{"title":"Peculiarities of Correlation Processing of Pseudo Noise Signals in Controlled-Source Electromagnetic Instruments","authors":"V. V. Bobrovskii","doi":"10.1134/S1069351324700678","DOIUrl":null,"url":null,"abstract":"<p><b>Abstract</b>—The paper presents the algorithm for eliminating structured noise (SN) appearing in the transient electromagnetic (TEM) response curves during correlation processing of recorded pseudo noise signals. The algorithm is based on the detection and elimination of pulses of structured noise (SNP) with energies exceeding optimal detection threshold (calculated from the TEM curve) against the background of the smoothly (slowly) varying TEM sounding signal. The method provides reliable SNP detection with high correct recognition and low false alarm rate as well the subsequent elimination of SN field from the recorded TEM curves. For automatic calculation of SNP optimal detection threshold, it is proposed to use the criterion that the noise and interference signals remaining in the TEM curve after SNP removal are maximally close to normal distribution. Selection and setting of the optimal detection threshold is performed by successive sweep through its values (from maximum to minimum) with a given step which determines the accuracy of detection threshold. The procedure of selecting the detection threshold is reduced to finding, at each step, the maximum value of the Pearson correlation coefficient between the probability density distribution functions of amplitudes of noise and interference signals remaining in TEM curve after SN removal and the calculated normal distribution. This approach has made data processing maximally automated and increased its speed, which is very important in the field work. The algorithm was tested on TEM signals recorded at Chunkurchak experimental site in the field work with electromagnetic measurement system using pseudo noise signals (EMS PNS). Application of the algorithm has significantly improved (approximately by a factor of 450) the signal to noise ratio in the TEM response curve in the interval of late times.</p>","PeriodicalId":602,"journal":{"name":"Izvestiya, Physics of the Solid Earth","volume":"60 4","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Izvestiya, Physics of the Solid Earth","FirstCategoryId":"89","ListUrlMain":"https://link.springer.com/article/10.1134/S1069351324700678","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract—The paper presents the algorithm for eliminating structured noise (SN) appearing in the transient electromagnetic (TEM) response curves during correlation processing of recorded pseudo noise signals. The algorithm is based on the detection and elimination of pulses of structured noise (SNP) with energies exceeding optimal detection threshold (calculated from the TEM curve) against the background of the smoothly (slowly) varying TEM sounding signal. The method provides reliable SNP detection with high correct recognition and low false alarm rate as well the subsequent elimination of SN field from the recorded TEM curves. For automatic calculation of SNP optimal detection threshold, it is proposed to use the criterion that the noise and interference signals remaining in the TEM curve after SNP removal are maximally close to normal distribution. Selection and setting of the optimal detection threshold is performed by successive sweep through its values (from maximum to minimum) with a given step which determines the accuracy of detection threshold. The procedure of selecting the detection threshold is reduced to finding, at each step, the maximum value of the Pearson correlation coefficient between the probability density distribution functions of amplitudes of noise and interference signals remaining in TEM curve after SN removal and the calculated normal distribution. This approach has made data processing maximally automated and increased its speed, which is very important in the field work. The algorithm was tested on TEM signals recorded at Chunkurchak experimental site in the field work with electromagnetic measurement system using pseudo noise signals (EMS PNS). Application of the algorithm has significantly improved (approximately by a factor of 450) the signal to noise ratio in the TEM response curve in the interval of late times.
摘要--本文介绍了在对记录的伪噪声信号进行相关处理时消除瞬态电磁（TEM）响应曲线中出现的结构噪声（SN）的算法。该算法的基础是在平滑（缓慢）变化的 TEM 探测信号背景下，检测并消除能量超过最佳检测阈值（根据 TEM 曲线计算得出）的结构化噪声（SNP）脉冲。该方法可提供可靠的 SNP 检测，识别正确率高，误报率低，并可从记录的 TEM 曲线中消除 SN 场。为了自动计算 SNP 的最佳检测阈值，建议使用以下标准：去除 SNP 后，TEM 曲线中剩余的噪声和干扰信号最大程度地接近正态分布。最佳检测阈值的选择和设置是通过连续扫描其值（从最大值到最小值）来完成的，其给定的步长决定了检测阈值的精度。选择检测阈值的程序简化为在每一步找到去除 SN 后 TEM 曲线中剩余噪声和干扰信号振幅的概率密度分布函数与计算出的正态分布之间的最大皮尔逊相关系数值。这种方法最大限度地实现了数据处理的自动化，并提高了速度，这在现场工作中非常重要。该算法在 Chunkurchak 试验场地利用伪噪声信号（EMS PNS）电磁测量系统记录的 TEM 信号上进行了测试。该算法的应用极大地提高了（大约提高了 450 倍）TEM 反应曲线在后期时间间隔内的信噪比。
期刊介绍：
Izvestiya, Physics of the Solid Earth is an international peer reviewed journal that publishes results of original theoretical and experimental research in relevant areas of the physics of the Earth''s interior and applied geophysics. The journal welcomes manuscripts from all countries in the English or Russian language.
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