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We catalog the 443 bright supernovae discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) in $2018-2020$ along with the 519 supernovae recovered by ASAS-SN and 516 additional $m_{peak}\leq18$ mag supernovae missed by ASAS-SN. Our statistical analysis focuses primarily on the 984 supernovae discovered or recovered in ASAS-SN $g$-band observations. The complete sample of 2427 ASAS-SN supernovae includes earlier $V$-band samples and unrecovered supernovae. For each supernova, we identify the host galaxy, its UV to mid-IR photometry, and the offset of the supernova from the center of the host. Updated light curves, redshifts, classifications, and host galaxy identifications supersede earlier results. With the increase of the limiting magnitude to $g\leq18$ mag, the ASAS-SN sample is roughly complete up to $m_{peak}=16.7$ mag and is $90\%$ complete for $m_{peak}\leq17.0$ mag. This is an increase from the $V$-band sample where it was roughly complete up to $m_{peak}=16.2$ mag and $70\%$ complete for $m_{peak}\leq17.0$ mag., Comment: 14 pages, 7 figures, 4 tables. Updated to reflect changes made in the published version. Tables containing the catalog data presented in this submission are included in machine-readable format as ancillary files

The band-limited vertical resolution of conventional 3D seismic measurements for reservoir characterization has an impact on our ability to accurately model thin reservoirs for volumetric computations. Stochastic seismic inversion addresses this concern by producing multiple, equally likely realizations, consistent with the available well and seismic data, at the fine-scale vertical resolution required for such reservoirs. The nature of the algorithm results in a large number of realizations (typically in excess of 200). We, therefore, require a methodology to rank the realizations in a way that is meaningful for the problem at hand and identify models corresponding to the P10th, P50th, and P90th percentiles. In the example presented here a feature recognized on a 3D deterministic seismic inversion result was interpreted as a mitten-shaped tidal bar using well-log data. The stochastic seismic inversion process generated realizations that showed a wide variation in the extent and geometry of the tidal bar. In this work we present an innovative ranking method used to classify the broad range of stochastic inversion results targeted at approximating this tidal bar geomorphological feature. From these results we were successful in identifying the various percentile models required for further analysis including input to reservoir simulation modeling.