Automated Seismic Interpretation: Machine Learning Technologies are Being Used to Develop Automated Seismic Interpretation to Identify Geological Features, Such as Faults and Stratigraphic Horizons

Abstract

This paper describes the use of machine learning technologies to create an
automated seismic interpretation capable of identifying geological features such
as fractures and stratigraphic horizons. Geologists use Automated Seismic
Interpretation (ASI) to extract geologic information from seismic data. Geologic
features can be identified through the amplitude, frequency, and polarization
parameters of seismic signals, and automated techniques can be used to identify
geologic features. This paper examines the present state of automated seismic
interpretation and the potential of machine learning technologies for this
endeavor. A review of the research indicates that machine learning techniques
can be used to accurately identify faults and stratigraphic horizons in seismic
data. The authors discuss the features that can be extracted by machine learning
algorithms and compare the various machine learning techniques applied to
seismic interpretation. The paper also discusses the difficulties associated with
automated seismic interpretation and the need for additional development to
improve the precision of seismic interpretation. Future research, according to
the authors, should concentrate on increasing the accuracy of fault and horizon
recognition and devising algorithms to detect other geological features. Overall,
the paper provides a summary of the current state of automated seismic
interpretation and the obstacles that must be overcome. In addition, it
demonstrates the capability of machine learning technologies to recognize faults
and stratigraphic horizons in seismic data. With additional research, the precision
of automated seismic interpretation can be enhanced, leading to more precise
geological interpretations and a deeper comprehension of the Earth’s subsurface.
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