The acoustic emission (AE) Kaiser effect method is widely used for in-situ stress measurements because of its nondestructive nature, operational efficiency, and low cost. A key step in this method is the identification of the Kaiser point. However, traditional manual approaches require further improvement, indicating the importance of developing intelligent identification methods. In this study, an intelligent Kaiser point identification method was proposed based on a dual-branch gated recurrent unit (GRU) deep learning framework and phase-space reconstruction (PSR). In the proposed framework, AE waveform data was processed via PSR and principal component analysis to generate chaotic feature representations, which are then fused with the original waveform data through a dual-branch GRU architecture for classification. The classification results were then used for Kaiser point identification. The proposed model achieved an accuracy of 90.7% and an area under the curve of 0.9357 on the test set, indicating good discrimination ability between the Felicity and Kaiser areas. Compared with representative deep learning baseline models, including long short-term memory (LSTM), bidirectional LSTM, temporal convolutional network, and Transformer, the proposed model exhibited superior overall performance. The ablation results confirmed the positive contributions of both the original waveform and chaotic feature branches. The interpretability analysis revealed that the model relied primarily on a limited number of salient waveform segments and chaotic feature groups, both of which were physically relevant. Compared with the traditional manual method, the proposed method identifies the Kaiser point more stably and accurately. The application of the method to a porphyry copper mine in western China demonstrates its practicability in engineering applications.
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