Heterogeneous Seismic Waves Pattern Recognition in Oil Exploration with Spectrum Imaging

project

Jan 2022

This paper presents a classification model for seismic data using Mel-spectrum analysis and pre-trained ResNet34 with transfer learning, achieving an accuracy of 98.32%. It introduces a more efficient approach for seismic wave pattern recognition in oil exploration compared to traditional Fourier transformation methods.

The paper discusses the challenges in managing and processing seismic wave data in oil exploration. It introduces a novel approach using Mel-spectrum analysis and deep learning (ResNet34) for the classification of seismic data. The study demonstrates significant improvements over traditional Fourier transformation methods, both in terms of accuracy and efficiency.

Seismic Waves Processing
Quality Control
Pattern Classification
Neural Network
Bulk Data Management

Paper

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