Local geodetic and seismic energy balance for shallow earthquake prediction
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Earthquake analysis for prediction purposes is a delicate and still open problem largely debated among scientists. In this work, we want to show that a successful time-predictable model is possible if based on large instrumental data from dense monitoring networks. To this aim, we propose a new simple data-driven and quantitative methodology which takes into account the accumulated geodetic strain and the seismically-released strain to calculate a balance of energies. The proposed index quantifies the state of energy of the selected area and allows us to evaluate better the ingoing potential seismic risk, giving a new tool to read recurrence of small-scale and shallow earthquakes. In spite of its intrinsic simple formulation, the application of the methodology has been successfully simulated in the Eastern flank of Mt. Etna (Italy) by tuning it in the period 2007–2011 and testing it in the period 2012–2013, allowing us to predict, within days, the earthquakes with highest magnitude.
KeywordsEarthquake prediction Strain analysis Time-predictable model
GPS and Analisi Dati Sismici groups of Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, is kindly acknowledged for providing the EQ database (INGV, Catania (http://www.ct.ingv.it/ufs/analisti/catalogolist.php)). Our research was partially supported by funds of the agreement V3 between INGV and Italian Department of Civil Protection (DPC); we thank R. Azzaro and S. D’Amico for this support. FC would like to thank Marco Aloisi, Andrea Cannata, Pablo Gonzalez, Asta Miklius, and Mike Poland for their useful suggestions. We thank S. Ciancitto for correcting and improving the English of this manuscript. We would like to express our gratitude to the Editor M. Garcia-Fernandez, to Prof. V.G. Kossobokov, and to the anonymous referees who helped us with their comment to improve the manuscript.
- Azzaro R, Branca S, Gwinner K, Coltelli M (2012) The volcano-tectonic map of Etna volcano, 1: 100.000 scale: an integrated approach based on a morphotectonic analysis from high-resolution DEM constrained by geologic, active faulting and seismotectonic data. Ital J Geosci 131(1):153–170Google Scholar
- Bak P (1997) How nature works. Oxford University Press, UKGoogle Scholar
- Gutenberg B, Richter CF (1956) Magnitude and energy of earthquakes. Ann Geofis 9:1–15Google Scholar
- Herring TA (2004) GLOBK: Global Kalman filter VLBI and GPS analysis program, version 10.2, user’s manual. Mass. Inst. of Technol, CambridgeGoogle Scholar
- King RW, Bock Y (2004) Documentation of the MIT GPS Analysis Software: GAMIT, release 10.2, user’s manual. Mass. Inst. of Technol, CambridgeGoogle Scholar
- Palano M, Rossi M, Cannavò F, Bruno V, Aloisi M, Pellegrino D, Pulvirenti M, Siligato G, Mattia M (2010) Etn@ref: a geodetic reference frame for Mt. Etna GPS networks. Ann Geophys 53(4):49–57Google Scholar
- Reid H (1910) The mechanics of the earthquake: the California earth-quake of April 18, 1906, report, vol 2, 192 pp., State Earthquake Invest. Comm.. Carnegie Inst. of Wash, WashingtonGoogle Scholar
- Savage JC, Simpson RW (1997) Surface strain accumulation and the seismic moment tensor. Bull Seismol Soc Am 87:1345–1353Google Scholar
- Wells DL, Coppersmith KJ (1994) New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement. Bull Seismol Soc Am 84:974–1002Google Scholar
- Zavyalov AD (2005) Medium-term prediction of earthquakes from a set of criteria: principles, methods, and implementation. Russ J Earth Sci, 7(1)Google Scholar