{"id":"W4321327328","doi":"10.1029/2022jb025964","title":"Implicit Seismic Full Waveform Inversion With Deep Neural Representation","year":2023,"lang":"en","type":"article","venue":"Journal of Geophysical Research Solid Earth","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Ocean University of China; China Postdoctoral Science Foundation","keywords":"Initialization; Inversion (geology); Inference; Computer science; Algorithm; Inverse problem; Geophysical imaging; Seismic inversion; Deep learning; Bayesian inference; Waveform; Artificial neural network; Bayesian probability; Artificial intelligence; Geology; Geophysics; Seismology; Mathematics; Data assimilation; Meteorology; Telecommunications","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":[0.0006092495,0.0005396787,0.0003271907,0.0002848029,0.0001397689,0.0006500562,0.001016601,0.0007147943,0.001825177],"category_scores_gemma":[0.002461269,0.0003608832,0.0003488441,0.0003949342,0.0004077725,0.001405419,0.001065207,0.001483244,0.0004718884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004149606,"about_ca_system_score_gemma":0.0009214038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004249566,"about_ca_topic_score_gemma":0.005511121,"domain_scores_codex":[0.9998589,0.00004192177,0.000008372001,0.00002318779,0.00004828397,0.0000193805],"domain_scores_gemma":[0.9994385,0.0002771934,0.00005790464,0.00009482871,0.000103147,0.00002843768],"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":[0.00006288828,0.00002938796,0.0004307666,0.00003727763,0.00002173962,0.00003290294,0.00003047675,0.912936,0.005069163,0.009831444,0.0008129101,0.07070497],"study_design_scores_gemma":[0.000001312819,0.000003116442,0.00001861123,0.000001116553,5.201654e-7,0.000002145909,9.94816e-7,0.998446,0.0003383657,0.001106804,0.00007998879,0.000001014483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02784103,0.00007915498,0.9692947,0.0002212694,0.0000227146,0.00001965354,0.0001051541,0.0006102887,0.001805968],"genre_scores_gemma":[0.6505067,0.0001147512,0.3448319,0.0001352729,0.00003668505,0.00007952983,0.0005082073,0.0001634188,0.003623599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004249566,"threshold_uncertainty_score":0.008449674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04148452149159892,"score_gpt":0.3250095647305852,"score_spread":0.2835250432389863,"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."}}