Gaussian process regression in seismic fault detection
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Gaussian process regression, as a nonparametric probabilistic model based on Bayesian statistics, is highly capable of supporting sparse features such as global anomalies. Detecting abnormal behavior from normal behavior makes Gaussian process regression as an edges detector where faults may occur in the seismic data. In this study, the Gaussian process regression-based anomaly detection was applied to both synthetic and real data containing normal fault to detect the fault edge. To identify the fault edges, the geological layers are considered as normal interaction and the fault edge as a global anomaly which disrupts the normal behavior of layers. The error of regression is analyzed to separate the fault edge. To evaluate the proposed method, it was applied on a series of synthetic seismic data and a real 2D seismic section of F3 block of the North Sea containing the fault. The results show the ability of this method in fault detection.
Keywords:
Language:
Persian
Published:
Journal of Petroleum Geomechanics, Volume:3 Issue: 2, 2019
Pages:
27 to 41
https://www.magiran.com/p2118021