Scientists struggling with the manual interpretation of growing seismic data to explore causes of earthquake, particularly when the area is geologically complex, are now armed with a machine learning-based solution that can help in automatic interpretation of this data.
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Key-Points
This first of its kind approach has been developed by computing a new attribute called meta-attribute.
Scientists from WIHG have designing a workflow and computing Sill Cube (SC) and Fluid Cube (FC) meta-attributes. These are hybrid attributes that are generated by amalgamating a number of seismic attributes using a neural-based approach.
The WIHG team prepared the meta-attributes following supervised neural learning (machine learning), where computing systems are trained under the guidance of a human analyst.
This work is an important step forward towards application of machine learning to address geological problems and looks promising in understanding complex geological processes in an active mountain belt such as the Himalaya.
