We present a machine learning approach for earthquake location and magnitude estimation based on seismic arrival time patterns,using Histogram-Based Gradient Boosting for its high accuracy and computational efficiency...We present a machine learning approach for earthquake location and magnitude estimation based on seismic arrival time patterns,using Histogram-Based Gradient Boosting for its high accuracy and computational efficiency.The model is first evaluated using a synthetic earthquake bulletin that simulates realistic network geometry,station-event distributions,and incorporates a 3D velocity model for accurate travel-time compu-tation.Input features include P and S arrival times and amplitudes,while targets consist of location,origin time,magnitude,and uncertainty measures(horizontal and depth errors,azimuthal gap).Model performance is evaluated using𝑅2,Mean Absolute Error(MAE),and Median Absolute Error(MEDAE),demonstrating high accuracy across datasets with varying levels of completeness.Finally,we validate the model using real-world data from the Ahar-Varzaghan 2012 aftershock sequence in NW Iran.The model accurately recovers key spatial patterns of seismicity despite significant missing data,and the results align with previous high-resolution studies.These findings confirm that the proposed method generalizes well beyond synthetic settings and offers a fast,robust alternative for operational seismic networks and rapid hazard assessment.展开更多
基金support provided by the Interna-tional Institute of Earthquake Engineering and Seismology(IIEES)un-der project number 5424-816.
文摘We present a machine learning approach for earthquake location and magnitude estimation based on seismic arrival time patterns,using Histogram-Based Gradient Boosting for its high accuracy and computational efficiency.The model is first evaluated using a synthetic earthquake bulletin that simulates realistic network geometry,station-event distributions,and incorporates a 3D velocity model for accurate travel-time compu-tation.Input features include P and S arrival times and amplitudes,while targets consist of location,origin time,magnitude,and uncertainty measures(horizontal and depth errors,azimuthal gap).Model performance is evaluated using𝑅2,Mean Absolute Error(MAE),and Median Absolute Error(MEDAE),demonstrating high accuracy across datasets with varying levels of completeness.Finally,we validate the model using real-world data from the Ahar-Varzaghan 2012 aftershock sequence in NW Iran.The model accurately recovers key spatial patterns of seismicity despite significant missing data,and the results align with previous high-resolution studies.These findings confirm that the proposed method generalizes well beyond synthetic settings and offers a fast,robust alternative for operational seismic networks and rapid hazard assessment.
