A Study on Construction of ELF Band Electromagnetic Field Measuring System and Application to Tectonic Activity Detection

(1)建设电磁辐射观测相控阵网络：增加观测点位。我们实现了数字滤波，以缩小观测频带。高灵敏度磁场传感器和高增益滤波放大器的研制：减小了传感器的体积和重量，并将其放置在耐候外壳中，便于设置和稳定。(3)具有GPS同步和数据记录装置的相敏检测器的研制：研制了基于GPS的定时同步电路。基于实时Linux的数据记录系统的实现。(4)基于统计模型和声学特征的地壳电磁辐射检测系统的开发：提出了基于线性预测误差的自动检测方法。异常电磁辐射源的估计和概率模型的讨论：以倒谱系数为特征参数，确定了GTM对观测数据进行了MAP位置分类。(6)对滤波方法和异常辐射检测方法的研究：提出了基于绝对误差的TPE去噪方法、ICA和NMF去噪方法。神经网络信号分析：以小波变换后的观测数据为参考，构造神经网络。电磁场分析：通过监测磁场方位角的变化，可以自动检测到异常电磁辐射。

(1) Construction of phased array network to observe EM radiation: We increased the number of observation sites. We implemented digital filter to narrow down observing frequency band. We achieved accurate timestamp of data by GPS.(2) Development of the high-sensitive magnetic field sensor and high-gain filtering amplifier: We reduced size and weight of the sensor, and set it in the weather-resistant case for easy setting and stability.(3) Development of phase sensitive detector with GPS synchronization and data logging device: We developed the timing synchronous circuit using GPS. We achieve data logging system based on real-time Linux.(4) Development of detection system for electromagnetic radiation from crust by statistical model and acoustic signature: We proposed automatically detection method monitoring on liner prediction errors. We got knowledge that indicates the existence of earthquake-precursor from detected anomalous radiations around occurrence of west-off Fukuoka earthquake.(5) Source Estimation of anomaly electromagnetic radiation and deliberation of probability model: We confirmed the observed data are classified with map position by GTM when using cepstrum coefficients as feature parameter.(6) Deliberation of filtering method and deliberation of anomalous radiation detection method: We proposed background noise reduction methods by TPE using absolute errors, ICA and NMF. Each method removes global background noise well.(7) Signal analysis by neural network: We made neural net using wavelet-transformed observed data as reference. We confirmed our neural net could detect anomalous signal.(8) Analysis of electromagnetic field: We found out anomalous electromagnetic radiation can be detected automatically by monitoring the variance of magnetic field azimuth.