	60	Seconds within which consensus must occur
N_SEEDS	3	Ensemble size
STALTA_ON	1	Enable STA/LTA pre-filter (blocks low-energy noise)
STALTA_SHORT_S	0.5	STA window length (seconds)
STALTA_LONG_S	10.0	LTA window length (seconds)
STALTA_THRESH	2.5	Minimum STA/LTA ratio to pass to consensus

License

AGPL-3.0-or-later — see LICENSE