Sensitivity of Time-Dependent Earthquake Conditional Probabilities to Catalogue Declustering in the Himalayas

Earthquake catalogue declustering is a critical preprocessing step in time-dependent seismicity analyses (Gardner and Knopoff, 1974; Reasenberg, 1985), yet its systematic influence on conditional earthquake probability estimates remains insufficiently quantified, particularly in tectonically complex continental collision zones such as the Himalayas (Bungum et al., 2017). Renewal-based recurrence models typically assume that declustered catalogues isolate tectonically driven mainshock recurrence by removing dependent events. However, recent advances in declustering theory demonstrate that methodological choices, ranging from fixed spatio-temporal windows to adaptive and stochastic approaches, can substantially modify inter-event time statistics and inferred recurrence memory (Zaliapin et al., 2008; Zaliapin & Ben-Zion, 2020; Teng & Baker, 2019). Despite these developments, the implications of declustering-induced variability for time-dependent conditional probabilities remain underexplored in active orogenic belts.

In this study, we explicitly quantify how alternative declustering strategies influence time-dependent recurrence behavior and conditional rupture probabilities across selected Himalayan seismic source zones. Inter-event time series were constructed for moderate-to-large earthquakes (M ≥ 4.0) using both raw (non-declustered) and declustered catalogues derived from regional earthquake compilations. Declustering was performed using commonly applied fixed-window and adaptive approaches to capture epistemic variability associated with catalogue preprocessing. The resulting inter-event times were analyzed within renewal process models, including Brownian Passage Time (BPT), Lognormal, Weibull, and Gamma distributions, to estimate conditional probabilities as functions of elapsed time since the most recent major event.

Results show that declustered catalogues consistently yield smoother initial probability gradients and delayed probability peaks relative to raw catalogues, reflecting reduced short-term temporal clustering in inter-event time distributions. These shifts correspond to systematic changes in inferred renewal memory parameters, with declustering suppressing short-term contagion effects while largely preserving long-term mean recurrence intervals. In the Himalayas, collision-driven aftershock swarms and spatially heterogeneous fault interactions amplify these effects, introducing substantial epistemic uncertainty in early-time conditional probabilities, which can locally exceed factors of two to three depending on the declustering strategy employed. In contrast, long-term probability remains comparatively robust across declustering scenarios, consistent with steady-state tectonic strain accumulation.

These findings identify catalogue declustering as a dominant and often underappreciated source of uncertainty in time-dependent seismic probability modelling, reinforcing recent calls for ensemble-based and transparent pre-processing strategies in probabilistic seismic hazard workflows. This study advances a methodological framework for interpreting renewal-based conditional probabilities in clustered tectonic regimes. The Himalayas emerge as a natural laboratory where combined raw and declustered analyses can yield more resilient probabilistic interpretations.