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.

Keywords: Automated seismic interpretation, Machine learning, Geological features, Faults, Stratigraphic horizons