{"id":"W4321488019","doi":"10.1109/tgrs.2023.3247593","title":"Optimal Seismic Sensor Placement Based on Reinforcement Learning Approach: An Example of OBN Acquisition Design","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Computer science; Reinforcement learning; Wireless sensor network; Node (physics); Detector; Notation; Quality (philosophy); Software deployment; Geophone; Seismic survey; Data mining; Algorithm; Artificial intelligence; Engineering; Mathematics; Geology; Seismology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009932063,0.0007212128,0.000669266,0.0003875138,0.0003178819,0.0005907586,0.0006790992,0.0009176028,0.001994838],"category_scores_gemma":[0.002623299,0.0003526564,0.000359227,0.000357984,0.00071318,0.0005807301,0.00086113,0.0006844916,0.0003314887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006635554,"about_ca_system_score_gemma":0.0008838139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003292331,"about_ca_topic_score_gemma":0.003616285,"domain_scores_codex":[0.9994524,0.0002106107,0.00002870259,0.0001222869,0.0001199967,0.00006602751],"domain_scores_gemma":[0.9990399,0.0004926592,0.0001160059,0.00008522793,0.0002169605,0.00004918091],"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":[0.0000927749,0.00004053627,0.0007019241,0.00007691589,0.00001828055,0.0001363915,0.00007935965,0.929142,0.005707341,0.006937465,0.000685419,0.05638168],"study_design_scores_gemma":[0.00001383128,0.000041857,0.0001022324,0.000005967879,0.000004907424,0.00002828235,0.00001343002,0.9956822,0.001231941,0.00235989,0.0005100829,0.000005358431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01080951,0.00008855512,0.986869,0.0001402265,0.00001481287,0.00004589883,0.00002120753,0.0001575555,0.001853166],"genre_scores_gemma":[0.6023884,0.0001468453,0.3948124,0.0001028731,0.00002723197,0.0001338074,0.00005334015,0.00005846604,0.002276665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003292331,"threshold_uncertainty_score":0.006673455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04365776794830735,"score_gpt":0.2451782146434696,"score_spread":0.2015204466951622,"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."}}