Abstract
Seismic events such as earthquakes are one of the most important issues in the field of geology. Meanwhile, less attention has been paid to micro-seismic events, despite the high number of earthquakes. Earthquakes, regardless of their size, affect human life; therefore, their detection and management is considered an important issue. For this purpose, experts developed seismic arrays as a system of linked seismometers. These systems equipped with sensors and seismographs are able to receive a range of waves from the earth, which are then sent to the central seismic station for analysis. So far, many tools and methods have been devised to analyze seismic data. However, the dominant method in most seismic mechanisms is trigger function, based on STA/LTA (short-time-average through long-time-average trigger). These mechanisms have considerable threshold in terms of earthquake range, so many micro-events are ignored as noise. Generally, in this field of geology, computer science techniques have been used to detect and classify these events. Statistical methods such as kurtosis, variance, and skewness can be applied to understand the changes in the signal curves of geophones in a seismic event, thereby helping in the initial detection of fuzzy features. According to the last 3 years’ reports of global data mining agencies such as Rexer, KDnugget, and Gartner, Rapid Miner is one of the most popular tools for data mining in recent years. Furthermore, these institutions considered artificial neural networks, especially multilayer perceptron (MLP) and base radial function (RBF), to be among the most successful algorithms for detection and classification of stream data. In this research, the recorded data from several seismic experiments has been classified by a hybrid model. Hence, the present study was aimed to enhance the authenticity of data based on the application of effective variables. This was undertaken through use of a fuzzy method and an integrated neural network algorithm, involving MLP perceptron and radial network of RBF in the form of a collective learning system, in order to identify seismic events on a small scale. Based on the results, in comparison to basic methods, the proposed method significantly improved using the actual error and root-mean-square error (RMSE) criteria.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Back propagation
Quick propagation
Delta-bar-delta
Extended delta-bar-delta
References
Alam, F., Mehmood, R., Katiba, I., & Albeshri, A. (2016). Analysis of eight data mining algorithms for smarter internet of things (IoT). Procedia Computer Science, 98(2016), 437–442.
Annual Gartner report (2017). Gartner magic quadrants for data science platforms compared, 2017 vs 2016, https://www.gartner.com/technology/home.
Annual KDnugget report (2015). https://www.kdnuggets.com/.
Ebel, J. E., Chambers, D. W., Kafka, A. L., & Baglivo, J. A. (2007). Non-Poissonian earthquake clustering and the hidden Markov model as bases for earthquake forecasting in California. Seismological Research Letters, 78, 57–65.
Haykin, S. (1998). Neural networks: a comprehensive foundation (2 ed.), Prentice Hall, ISBN 0-13-273350-1.
Juan, I. S., Mauricio, D. S., & Danilo, R. V. (2015). Radon transform-based micro seismic event detection and signal-to-noise ratio enhancement. Journal of Applied Geophysics, 113, 51–63.
Katsumata, K. (2011). Long-term seismic quiescence started 23 years before the 2011 off the pacific coast of Tohoku earthquake (M=9.0). Earth, Planet and Space, 63, 709–712.
Kurban, T., & Beşdok, E. (2009). A comparison of rbf neural network training algorithms for inertial sensor based terrain classification, Geomatics Engineering, Engineering Faculty, Erciyes University, Turkey.
Morales-Esteban, A., Martínez-Álvarez, F., Troncoso, A., Justo, J. L., & Rubio-Escudero, C. (2010). Pattern recognition to forecast seismic time series. Expert Systems with Applications, 37(12), 8333–8342.
Phama, D. T., & Sagiroglu, S. (2001). Training multilayered perceptions for pattern recognition: a comparative study of four training algorithms. International Journal of Machine Tools & Manufacture, 41, 419–430.
Rosenblatt, F. X. (1961). Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Washington DC: Spartan Books.
Rumelhart, D. E., & McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1). Cambridge: MIT Press.
Rumelhart, D.E., Geoffrey E. H., & Williams, R.J. (1986). Learning internal representations by error propagation, MIT Press.
Sharma, B. K., Kumar, A., & Murthy, V. M. (2010). Evaluation of seismic events detection algorithms. Journal of the Geological Society of India, 75(3), 533–538.
Sorbi, M. R., Nilfouroushan, F., & Zamani, A. (2012). Seismicity patterns associated with the September 10, 2008 Qeshm earthquake, South Iran. International Journal of Earth Sciences, 101(8), 2215–2223.
Venkataraman, S. (2010). A grid-based neural network framework for multimodal biometrics world academy of science. Engineering and Technology, 2, 298–303.
Wyss, M., Pacchiani, F., Deschamps, A., & Patau, G. (2008). Mean magnitude variations of earthquakes as a function of depth: different crustal stress distribution depending on tectonic setting. Geophysical Research Letters, 35. https://doi.org/10.1029/2007GL031057.
Zamani, A., & Agh-Atabai, M. (2011). Multifractal analysis of the spatial distribution of earthquake epicenters in the Zagros and Alborz-Kopeh Dagh regions of Iran. Journal Science and Technology, A1, 39–51.
Zamani, M., Sorbi, R., & Safavi, A. A. (2013). Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Science Informatics, 6(2), 71–85.
Funding resources
Is not available.
Author information
Authors and Affiliations
Contributions
Saeed Ghorbani (corresponding author) designed the study, developed the methodology, carried out the tests, collected the data, performed the analysis, and wrote the manuscript.
Morteza Barari designed the proposed model structure and has offered the MLP and RBF algorithms (neural network).
Mojtaba Hoseini checked the performance correctness of tools and methods.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Rights and permissions
About this article
Cite this article
Ghorbani, S., Barari, M. & Hoseini, M. Presenting a new method to improve the detection of micro-seismic events. Environ Monit Assess 190, 464 (2018). https://doi.org/10.1007/s10661-018-6837-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-018-6837-6