{"id":"W1558135433","doi":"10.1111/1365-2478.12234","title":"Enhancing 3D post‐stack seismic data acquired in hardrock environment using 2D curvelet transform","year":2015,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"Narodowe Centrum Nauki","keywords":"Curvelet; Noise (video); Deconvolution; Noise reduction; Energy (signal processing); Computer science; Stack (abstract data type); Synthetic data; Geology; SIGNAL (programming language); Footprint; Attenuation; Algorithm; Seismology; Artificial intelligence; Wavelet; Wavelet transform; Image (mathematics); Statistics; Mathematics; Physics; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005724786,0.0009617258,0.0004444041,0.00178888,0.0002481719,0.0009141959,0.0005690728,0.0007821531,0.001004376],"category_scores_gemma":[0.00116843,0.0002907313,0.0005840578,0.001210388,0.000418433,0.0007057609,0.000948997,0.0007300354,0.000838813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002758022,"about_ca_system_score_gemma":0.0007818532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002911594,"about_ca_topic_score_gemma":0.003903213,"domain_scores_codex":[0.9996794,0.00003134323,0.0000153699,0.00005442537,0.0001748562,0.00004466269],"domain_scores_gemma":[0.9993995,0.0001264562,0.00008505874,0.00009005799,0.0002603964,0.00003848188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003579857,0.0002609799,0.008197906,0.0003082036,0.0001134554,0.0008616507,0.0004870881,0.2165317,0.4309878,0.002914532,0.002701238,0.3362776],"study_design_scores_gemma":[0.00001483487,0.0001065901,0.009596066,0.00001686033,0.0000426926,0.0002937306,0.0001328036,0.8744196,0.1101031,0.001460176,0.003749302,0.00006426532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2607552,0.0001453874,0.7341455,0.0001876562,0.00007081556,0.00006845689,0.0005199053,0.001999862,0.002107284],"genre_scores_gemma":[0.5237356,0.0003456579,0.4716128,0.00009655541,0.00005548149,0.00007425546,0.001665348,0.0003325123,0.002081731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002911594,"threshold_uncertainty_score":0.00578922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04137370471721077,"score_gpt":0.2500177250218521,"score_spread":0.2086440203046413,"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."}}