基于LSM和RELBP的煤岩识别方法探讨

张 荣 华

基于LSM和RELBP的煤岩识别方法探讨

引用本文：张荣华.基于LSM和RELBP的煤岩识别方法探讨[J].计算技术与自动化,2021,(1):109-113

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作者	单位
张荣华	（国家能源集团上榆泉煤矿，山西河曲 036500）

中文摘要:对当前煤岩识别方法的研究现状进行了介绍，并提出将最小二乘法模型（Least square model ，LSM）和融入平滑滤波思想的鲁棒扩展局部二值模式（Robust extended local binary Pattern ，RELBP）融入煤岩识别领域。对基于LSM和RELBP的煤岩识别方法的煤岩自动化识别技术（RELBP-LSM）进行了探讨。结果表明：（1）当前的煤岩识别方法大多存在效果较差、稳定性欠佳、适用范围小等缺点，同时易受人为因素的影响；（2）以最小二乘法和局部二值模式为理论基础，建立起RELBP-LSM煤岩识别方法，并通过参数敏感性分析，确定正则化参数λ的最佳取值为10-3.5，优选模式数d的最佳取值为500；（3）对不同方法的准确识别率进行对比分析，认为RELBP-LSM法不仅具有较高的准确识别率，同时能大大降低内存占用率，加快识别速率和效率。

中文关键词:煤岩最小二乘法局部二值法 RELBP-LSM 参数敏感性准确识别率

Discussion on Coal Rock Identification Method Based on LSM and RELBP

Abstract:This paper discusses the current research situation of coal and rock recognition methods, and puts forward that the least square model (LSM) and robust extended local binary pattern (RELBP), which are integrated into the idea of smooth filtering, are integrated into the field of coal and rock recognition, and the coal and rock automatic recognition technology (RElB) based on LSM and RELBP is applied to the field of coal and rock recognition P-LSM). The results show that: (1) most of the current methods of coal rock identification have some disadvantages, such as poor effect, poor stability and small application scope, and are easily affected by human factors; (2) based on the least square method and local binary model, the RELBP-LSM method of coal rock identification is established, and the best value of regularization parameter λ is determined to be 10-3.5 by parameter sensitivity analysis The best value of pattern number d is 500; (3) by comparing and analyzing the accuracy of different methods, it is considered that RELBP-LSM method not only has a high accuracy of recognition, but also can greatly reduce the memory occupation rate and speed up the recognition rate and efficiency.

keywords:coal rock least square method local binary method RELBP-LSM parameter sensitivity accuracy recognition rate

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