High-resolution seismicity imaging and early aftershock migration of the 2023 Kahramanmara?(SE Türkiye)M_W7.9&7.8 earthquake doublet

We build a high-resolution early aftershock catalog for the 2023 SE Türkiye seismic sequence with PALM,a seamless workflow that sequentially performs phase picking,association,location,and matched filter for continuous data.The catalog contains 29,519 well-located events in the two mainshocks rupture region during 2023-02-01–2023-02-28,which significantly improves the detection completeness and relocation precision compared to the public routine catalog.Employing the new PALM catalog,we analyze the structure of the seismogenic fault system.We find that the Eastern Anatolian Fault(EAF)that generated the first M_(W)7.9 mainshock is overall near-vertical,whereas complexities are revealed in a small-scale,such as subparallel subfaults,unmapped branches,and stepovers.The seismicity on EAF is shallow(<15 km)and concentrated in depth distribution,indicating a clear lock-creep transition.In contrast,the SürgüFault(SF)that is responsible for the second M_(W)7.8 mainshock is shovel-shaped for the nucleation segment and has overall low dip angles(~40°–80°).Aftershocks on the SF distribute in a broad range of depth,extending down to~35 km.We also analyze the temporal behavior of seismicity,discovering no immediate foreshocks within~5 days preceding the first mainshock,and no seismic activity on the SF before the second mainshock.

A high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter

We present a high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi sequence.The catalog has a time range of 2021-05-01 to 2021-05-28,and contains~8,000 well located events.It captures the features of the whole foreshock sequence and the early aftershocks.We designed a detection strategy incorporating both an artificial intelligent(AI)picker and a matched filter algorithm.Here,we adopt a hybrid AI method incorporating convolutional and recurrent neural network(CNN&RNN)for event detection and phase picking respectively(i.e.CERP),a light-weight AI picker that can be trained with small volume of data.CERP is first trained with detections from a STA/LTA and Kurtosis-based method called PAL,and then construct a rather complete template set of~4,000 events.Finally,the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation,which finally results in precise relocation.This process gives 9,026 detections,among which 7,943 events can be well relocated.The catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity evolution.The main features are:(1)the foreshock sequence images simple fault geometry with consistent strike,but also show a variable event depth along strike;(2)the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast;(3)complex seismicity are developed in the post-seismic period,indicating complex triggering mechanisms.Thus,our catalog provides a reliable basis for further investigations,such as b-value studies,rupture process,and triggering relations.

A test on methods for MC estimation based on earthquake catalog

This study tested five methods widely used in estimating the complete magnitudes(M_C) of earthquake catalogs. Using catalogs of observed earthquake properties, we test the performance of these five algorithms under several challenging conditions, such as small volume of events and spatial-temporal heterogeneity, in order to see whether the algorithms are stable and in agreement with known data. We find that the maximum curvature method(MAXC) has perfect stability, but will significantly underestimate M_C unless heterogeneity is absent. M_C estimated by the b-value stability method(MBS) requires many events to reach a stable result. Results from the goodness of fit method(GFT) were unstable when heterogeneity lowered the fitness level. The entire magnitude range method(EMR) is relatively stable in most conditions, and can reflect the change in M_C when heterogeneity exists, but when the incomplete part of the earthquake catalog is dismissed, this method fails. The median-based analysis of the segment slope method(MBASS) can tolerate small sample size, but is incapable of reflecting the missing degree of small events in aftershock sequences. In conditions where M_C changes rapidly, such as in aftershock sequences, observing the time sequence directly can give a precise estimation of the complete sub-catalog, but only when the number of events available for study is large enough can the MAXC, GFT, and MBS methods give a similarly reliable estimation.