{"id":"W3035553015","doi":"10.3997/2214-4609.2019x604042","title":"Velocity Estimation Below the Well Bottom by using FWI: Application to Walkaway Synthetic Seismic Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"Inversion (geology); Geology; Borehole; Synthetic data; Robustness (evolution); Overpressure; Computer science; Vertical seismic profile; Seismic inversion; Algorithm; Seismology; Geotechnical engineering; Azimuth; Mathematics","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":[0.0005163249,0.00045886,0.0002794831,0.0006046725,0.0001504689,0.0004145117,0.000425581,0.0005485025,0.0004170189],"category_scores_gemma":[0.001887071,0.000205836,0.0003197308,0.0004804911,0.000332351,0.0003987514,0.000375117,0.0003303744,0.00009498634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001678058,"about_ca_system_score_gemma":0.0002599675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004402355,"about_ca_topic_score_gemma":0.003759474,"domain_scores_codex":[0.9998471,0.00004199538,0.00001263194,0.00003249025,0.00004524103,0.0000204994],"domain_scores_gemma":[0.9991556,0.0004047539,0.00008623507,0.0001277518,0.0001659905,0.00005957122],"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":[0.0005154319,0.0004354182,0.03557019,0.0002080747,0.0001644894,0.0009872161,0.0004126101,0.7480184,0.09339384,0.001381803,0.0007792649,0.1181332],"study_design_scores_gemma":[0.0000239035,0.00008658046,0.01008355,0.000005936709,0.00001349553,0.00007303422,0.00006602883,0.9751313,0.01383264,0.0003960049,0.0002605799,0.00002698602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556596,0.00004827409,0.04285286,0.000108597,0.00001891099,0.00002535401,0.0003697474,0.0004212942,0.0004953243],"genre_scores_gemma":[0.9743074,0.00003336098,0.02490346,0.00001043188,0.000005927247,0.00001047212,0.0005480204,0.00002774824,0.0001532128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004402355,"threshold_uncertainty_score":0.008753479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657470033300583,"score_gpt":0.2385258480655524,"score_spread":0.2219511477325465,"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."}}