An ANN-based identification of geological features using multi-attributes: a case study from Krishna-Godavari basin, India

Abstract

Artificial neural network (ANN)–based workflow has been applied to the multi-channel seismic reflection data from the Krishna-Godavari (K-G) basin on the Eastern continental margin of India for delineation of prominent structural features such as chimneys and faults. To eliminate the noise and enhance the data quality, the seismic data is conditioned and filtered by data-conditioning techniques such as dip-steered and structural filters. A single attribute is not sufficient for the delineation of structures such as faults and chimneys; hence, an amalgamation of multiple attributes is necessary for the enhancement of laterally continuous seismic events. Hence, multi-attributes such as dip variance, curvature, coherency, energy, similarity, instantaneous frequency, and frequency washout ratio are extracted from the conditioned data which were combined and fed as inputs for the ANN for the chimney and fault detection. The neural network is trained at the identified chimney/non-chimney and fault/non-fault locations, which generates a final output, namely chimney probability attribute and a fault probability attribute, revealing an improved visibility of faults and chimneys in the seismic data. This multi-attribute approach shows more reliability in comparison with individual attribute responses, which helps in better structural interpretation. The study presents a workflow for better visualization of the subsurface structural features which thereby helps in detailed structural interpretation of the seismic data.

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Acknowledgements

The authors thank the Editor and the anonymous reviewer for their kind suggestions in improving the manuscript. Director, CSIR-National Geophysical Research Institute is acknowledged for providing data of the K-G basin. Director, Wadia Institute of Himalayan Geology, Dehradun is thanked for according permission to publish this work. Special thanks to dGB Earth Sciences for providing the OpendTect software license for academic use at the Centre for Earth Ocean and Atmospheric Sciences (CEOAS), University of Hyderabad, India. The authors are thankful to the Head, CEOAS, for providing the facilities for carrying out this work.

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Correspondence to Sri Lakshmi Sunkara.

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Responsible Editor: Narasimman Sundararajan

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Ramu, C., Sunkara, S.L., Ramu, R. et al. An ANN-based identification of geological features using multi-attributes: a case study from Krishna-Godavari basin, India. Arab J Geosci 14, 299 (2021). https://doi.org/10.1007/s12517-021-06652-z

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Keywords

  • Artificial neural networks
  • Seismic attributes
  • Faults
  • Gas chimney
  • Structural features