WITHDRAWN: GeoPredict-LLM: Intelligent Tunnel Advanced Geological Prediction by Reprogramming Large Language Models

With the improvement of multisource information sensing and data acquisition capabilities inside tunnels, the availability of multimodal data in tunnel engineering has significantly increased. However, due to structural differences in multimodal data, traditional intelligent advanced geological prediction models have limited capacity for data fusion. Furthermore, the lack of pre-trained models makes it difficult for neural networks trained from scratch to deeply explore the features of multimodal data. To address these challenges, we leverage the fusion capability of knowledge graph for multimodal data and the pre-trained knowledge of large language models (LLMs) to establish an intelligent advanced geological prediction model (GeoPredict-LLM). First, we developed advanced geological prediction ontology model, forming a knowledge graph database. By using knowledge graph embeddings, multisource and multimodal data are transformed into low-dimensional vectors with a unified structure. Secondly, pre-trained LLMs, through reprogramming, reconstructs these low-dimensional vectors, imparting linguistic characteristics to the data. This transformation effectively reframes the complex task of advanced geological prediction as a "language-based" problem, allowing the model to approach the task from a linguistic perspective. Moreover, we propose the prompt-as-prefix method, which enables output generation, while freezing the core of the LLM, and significantly reduces the number of training parameters. Finally, evaluations show that compared to neural network models without pre-trained models, GeoPredict-LLM significantly improves prediction accuracy. It is worth noting that as long as a knowledge graph database can be established, GeoPredict-LLM can be adapted to multimodal data mining tasks with minimal modifications.