# A new method of rock type identification based on transformer by utilizing acoustic emission

## TL;DR

This paper introduces a new deep learning model called 3CTNet that accurately identifies rock types using acoustic emission signals, achieving over 98% accuracy.

## Contribution

The novel 3CTNet model combines CNNs and Transformers for intelligent rock type identification from acoustic emission data.

## Key findings

- 3CTNet achieves 98.780% overall identification accuracy for rock types.
- The model uses ADASYN oversampling to enhance robustness and generalization.
- 3CTNet outperforms existing time series processing models in rock type identification.

## Abstract

The characterization and analysis of rock types based on acoustic emission (AE) signals have long been focal points in earth science research. However, traditional analysis methods struggle to handle the influx of big data. While signal processing methods combined with deep learning have found widespread use in various process analyses and state identification, effective feature extraction using progressive fusion technology still faces challenges in the field of intelligent rock type identification. To address this issue, our study proposes a novel framework for rock type identification based on AE and introduces a new signal identification model called 3CTNet. This model integrates convolutional neural networks (CNNs) and Transformer encoder, intelligently identifying AE of different rock fractures by establishing dependencies between adjacent positions within the data and gradually extracting advanced features. Furthermore, we experimentally compare five oversampling methods, ultimately selecting the adaptive synthetic sampling method (ADASYN) to balance the dataset and enhance the model’s robustness and generalization ability. Comparison of the internal structure of our model with a series of time series processing models demonstrates the effectiveness of the proposed model structure. Experimental results showcase the high identification accuracy of the intelligent rock type identification model based on 3CTNet, with an overall identification accuracy reaching 98.780%. Our proposed method lays a solid foundation for the efficient and accurate identification of formation rock types in geological exploration and oil and gas development endeavors.