{"id":"W4403794510","doi":"10.1093/gji/ggae386","title":"Deep learning phase pickers: how well can existing models detect hydraulic-fracturing induced microseismicity from a borehole array?","year":2024,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Biogeoscience Institute, University of Calgary; Natural Environment Research Council; Sight Research UK","keywords":"Hydraulic fracturing; Borehole; Geology; Petroleum engineering; Seismology; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001345709,0.00121859,0.0005716439,0.0007612692,0.000180855,0.0009517664,0.001149619,0.0008984229,0.001255765],"category_scores_gemma":[0.003237773,0.0002701166,0.0005277253,0.0005691677,0.0004652869,0.001293179,0.0007509144,0.001060469,0.0006181252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006091,"about_ca_system_score_gemma":0.00073734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009488442,"about_ca_topic_score_gemma":0.009160927,"domain_scores_codex":[0.9997076,0.00006117106,0.00001768257,0.0000978326,0.00005554179,0.00006013724],"domain_scores_gemma":[0.9989266,0.0005374589,0.0001159071,0.0001206171,0.0002230555,0.00007640577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006401564,0.0002646988,0.04602003,0.000149005,0.0002327026,0.0001143152,0.00007047257,0.6515523,0.007034992,0.001119744,0.00658878,0.2862128],"study_design_scores_gemma":[0.00001015885,0.00006730864,0.001986094,0.00001331417,0.00001578823,0.00001561915,0.00002513385,0.9941941,0.002634559,0.0007276405,0.0003018063,0.000008515895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8310096,0.001918965,0.1563316,0.001437085,0.0001954809,0.0000642161,0.001207419,0.002991652,0.004844053],"genre_scores_gemma":[0.9824997,0.0002034367,0.01434123,0.0001878546,0.00003195945,0.00001648146,0.001276924,0.00004231191,0.001400077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009488442,"threshold_uncertainty_score":0.01886642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641863038665707,"score_gpt":0.261042563907218,"score_spread":0.2346239335205609,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}