Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator

Researchers have developed Phase Neural Operator (PhaseNO), a transfer learning model for microseismic phase picking that fine-tunes only 3.6% of its parameters on small datasets, significantly improving performance in low signal-to-noise ratio environments compared to existing methods. This advancement is crucial for real-time monitoring and subsurface imaging with limited labeled data, offering practical solutions for deploying network-wide models efficiently.

Read the full article at arXiv cs.LG (ML)

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