Integrating multiple seismic attributes for fault detection using a new hybrid machine learning.

ABSTRACT: This paper proposes a novel hybrid approach for fault detection in seismic data by integrating multiple seismic features using a novel hybrid machine. This hybrid method introduces the integration of Multilayer Perceptron (MLP) neural networks with Support Vector Machines (SVM). The main objective of this study is to enhance the analysis and recognition of fault patterns in seismic data while reducing detection errors. The dataset consists of two-dimensional synthetic and real seismic data and their corresponding labels. Various seismic features, such as the gray-level co-occurrence matrix (GLCM), and features derived from ant tracking, chaos, variance, sweetness, correlation, slope direction, and energy, are extracted and normalized. These features are subsequently used to train MLP and S

SUBMITTER: Esmaeili H 

PROVIDER: S-EPMC12663330 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature