Regional level forecasting of seismic energy release

Abstract

This article explores a new strategy for forecasting of earthquake energy release in the seismogenic zones of the world. A total of 41 active seismogenic zones are identified with the help of past seismicity data. The magnitudes of individual events occurred in each zone are converted into seismic energy using an empirical relation. The annual earthquake energy time series is constructed by adding the energy releases of all the events in a particular year. The technique of principal component analysis is employed for the regionalization of these seismogenic zones using seismic energy time series. The annual energy time series of seismogenic zones are decomposed into finite number of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition technique. The periodicities of the IMFs and their contribution to the total variance of the earthquake energy release are examined. The artificial neural network technique is used for modeling and forecasting the energy-time series of seismogenic zones. The model is verified with an independent subset of data and validated using statistical parameters. The forecast of the annual earthquake energy release in each seismogenic zone is provided for the year 2015.

Comment on the paper by Kavitha and Raghukanth, “Regional level forecasting of seismic energy release”

“…However, for several representative case studies their predictions not only are significantly smaller than the observations but also have unreasonable uncertainty. This commentary discusses some of the problems associated with the earthquake data selection for the input of modeling, which may improve the accuracy of the earthquake energy prediction.Keywords Earthquake forecasting Á Seismic hazard assessment Á Seismogenic zone Kavitha and Raghukanth (2015) proposed a time-independent algorithm to forecast the earthquake energy release for the global seismogenic zones on the basis of the global seismicity catalog and the plate boundary model. Such an algorithm seems be useful to determine the most likely moment magnitude and assess the seismic hazard for a specific region.…”

Reply to Wu “Comment on Regional level Forecasting of Seismic energy release by Kavitha and Raghukanth”

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Abstract Kavitha and Raghukanth (doi:10.1007/s40328-015-0131-7, 2015) have developed an algorithm to forecast earthquake energy for a given seismogenic zone. The forecasting strategy is based on empirical mode decomposition and nonlinear regression analysis.

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“…Wu (Acta Geod Geophys 2015) has raised concern about the uncertainties and the input seismicity data used to develop the model. This article discusses the problems associated with the modelling of the seismic energy at regional level.Keywords Earthquake forecasting Á Seismic energy release Á Tectonic zones Kavitha and Raghukanth (2015) have compiled a global earthquake catalogue from various sources in literature and used a well defined procedure to forecast earthquake energy in 41 tectonic regions. The seismic energy time series is first decomposed into two statistically uncorrelated models using the empirical mode decomposition (EMD) technique of Huang et al (1998).…”