A neural-based (machine learning-based) practical approach for automatic interpretation of 3D seismic data, has been developed by the scientists from Wadia Institute of Himalayan Geology(WIHG), an autonomous Institute under the Department of Science & Technology(DST). This 1st of its kind approach was developed by computing a new attribute called meta-attribute.
Need of the Development
It aids the scientists who were struggling with the manual interpretation of growing seismic data(a time picture of subsurface structure) to explore causes of earthquakes, especially when the area is geologically complex.
Significance of Seismic data
To understand the geotectonic, basin evolution, resource exploration, and process that causes earthquakes (seismogenesis) of an area, an effective detection of subsurface geologic features from surface seismic data is very important.
i.The Scientists designed a workflow and computed Sill Cube (SC) and Fluid Cube (FC) meta-attributes.
ii.SC & FC meta attributes are hybrid attributes that are generated by combining a number of seismic attributes (associated with the geologic targets) using a neural-based approach.
iii.The team prepared the meta-attributes following supervised neural learning (machine learning), where computing systems are trained under the guidance of a human analyst.
It is to be noted that this work is a vital step in using machine learning to address geological problems and also encourages to understand complex geological processes in an active mountain belt such as the Himalaya.
The study has been published under the title, ‘Interpretation of magma transport through saucer sills in shallow sedimentary strata using an automated machine learning approach’ in the research journal ‘Tectonophysics’.
Author– Priyadarshi Chinmoy Kumar & Kalachand Sain from WIGH
Publication Link– https://doi.org/10.1016/j.tecto.2020.228541
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