On 6 February 2023, a devastating earthquake doublet consisting of Mw 7.8 and 7.6 events separated by about 9 hr struck the southeastern part of Türkiye. The developing aftershock sequence contained thousands of events during the first few days and overwhelmed the routine algorithms handling their detection and location. In addition, several stations temporarily lost real‐time contact and came online again later. At the same time the Omori decay of the aftershock event rate reduced the event frequency and allowed for inclusion of progressively smaller‐magnitude events with time. One possibility to help deal with such a complex situation is the use of machine learning (ML) methods to generate earthquake catalogs with a substantially higher number of events. Here, we present high‐resolution earthquake catalogs derived with two ML association methods for the first five days of the aftershock sequence of this doublet. In terms of the number of reliably located events, the event catalog created from PhaseNet picks and the GENIE phase association method outperforms both the routine regional catalog and the second ML‐derived catalog obtained from the GaMMA phase association method. Although both GaMMA and GENIE catalogs detect about 6 times more events than the routine catalog, GENIE associates on average about double the phases to a single event than GaMMA, which results in better constrained event locations. The spatiotemporal evolution of the event rates is sensitive to changes in the network geometry due to variable station availability. During the first few days, no decay of the event rate in the enhanced catalog is observed due to the inclusion of progressively smaller‐magnitude events with time and increased station availability. This study indicates that ML‐derived earthquake catalogs for challenging time periods like the early aftershock sequences of large earthquakes have the potential to significantly improve routine event catalogs.

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