Understanding the underlying structure of data from volcano monitoring is essential to identify precursors to changes in eruptive activity and to comprehend volcanic processes. However, effective analysis of longer-term trends in these signals is challenging as volcanic data are not necessarily statistically stationary or linear, particularly those from lava dome-forming volcanoes, which are commonly characterised by pulsatory eruptive activity. Here, we use detrended fluctuation analysis (DFA), a statistical technique previously applied to nonstationary data, to identify long-range (slowly decaying, e.g. power-law) correlations in a number of time-series of volcano seismicity recorded during the recent dome-forming eruptions of Volcán de Colima, Mexico, and Soufrière Hills Volcano, Montserrat. For all the time-series analysed, correlation strength varies through time and/or on different timescales; in some cases, this variation is periodic, seasonal, and/or related to activity. These results may provide new insights into eruptive processes and possibly further constrain the generation mechanisms of a number of the volcano-seismic event classes analysed. Furthermore, the correlation properties of real-time seismic measurements are shown (retrospectively) to contain information valuable to real-time volcano monitoring that is not identifiable by conventional analysis techniques. This study therefore demonstrates that long-range correlation analysis may be useful for extracting additional information from monitoring data at dome-forming or similar volcanoes. © 2013 Elsevier B.V.
