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Table 1 MLE of the models and values of the AIC/2 for respective datasets

From: Statistical monitoring of aftershock sequences: a case study of the 2015 Mw7.8 Gorkha, Nepal, earthquake

  Data Models Mc Tstart Tend AIC/2 μ K c α p
(a) PDE ETAS 4.5 0.0417 17.0 −64.06 0.00 4.12 0.0571 2.08 1.59
PDE O–U 4.5 0.0417 17.0 −61.56 0.00 12.18 0.2870 1.95
PDE ETAS 4.5 0.0417 1.7 −73.77 0.00 7.50 0.0091 2.24 1.00
PDE O–U 4.5 0.0417 1.7 −74.47 0.00 11.16 0.0000 0.81
PDE Poisson 4.5 1.7 17.0 8.89 0.20
(b) ANSS ETAS 4.5 0.01 17.00 −142.58 0.00 5.95 0.0369 2.62 1.44
ANSS O–U 4.5 0.01 17.00 −141.38 0.00 7.74 0.0488 1.44
ANSS ETAS 4.5 0.01 1.70 −154.92 0.00 9.59 0.0055 2.85 1.00
ANSS O–U 4.5 0.01 1.70 −154.77 0.00 10.80 0.0067 1.00
ANSS ETAS 4.5 1.7 17.00 12.16 0.00 2.61 0.0000 Large 1.00
ANSS O–U 4.5 1.7 17.00 11.06 0.00 2.61 0.0000 1.00
ANSS ETAS 4.4 0.0417 17.00 −145.22 0.00 7.15 0.0595 2.38 1.63
ANSS O–U 4.4 0.0417 17.00 −142.29 0.00 11.77 0.1130 1.68
ANSS ETAS 4.4 0.0417 1.70 −156.86 0.00 12.62 0.0060 2.63 1.00
ANSS O–U 4.4 0.0417 1.70 −156.11 0.00 14.33 0.0004 1.00
ANSS ETAS 4.4 1.7 17.00 11.06 0.00 2.61 0.0000 Large 1.00
ANSS O–U 4.4 1.7 17.00 11.16 0.00 2.61 0.0000 1.00
(c) ANSS ETAS 4.2 0.0417 17.00 −221.34 0.00 11.14 0.0207 2.18 1.26
ANSS ETAS 4.2 0.0417 1.50 −228.12 0.00 10.47 0.0420 2.16 1.42
ANSS O–U 4.2 1.5 17.00 7.66 0.00 10.71 0.0000 1.00
ANSS ETAS 4.2 0.0417 1.70 −232.94 0.00 11.62 0.0344 2.20 1.33
ANSS O–U 4.2 1.7 17.00 11.96 0.00 9.99 1.00
ANSS ETAS 4.2 0.0417 2.00 −234.82 0.00 11.21 0.0381 2.18 1.36
ANSS O–U 4.2 2.0 17.00 13.78 0.00 9.81 1.00
ANSS ETAS 4.2 0.0417 3.00 −236.01 0.00 10.91 0.0419 2.19 1.40
ANSS O–U 4.2 3.0 17.00 15.16 0.00 9.80 1.00
(d) ANSS ETAS 4.2 0.0417 164.72 −294.23 0.00 12.64 0.0123 2.08 1.19
ANSS ETAS 4.2 0.0417 17.04 −221.34 0.00 11.14 0.0207 2.18 1.26
ANSS ETAS 4.2 17.04 164.72 −75.56 0.00 69.32 0.0541 4.43 1.41
  1. ‘Tstart’ and ‘Tend’ indicate the range of target intervals of respective datasets. ‘O–U’ is an abbreviation for the Omori–Utsu model. Blocks (a)–(d) provide the fitted results of the models for the same datasets but different setups, as cited in the text