## 1 Introduction
In this method, the ratio of continuously calculated amplitude (or envelope or average energy) of a recorded signal realisation in two consecutive moving time windows--a short-term window and a subsequent long-term window--is calculated and used as a criterion for signal detecting [20; 21; 25]. Despite the simplicity, this method also requires the careful data-driven setting of parameters, including a trigger threshold level and lengths of both STA and LTA windows [26]. Similarly to the amplitude threshold, the too low trigger setting can give rise to many false alarms while the high threshold may result in missing low amplitude events. The value of the optimal activation threshold depends on the total noise level, its nonstationarity and the presence of spurious noise. Many modern time-domain approaches rely on statistical procedures including a group of autoregressive methods (AR) and cross-correlation methods [27; 28], the CUSUM (cumulative sum) algorithm--a simple yet efficient sequential analysis technique developed for change detection in time series [29], fractal dimension-based algorithms [30; 31], higher-order statistics [32], e.g., those using the short-term kurtosis to long-term kurtosis ratio [33] or its improved version employing the discrete wavelet representation [34]. Besides, the Akaike Information Criterion (AIC) [35] should be highlighted as an effective statistical procedure devised to determine the transition point in a time series between noise and a coherent signal (see [36] for the efficient implementation of the AIC for p-phase picking). This criterion has become a core component in a family of hybrid methods combining benefits of different techniques, e.g., AR-AIC picker [37; 38; 39], or empirical mode decomposition EMD-AIC picker [40; 41]. Finally, a group of methods exploiting artificial intelligence in the form of neural networks [42; 43] should be mentioned as an emerging trend in contemporary signal detection arena. These methods have proved to be useful in many situations, but require the adequate training of the network, which is not always possible or is resource-consuming. The use of multiple promising spectral wavelet-based algorithms, in both continuous (CWT) and discrete (DWT) forms, for signal picking has been reviewed in [19; 44] with a focus on AE applications for signal characterisation [45], denoising [46; 47; 48], phase picking and source location [38; 45; 49; 50; 51; 52; 53]. A simple adaptive picking method based on the detection of abrupt changes in the signal energy was proposed in [54]. The sliding power spectral density (PSD) method was employed to separate noise and signal containing realisations in the continuous seismic waveform [55]. It was particularly shown that the PSD-based method could outperform the STA/LTA technique by detecting a higher number of low amplitude events while keeping the number of false triggers reasonably small. Finishing this brief--and by no means all-inclusive--survey of currently available signal detection algorithms, we would like to highlight specific reviews by [PERSON] et al. [24] and [PERSON] et al. [56], who also conclude that despite the vast amount of research in this field, the event-picking algorithms have not yet fully come of age. Commonly and unfortunately, when background noise is high and the signal quality is poor, most techniques tend to fail or become very computationally intensive. Therefore, while a wealth of techniques have been devised for signal detection, the main challenge is still on hold, and in practice, only relatively simple trigger algorithms are broadly accepted. This motivated us to seek for a \"data-driven\", practical, and yet robust procedure providing consistent and reliable picks of low amplitude signal buried into background noise in automated schemes.