Magnetic transfer function entropy and the 2009 <i>M</i><sub>w</sub> = 6.3 L'Aquila earthquake (Central Italy)

Abstract

Abstract. With the aim of obtaining a deeper knowledge of the physical phenomena associated with the 2009 L'Aquila (Central Italy) seismic sequence, culminating with a M w = 6.3 earthquake on 6 April 2009, and possibly of identifying some kind of earthquake-related magnetic or geoelectric anomaly, we analyse the geomagnetic field components measured at the magnetic observatory of L'Aquila and their variations in time. In particular, trends of magnetic transfer functions in the years 2006-2010 are inspected. They are calculated from the horizontal to vertical magnetic component ratio in the frequency domain, and are very sensitive to deep and lateral geoelectric characteristics of the measurement site. Entropy analysis, carried out from the transfer functions with the so called transfer function entropy, points out clear temporal burst regimes of a few distinct harmonics preceding the main shock of the seismic sequence. A possible explanation is that they could be related to deep fluid migrations and/or to variations in the micro-/meso-fracturing that affected significantly the conductivity (ordered/disordered) distribution in a large lithospheric volume under the seismogenic layer below L'Aquila area. This interpretation is also supported by the analysis of hypocentres depths before the main shock occurrence.

Geospace perturbations induced by the Earth: The state of the art and future trends

“…More recently, Cianchini et al (2012) have analysed the components of the geomagnetic field variations, measured at the ground based magnetic observatory of L'Aquila (Central Italy), introducing the Transfer Function Entropy. This approach allowed these authors to detect specific anomalous periods in the magnetic data: this analysis pointed out clear temporal burst regimes of a few distinct harmonics corresponding to lower crust skin depths and preceding the main shock of the seismic sequence.…”

Foreshocks and Short-Term Hazard Assessment to Large Earthquakes using Complex Networks: the Case of the 2009 L'Aquila Earthquake

“…Here, we propagated the sliding window using two alternatives: either by a constant time period (here, 1 day) or by constant number of seismic events (here set to 10 events). Within each sliding window, we measure the following properties of the underlying directed network: the small-world index (SW), the average clustering coefficient (ACC), the betweenness centrality (BC) and the mean degree of the underlying degree distribution (Newman, 2003;Albert and Barabasi, 2002;Costa et al, 2007;Fagiolo, 2007; see Appendix A for mathematical definitions). The clustering coefficient is the ratio between all directed triangles actually formed by node i and the number of all possible triangles that node i could form (Fagiolo, 2007) and that consequently by averaging the ACC measures the cliquishness (structure) of the network (Watts and Strogatz, 1998).…”

“…Within each sliding window, we computed the following statistical properties of the emerged networks (Watts and Strogatz, 1998;Newman, 2003;Albert and Barabasi, 2002;Costa et al, 2007;Fagiolo, 2007). a.…”

Geosphere Coupling and Hydrothermal Anomalies before the 2009 Mw 6.3 L'Aquila Earthquake in Italy

“…The coversphere performs the vital functions of producing observable signals and enlarging or reducing the transmission of electric, magnetic, electromagnetic, and thermal signals from the lithosphere to the atmosphere, and even to satellite sensors. However, the existence of many diagnostic precursors -such as crustal strain, seismic velocity, hydrological change, gas emission, and electromagnetic signals -and their usefulness for earthquake forecasting is still controversial (Cicerone et al, 2009;Jordan et al, 2011). With the abundant data provided by Global Earth Observation System of System (GEOSS), multiple parameters from the integrated Earth observation should be encouraged to test for earthquake anomaly recognition and advance knowledge of precursor signals.…”