Physics-Guided LLM Prompt Engineering for Distributed Acoustic Sensing Data Augmentation in Pipeline Intrusion Detection

Distributed acoustic sensing (DAS) is increasingly used for third-party intrusion (TPI) detection in oil and gas pipeline monitoring, but labeled DAS data are often scarce, leading to overfitting, poor generalization, and increased false alarms and missed detections. Conventional data augmentation, GAN-based synthesis, and transfer learning may generate physically implausible samples or fail to cover the event feature space. To address this, we propose a physics-guided large language model (LLM) prompt-engineering framework for DAS data augmentation and pipeline intrusion detection. The framework establishes a physically grounded feature-indicator framework for DAS disturbance-event classification by mapping primary event mechanisms to measurable signal indicators, and then uses a standardized four-module prompt template to guide LLM-based synthesis-script generation. A two-stage iterative verification procedure is further introduced to constrain the generated samples in terms of physical-mechanism compliance and feature-parameter consistency. Synthetic data are combined with real data to train a lightweight PatchTransformer model for TPI detection, while an additional CNN is used to assess cross-architecture applicability. Using the public DAS1K benchmark with five-fold stratified cross-validation and a univariate controlled experiment (0–800 synthetic samples per category), the results show that the use of synthetic data improves detection performance overall. The configuration with 600 synthetic samples per category achieves 92.27% accuracy and 92.38% macro-F1, outperforming the conventional augmentation baseline by 4.74 and 4.86 percentage points, respectively. An additional CNN experiment also showed consistent performance gains across the tested augmentation settings, indicating that the benefit of the proposed synthetic data was not restricted to the PatchTransformer architecture. These findings indicate that LLM-assisted data augmentation can effectively improve the generalization of DAS-based pipeline intrusion detection when field-labeled samples are scarce.