# First-arrival picking

First arrivals in mountainous seismic data are often weak, intermittent, and masked by strong direct waves and noise, so energy-based pickers jump to later arrivals or noise, and deep-learning pickers often need retraining for a new survey. FA-Agent combines a multimodal large language model, deterministic processing tools, and skill files that carry processing experience into an extensible framework: the agent surveys the whole gather to mark doubtful zones, then examines them enlarged and decides per segment whether to keep, correct, or remove each pick. On semi-synthetic gathers with known first arrivals and real mountainous noise, it picked clearly visible first arrivals accurately and removed picks where the first arrival was invisible instead of picking later events.

First arrivals in mountainous seismic data are often weak, intermittent, and masked by strong direct waves and noise, so energy-based pickers jump to later arrivals or noise, and deep-learning pickers often need retraining for a new survey. FA-Agent combines a multimodal large language model, deterministic processing tools, and skill files that carry processing experience into an extensible framework: the agent surveys the whole gather to mark doubtful zones, then examines them enlarged and decides per segment whether to keep, correct, or remove each pick. On semi-synthetic gathers with known first arrivals and real mountainous noise, it picked clearly visible first arrivals accurately and removed picks where the first arrival was invisible instead of picking later events.