探地雷达钢筋检测数据深度学习反演

LIU PengFei, CAO Nan,ZHANG ZhiHou,ZHANG TianYi, YANG Yang

Progress in Geophysics（2023）

Cited 0|Views11

Abstract

实际工程中探地雷达采集的数据量大,目前对其数据解译仍然以人工为主,成本高、效率低,且解译结果受解译员的专业知识及经验等主观因素影响.因此,为快速、准确且智能化对探地雷达实测数据进行处理,文中提出一种探地雷达钢筋检测数据深度学习反演方法.文中首先运用时域有限差分方法对不同尺寸、不同埋深的钢筋模型进行正演计算,构建样本数据集;其次搭建一种端到端的深度学习网络架构,其输入端为归一化后的探地雷达数据,输出端为介电常数模型;然后针对网络进行监督学习;最后,利用已训练好的网络对未知探地雷达数据进行反演.模型试验表明,反演结果与模型中钢筋位置及尺寸均高度吻合,模型一、模型二和模型三的介电常数反演值精度分别为99.28％、99.23％和98.84％.对于含噪声数据的反演,仍然具有较好的效果.实测探地雷达数据的反演也验证了文中方法的有效性.

Key words

Deep learning,Ground penetrating radar,Rebar identification,End-to-end inversion

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：提出一种基于深度学习的探地雷达数据反演方法，用于快速准确检测加固领域中的钢筋位置及尺寸，实现高效智能的数据解译。

【方法】：采用时域有限差分方法构建样本数据集，并搭建端到端的深度学习网络架构，输入为归一化的探地雷达数据，输出为介电常数模型，进行监督学习。

【实验】：使用时域有限差分方法生成的数据集进行实验，反演结果与模型中钢筋位置及尺寸高度吻合，模型一、模型二和模型三的介电常数反演值精度分别达到99.28％、99.23％和98.84％，且对含噪声数据具有较好的反演效果。