PhaseNet-PyTorch

PyTorch implementation of PhaseNet for seismic and DAS phase picking, event detection, and polarity classification.

Models

The _tf variants add a Short-Time Fourier Transform (STFT) branch that extracts frequency features alongside the temporal waveform, improving performance on noisy data.

Prediction

CEED (Seismic) Prediction

# Demo: process a few events with plots
python scripts/predict_ceed.py --n-events 5

# Process all days for a year (saves parquets to results/ceed/)
python scripts/predict_ceed.py --all --year 2025 --output-dir results/ceed

Output: one parquet per day file in results/ceed/{region}/, with columns: event_id, station_id, waveform_index, origin_id, origin_index, origin_time, phase_index, phase_time, phase_score, phase_type, phase_polarity

DAS Prediction

# Predict with base PhaseNet on a HuggingFace subset
python scripts/predict_das.py --subset arcata --plot

# Predict with a trained DAS model on local data
python scripts/predict_das.py \
    --data-dir data/quakeflow_das/arcata/data \
    --model-type phasenet_das_plus \
    --checkpoint output/train_das_arcata/checkpoint.pth \
    --output-dir results/das/train_das_arcata/arcata \
    --no-ema --plot

# Predict from a file list
python scripts/predict_das.py \
    --file-list file_list.txt \
    --model-type phasenet_das_plus \
    --checkpoint output/model.pth \
    --plot

# Multi-GPU prediction
bash scripts/predict_das.sh phasenet_das_plus arcata 8 output/model.pth

Output: one parquet per event in results/das/{model_name}/{subset}/, with columns: event_id, channel_index, origin_id, origin_index, origin_time, phase_index, phase_time, phase_score, phase_type, dt_s, ps_center, ps_interval

By default, picks are associated using P-S pairing. Use --use-event-head to associate via the model's event detection head instead.

Training

CEED (Seismic) Training

python train.py \
    --model phasenet_plus \
    --dataset-type ceed \
    --label-path results/ceed \
    --nx 16 \
    --max-iters 100000 \
    --batch-size 8 \
    --workers 4 \
    --lr 3e-4 \
    --eval-interval 5000 \
    --output-dir output/train_ceed

DAS Training

# Using the training script
bash scripts/train_das.sh 0 arcata v26

# Or directly
python train.py \
    --model phasenet_das_plus \
    --dataset-type das \
    --data-path data/quakeflow_das/arcata/data \
    --label-path results/das/phasenet/arcata/picks \
    --label-list results/das/phasenet/arcata/labels.txt \
    --nx 2048 --nt 4096 \
    --num-patch 16 \
    --max-iters 50000 \
    --batch-size 2 --workers 8 \
    --lr 1e-4 --weight-decay 0.01 \
    --model-ema --model-ema-decay 0.999 \
    --eval-interval 1000 --save-interval 1000 \
    --output-dir output/train_das_arcata_v26

Key Training Options

Semi-supervised Training

Iterative self-training pipeline for DAS: predict → train on predictions → predict with new model → repeat.

# Start from PhaseNet (train DAS model from scratch)
bash scripts/semisupervised_das.sh arcata 5 0

# Start from a pretrained DAS model
bash scripts/semisupervised_das.sh arcata 5 0 phasenet_das output/train_das_v26/checkpoint.pth

Arguments: [subset] [num_iterations] [gpu] [start_from] [checkpoint]

• From phasenet: iteration 0 predicts with PhaseNet, iteration 1 trains from scratch (10k steps, warmup), iterations 2+ continue (1k steps, no warmup)
• From phasenet_das: iteration 0 predicts with pretrained DAS model, iterations 1+ continue (1k steps, no warmup)

Results are saved to results/semisupervised_das/ and checkpoints to output/semisupervised_das_v{N}/.