Heterogeneous Seismic Waves Pattern Recognition in Oil Exploration with Spectrum Imaging

TL;DR

This paper introduces a spectrum imaging-based classification model for seismic data in oil exploration, achieving high accuracy and improving data processing efficiency through transfer learning and fine-tuning techniques.

Contribution

The study presents a novel seismic data classification method using Mel-spectrum and transfer learning, enhancing accuracy and efficiency over traditional Fourier-based approaches.

Findings

Achieved 98.32% classification accuracy.

Improved training efficiency with transfer learning and fine-tuning.

Enhanced seismic data quality control in oil exploration.

Abstract

The use of seismic waves to explore the subsurface underlying the ground is a widely used method in the oil industry, since different kinds of the rocks and mediums have different reflection rate of the seismic waves, so the amplitude of the reflected waves can unraveling the geological structure and lithologic character of a certain area under the ground, but the management and processing of seismic wave data often affects the efficiency of oil exploration and development. Different kinds of the seismic data bulk are always mixed and hard to be classified manually. This paper presents a classification model for four main types of seismic data, and proposed a classification method based on Mel-spectrum. An accuracy of 98.32% was achieved using pre-trained ResNet34 with transfer learning method. The accuracy is further improved compared with the pure fourier transformation method widely used in previous studies. Meanwhile, the transfer learning method and fine-tune strategy to train the neural network by training the first N-1 layers of the network separately and then train the fully connected layers further improves the training efficiency. Our model can also be seen as an efficient data quality control scheme for oil exploration and development. Meanwhile, our method is future-proofed, for further improvement of the seismic data processing quality control system, according to the spectrum characteristics, this model can be further extended into a error data classification model, reduces the workload of the bulk data management.