{"id":"W4390754920","doi":"10.3389/feart.2023.1285622","title":"Compressed sensing with log-sum heuristic recover for seismic denoising","year":2024,"lang":"en","type":"article","venue":"Frontiers in Earth Science","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Guilin University of Electronic Technology; Nova Scotia Department of Energy; Guangxi Provincial Key Laboratory of Precision Navigation Technology and Application, Guilin University of Technology; U.S. Department of Energy","keywords":"Algorithm; Computer science; Compressed sensing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001189072,0.001051061,0.001089749,0.0009490621,0.0004127029,0.00101376,0.001164878,0.001338021,0.003550217],"category_scores_gemma":[0.00450005,0.0004247736,0.0009204675,0.001424868,0.001060114,0.00137895,0.001505783,0.002698551,0.001081404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008635313,"about_ca_system_score_gemma":0.001470798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790454,"about_ca_topic_score_gemma":0.004996177,"domain_scores_codex":[0.9992656,0.0002251473,0.00003171958,0.0001157683,0.0003038943,0.00005785213],"domain_scores_gemma":[0.9989685,0.0006602406,0.00006740991,0.00009235325,0.0001680408,0.00004349096],"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":[0.0003113269,0.000118659,0.0004547935,0.0003609545,0.00009901666,0.0001848016,0.0001464194,0.6105572,0.008144761,0.08627206,0.01302051,0.2803296],"study_design_scores_gemma":[0.000008658481,0.00002823252,0.00006544842,0.00001323572,0.000008526593,0.00003358859,0.00001286798,0.9840184,0.001306885,0.01262497,0.001868635,0.00001056613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003557324,0.000847214,0.9918913,0.0003898394,0.00008049718,0.00003363988,0.00008642719,0.0002644437,0.002849367],"genre_scores_gemma":[0.2176162,0.003202733,0.763045,0.0007241549,0.0004883119,0.0002640736,0.001110989,0.0003488062,0.01319978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003790454,"threshold_uncertainty_score":0.0118767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040990145112647,"score_gpt":0.2167773420280391,"score_spread":0.2063674405769126,"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."}}