Application of attention mechanism-based LSTM neural network in stratigraphy identification

Application of attention mechanism-based LSTM neural network in stratigraphy identification

This paper proposes a lithology recognition system based on a Long Short-Term Memory (LSTM) neural network, which incorporates multi-head masked attention mechanisms and the Transformer architecture. Initially, an equipment-formation coupling model is established based on contact mechanics and rock...

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Bibliographic Details
Main Authors: Cunde Jia, Xiangdong Kong, Minghui Wang, Zhuowei Yu, Haipeng Li, Yunhong Jiang, Jianliang Hu, Chao Ai
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Results in Engineering
Subjects:
Rotary drilling rig
Stratigraphy identification
Recurrent neural network
Long short term memory neural network
Multi-head mask attention
Transformer architecture
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025013374
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