A network-based approach to earthquake pattern analysis

Abstract

This research proposes a new network-based method for the assessment of earthquake relationships in space-time-magnitude patterns. The method is applied to the study of volcanic seismicity in Hawaii, over a time period from January 1st, 1989 to December 31st, 2012. It is shown that networks with high values of the minimum edge weight W[subscript min] enjoy strong scaling properties, as opposed to networks with low values for W[subscript min], which exhibit poor or no such properties. The scaling behaviour along the spectrum of W[subscript min], in conjunction with the robustness regarding parameter variations, endorse the idea of a relationship between fundamental properties of seismicity and the scaling properties of the earthquake networks, and can be used to discern the interrelated earthquakes from the rest of the dataset. The scale free behaviour of the connectivity distribution along the spectrum of the minimum weight values is mirrored by a similar behaviour of the distribution of the number of nodes’ linked neighbours. The patterns found in the distributions of temporal and spatial intervals between earthquakes are similar in various networks, from large to small networks. Notable similarities are found between the variation of the network clustering coefficient, C, and the variation of the exponents of the connectivity distribution, [Beta], and of the weight distribution, [gamma] . Results of this method are further applied for the study of temporal changes in volcanic seismicity patterns. It is shown that [Beta], [gamma] , and C manifest a generally synchronous variation over successive temporal windows, which can be related to changes in seismicity and in the life of the volcanic system. A Zipf distribution is found for the ranked sets of magnitude values of successive network nodes. The distribution of differences between the magnitude values of successive nodes is also governed by a power law.