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Seismic Inversion Methods: A Practical Approach pp 177–216Cite as

Geostatistical Inversion

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Part of the book series: Springer Geophysics ((SPRINGERGEOPHYS))

Abstract

Geostatistical inversion methods are routinely used to predict various geophysical parameters away from the boreholes using seismic and well log data. The geostatistics derives a surface using the values from the measured locations to estimate data points for each location in between the data points. The present chapter discusses different types of seismic attributes and their use in the interpretation of seismic data. Thereafter, four types of geostatistical methods namely single attribute analysis, multi-attribute regression, probabilistic neural network, and multi-layer feed-forward neural network methods are discussed. Initially, the mathematical background of these methods has been discussed and finally, the application of these methods to the real data is provided for better understanding.

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Authors and Affiliations

  1. Department of Geophysics, Institute of Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    S. P. Maurya & N. P. Singh

  2. Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    K. H. Singh

Authors
  1. S. P. Maurya
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  2. N. P. Singh
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  3. K. H. Singh
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Correspondence to S. P. Maurya .

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Maurya, S.P., Singh, N.P., Singh, K.H. (2020). Geostatistical Inversion. In: Seismic Inversion Methods: A Practical Approach. Springer Geophysics. Springer, Cham. https://doi.org/10.1007/978-3-030-45662-7_7

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