{"id":"W2597325747","doi":"10.1190/geo2016-0520.1","title":"Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments","year":2017,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; University of British Columbia; National Natural Science Foundation of China; National Science Foundation","keywords":"Multiple; Wavelet; Algorithm; Inversion (geology); Amplitude; Computer science; Context (archaeology); Optics; Mathematics; Geology; Physics; Seismology; Arithmetic; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001719733,0.0001145302,0.0001344563,0.00002052728,0.0005209364,0.00009683746,0.0006989141,0.00005090582,0.00004339743],"category_scores_gemma":[0.00009581626,0.00009353889,0.00001351501,0.00004454013,0.0001200699,0.0009843626,0.0001815233,0.0001085542,0.00002634765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003231948,"about_ca_system_score_gemma":0.00006008107,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007313534,"about_ca_topic_score_gemma":0.00002820609,"domain_scores_codex":[0.9991197,0.00003098515,0.0001013689,0.0004229787,0.0001453574,0.0001796027],"domain_scores_gemma":[0.9984596,0.0001650837,0.0001175108,0.001144818,0.00002895884,0.00008402477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007166706,0.0001842996,0.2739999,0.00009587588,0.0001622946,0.00003254331,0.00116405,0.0007528778,0.000584576,0.00004482298,0.1226696,0.5995926],"study_design_scores_gemma":[0.0006358665,0.0002018431,0.01066652,0.00003898511,0.00002756634,0.000006053052,0.0002054568,0.9739838,0.001523698,0.0008670825,0.01163409,0.0002090335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832444,0.0001186776,0.01179178,0.001603149,0.000285247,0.0003842762,0.0008814915,0.0001196253,0.001571357],"genre_scores_gemma":[0.9828798,0.00005852081,0.01469861,0.0007734228,0.00003953177,7.135264e-7,0.001419133,0.000004756508,0.0001255037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.973231,"threshold_uncertainty_score":0.9992968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0700988761894967,"score_gpt":0.3033878865927192,"score_spread":0.2332890104032225,"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."}}