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多源信息融合约束下的工作面槽波探测智能反演方法

Intelligent Inversion Method of Slot Wave Detection on Working Face under Multi-source Information Fusion Constraint

Environment and Resource / 2025,7(1): 1-8 / 2025-07-04 82 58

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• Authors: 王有刚
• Information:

  吕梁学院数学与人工智能系，吕梁

• Keywords:
  Mining engineering; Multi-source information; Trough wave detection; Intelligent inversion
  矿业工程; 多源信息; 槽波探测; 智能反演
• Abstract: To meet the demand for precise prediction of structural distribution in intelligent mining and to enhance the accuracy of slot wave detection inversion, an intelligent inversion method for slot wave detection in working faces is proposed, which integrates multi-source information fusion constraints. This method addresses the limitations of traditional inversion methods by integrating multi-source information acquisition, information fusion techniques, machine learning algorithms, and deep learning models. Three constraint models—range constraint, nearest constraint, and mean value constraint—are proposed and tested on working faces. The research findings indicate that this method achieves a higher prediction accuracy compared to a single genetic algorithm, with a prediction accuracy of 96.2%, which is over 6% higher than traditional models. This significantly improves the precision of geological structure detection, providing robust technical support for safe and efficient coal mine mining. 为满足智能化开采对构造分布精准预测的需求，提高槽波探测反演的准确性，提出基于多源信息融合约束下的工作面槽波探测智能反演方法，分析传统反演方法的不足，基于此，综合多源信息获取、信息融合方法与技术，结合机器学习算法和深度学习模型，提出范围约束、就近约束和平均值约束3种约束模型，并在工作面进行测试验证，研究结果表明，该方法相较于单一遗传算法具有更高的预测准确性，预测准确度达96.2%，较传统模型提升6%以上，显著提高地质构造探测精度，为煤矿安全高效开采提供有力技术支持。
• DOI: https://doi.org/10.35534/er.0701001
• Cite: 王有刚．多源信息融合约束下的工作面槽波探测智能反演方法［J］．环境与资源，2025，7（1）：1-8．