<p>Due to the complexity and high dimensionality of seismic catalogues, the dimensional reduction of raw seismic data and the feature selections needed to decluster these catalogues into crisis and non-crisis events remain a challenge. To address this problem, we propose a two-level analysis.</p> <p> </p> <p>First, an unsupervised approach based on an artificial neural network called self-organising map (SOM) is applied. The SOM is a machine learning model that performs a non-linear mapping of large input spaces into a two-dimensional grid, which preserves the topological and metric relationships of the data. It therefore facilitates visualisation and interpretation of the results obtained. Then, agglomerative clustering is used to classify the different clusters obtained by the SOM method as containing background events, aftershocks and/or swarms. To estimate the classification uncertainty and confidence level of our declustering results, we developed a probabilistic function based on the feature representation learned by the SOM (spatiotemporal distances between events, magnitude variations and event density).</p> <p> </p> <p>We tested the two-level analysis on synthetic data and applied it to real data: three seismic catalogues (Corithn Rift, Taiwan and Central Italy) that differ in area size, tectonic regime, magnitude of completeness, duration and detection methods. We show that our unsupervised machine learning approach can accurately distinguish between crisis and non-crisis events without the need for preliminary assumptions and that it is applicable to catalogues of various sizes in time and space without threshold selection.</p>

Many applications in seismology require to isolate earthquake clusters from a background activity. Relative declustering methods essentially find a 2D representation of an earthquake catalogue that distinguishes between two classes of events: crisis and non-crisis events. However, the number of statistical and/or physical parameters to be used is often limited due to the difficulty of concatenating the information onto a physically meaningful 2D grid. In this study, we propose to alleviate the declustering task by using the ability of unsupervised artificial intelligence to model complex spatio-temporal relationships directly from data. Through a data-driven approach, we define an easily transferable declustering model that provides declustering results with fewer assumptions and no prior selection of thresholds. We first obtain this model by training a self-organising neural network (SOM) that learns to cluster data points according to their feature similarity on a 2D map. We then assign each SOM cluster a label (crisis or non-crisis class) using an agglomerative clustering procedure. We quantify the classification uncertainty by developing a probabilistic function based on the projection learned by SOM. Our method is applied to a synthetic dataset and to real catalogues from the Gulf of Corinth, Central Italy and Taiwan. We discuss the validity of the method by estimating its classification accuracy. For real data, we qualitatively compare our results to previous declustering attempts. We show that our approach is easy to handle, provides a fairly new representation of earthquake catalogues and has the potential to reduce classification ambiguities between nearby events.

Many applications in seismology require to isolate earthquake clusters from a background activity. Relative declustering methods essentially find a 2D representation of an earthquake catalogue that distinguishes between two classes of events: crisis and non-crisis events. However, the number of statistical and/or physical parameters to be used is often limited due to the difficulty of concatenating the information onto a physically meaningful 2D grid. In this study, we propose to alleviate the declustering task by using the ability of unsupervised artificial intelligence to model complex spatio-temporal relationships directly from data. Through a data-driven approach, we define an easily transferable declustering model that provides declustering results with fewer assumptions and no prior selection of thresholds. We first obtain this model by training a self-organising neural network (SOM) that learns to cluster data points according to their feature similarity on a 2D map. We then assign each SOM cluster a label (crisis or non-crisis class) using an agglomerative clustering procedure. We quantify the classification uncertainty by developing a probabilistic function based on the projection learned by SOM. Our method is applied to a synthetic dataset and to real catalogues from the Gulf of Corinth, Central Italy and Taiwan. We discuss the validity of the method by estimating its classification accuracy. For real data, we qualitatively compare our results to previous declustering attempts. We show that our approach is easy to handle, provides a fairly new representation of earthquake catalogues and has the potential to reduce classification ambiguities between nearby events.