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Advances in Geo-Time Series Modelling

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Journal of the Geological Society of India

Abstract

Recent advances in geo-time series modelling are briefly presented. These progressive developments and imminent applications in the data-driven research have come across three main categories of investigation efforts (i) Classical to recent advances in spectral analyses and their applications on some very significant geophysical/geological time series. Specially, applications of modern multi-taper methods of spectral analysis (MTM) and singular spectral analysis (SSA) techniques based filtering are discussed. Applications on 3D seismic reflection data de-noising using multi-channel SSA along with significant results are demonstrated. (ii) Methods of nonlinear time series analyses and physical concept of fractal and chaos are enumerated and relevance of chaos in complex geophysical and geological time series modelling are discussed (iii) The upcoming field of research in Machine learning (ML) based artificial neural network (ANN) and Deep Learning (DL) along with pertinent applications on variety of geophysical data, such well-log, ground water, gravity, seismic/seismological data etc., are presented and discussed.

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Acknowledgements

We thank Director, CSIR-NGRI for his kind permission to submit the manuscript. Second author is thankful to CSIR for SRA fellowship. We are also thankful to Editor and Associate editors for their invitation to submit this article.

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    R. K. Tiwari & Rajesh Rekapalli

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Tiwari, R.K., Rekapalli, R. Advances in Geo-Time Series Modelling. J Geol Soc India 97, 1313–1322 (2021). https://doi.org/10.1007/s12594-021-1862-4

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