李健威,薛生,侯彦威,等. 高精度位场边界识别方法及在煤矿火烧区边界解释的应用[J]. 煤炭学报,xxxx,xx(x): x−xx. doi: 10.13225/j.cnki.jccs.2023.1651
引用本文: 李健威,薛生,侯彦威,等. 高精度位场边界识别方法及在煤矿火烧区边界解释的应用[J]. 煤炭学报,xxxx,xx(x): x−xx. doi: 10.13225/j.cnki.jccs.2023.1651
LI Jianwei,XUE Sheng,HOU Yanwei,et al. A high-precision boundary identification method and its application to coal mine fire zone boundary interpretation[J]. Journal of China Coal Society,xxxx,xx(x): x−xx. doi: 10.13225/j.cnki.jccs.2023.1651
Citation: LI Jianwei,XUE Sheng,HOU Yanwei,et al. A high-precision boundary identification method and its application to coal mine fire zone boundary interpretation[J]. Journal of China Coal Society,xxxx,xx(x): x−xx. doi: 10.13225/j.cnki.jccs.2023.1651

高精度位场边界识别方法及在煤矿火烧区边界解释的应用

A high-precision boundary identification method and its application to coal mine fire zone boundary interpretation

  • 摘要: 场源边界识别是位场数据解释必不可少的任务,早先人们使用数据的分布特征来获取场源的边界信息,难以在较强的背景异常条件下识别弱异常,为此使用基于一定窗口尺寸的自动控制滤波器来识别场源的分布,然而这种方法的结果对于窗口尺寸有较大的依赖,且对于复杂异常难以良好应用。近几年,人们主要采用位场数据导数的特征点反映场源边界信息的特征,然后利用成像结果与边界的对应关系来识别场源体的水平边界。其中,磁异常水平导数的极值、垂直导数的零值与地质体边界相对应,现有边界识别方法主要采用一阶水平和垂直导数的比值所组成的均衡边界识别滤波器来完成地质体位置的圈定,但方法分辨率和泛用性较低。基于此,提出将基于不同阶导数比值的边界检测滤波器与多尺度无监督深度学习相结合,利用不同阶导数比值来获得更高分辨率的边缘成像结果,同时建立Deep Image Prior (DIP)与 Generative Adversarial Network-None Local (GAN-NL)网络相结合的多尺度无监督深度学习,根据边缘成像结果的极值来获取源水平位置。利用多尺度DIP网络来识别源位置,在DIP网络中加入自注意机制神经网络增强其学习能力,可以在不需要大量数据标签的情况下去除噪声,利用GAN-NL网络对极值点进行分类,给出极值点的位置信息。与其他边缘检测滤波器比较的结果表明:所开发的边界识别方法具有更高的分辨能力,其能够更精确、更清晰地显示场源的边缘,相对常规方法精度提升15%左右。多尺度无监督深度学习可以根据边缘成像结果自动给出源边缘,且结果与真实边缘一致,具有良好的泛用性。通常情况下,煤层与围岩之间不存在明显的磁性差异。而煤层自燃时,高温使煤层顶板地层中的黄铁矿、菱铁矿等结核体受热变质,形成含铁磁性矿物的烧成岩。温度降低后,保留了强烈的热剩余物,磁化强度比燃烧前高几十倍,因此煤矿燃烧区存在明显的磁异常。这一特性为利用磁测法圈定燃烧区域边界提供了物理前提。针对山东某矿煤田燃烧区开展磁法勘探来查明其分布,将所开发的边界识别方法用于获取火烧区的分布范围,后期打钻验证结果准确率85%。

     

    Abstract: Boundary identification of field sources is an indispensable task for interpreting field data. Initially, people used the distribution characteristics of data to obtain boundary information of field sources, making it difficult to identify weak anomalies amidst strong background anomalies. To address this issue, automatic control filters based on a certain window size were employed to identify the distribution of field sources. However, this method's results heavily relied on the window size and were not well applicable to complex anomalies. In recent years, features reflecting the boundary information of field sources have mainly been derived from the derivatives of scalar field data. Then, the correspondence between imaging results and boundaries is utilized to identify the horizontal boundaries of field sources. Specifically, extreme values of magnetic anomaly horizontal derivatives and zero values of vertical derivatives correspond to geological body boundaries. Existing boundary identification methods mainly utilize a balanced boundary identification filter composed of the ratio of first-order horizontal and vertical derivatives to delineate the positions of geological bodies, but the method has lower resolution and generality. Therefore, this paper proposes combining boundary detection filters based on ratios of derivatives of different orders with multiscale unsupervised deep learning. This approach utilizes different orders of derivative ratios to obtain higher-resolution edge imaging results. Additionally, a combination of Deep Image Prior (DIP) and Generative Adversarial Network-None Local (GAN-NL) networks for multiscale unsupervised deep learning is established to determine the horizontal position of sources based on extreme values of edge imaging results. The multiscale DIP network is used to identify the source position, and a self-attention mechanism neural network is added to the DIP network to enhance its learning ability, which can remove noise without requiring a large amount of data label.

     

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