PhaseNet-Plus-XUNet-v0

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PhaseNet-Plus-UNet-v1

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PhaseNet-Plus-UNet-v0

PhaseNet+ with new U-Net backbone

PhaseNet-DAS-v1

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PhaseNet-DAS-v0

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PhaseNet-DAS-Plus-Arcata-v1

PhaseNet-DAS-Plus (Arcata)

Semi-supervised DAS phase picker trained on Arcata (Northern California) DAS data.

Model Details

• Architecture: PhaseNet-DAS-Plus (UNet backbone with event detection heads)
• Training data: 136 labeled events from Arcata DAS array, labels generated by PhaseNet-2018 with PS-pair association (origin_window=0.1, moveout_window=0.2)
• Input: (1, nx, nt) DAS strain rate, nx=2048, nt=4096
• Output: Phase picks (P/S/Noise) + event center + event time
• Training: 50k iterations, lr=1e-4, EMA decay=0.999, batch_size=2, num_patch=16
• Parameters: 9.3M

Usage

import torch
import phasenet.models.phasenet_das_plus as model_module

ckpt = torch.load("phasenet_das_plus_arcata_v1.pth", map_location="cpu")
model = model_module.build_model(**ckpt["model_config"])
model.load_state_dict(ckpt["model_ema_weights"])  # Use EMA weights
model.eval()

DASNet-v1

DASNet v1

Mask R-CNN for event detection and classification in Distributed Acoustic Sensing (DAS) data from submarine cables.

Model Details

• Architecture: Mask R-CNN with ResNet50-FPN backbone (selectable P4 layer)
• Training data: SeaFOAM Monterey Bay DAS deployment (Romanowicz et al., 2023)
• Input: (1, nx, nt) DAS strain rate, preprocessed to 3-channel RGB (raw, 2–10 Hz bandpass, >10 Hz highpass)
• Output: Bounding boxes, class labels, confidence scores, instance masks
• Classes: Blue whale A/B/D, Fin whale, P wave, S wave, T wave, Ship, Others (10 classes + background)
• Parameters: ~44M

Usage

import torch
from dasnet import build_dasnet_model, load_checkpoint

model = build_dasnet_model()
load_checkpoint(model, "dasnet_v1.pth", torch.device("cpu"))

Reference

Zhang, C., et al. (2026). "A deep learning framework for marine acoustic and seismic monitoring with distributed acoustic sensing." arXiv:2603.14844

PhaseNet-v1

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PhaseNet-TF-LCSN

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PhaseNet-Plus-v1

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