# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 1 Introduction  
Acoustic emissions (AEs) are elastic waves occurring with micro-cracks and/or frictions, and they have been applied to non-destructive testing (NDT) [1; 2; 3; 4]. In particular, AE source localization has potential to detail the progress failures in materials. In the field of geotechnical engineering, AE sources generated by triaxial compression tests have visualized shear bands developed in saturated sand [5]. Moreover, source localization has been applied to the visualization of particle breakages during pile penetration in the soil tank [6]. Furthermore, source localization has visualized micro-cracks that appeared in infrastructures [7; 8], and it has been used in the field of health monitoring of existing structures. The time difference of arrival (TDOA) method [9] is one of the source localization methods. Owing to the straight propagations assumed in the algorithm of the TDOA method, the lengths of the ray paths from the source to the sensors and the arrival time difference in combinations of sensor locations can be used in defining the equation for the source localization. Furthermore, arrival times of AE signals are detected from measuredtime-history waveforms, and the detected arrival times are used for the input data of the TDOA method. Thus, the TDOA method has applicability since the TDOA method can localize sources generated in various materials if the AE signals can be measured at each sensor. In addition, it has been widely used in the field of NDT [5; 6; 10] because of the above applicability. The elastic wave velocity distributions of specimens used in element or model tests are expected to be updated by loading; for instance, the heterogeneous velocity distributions of the rock materials in triaxial tests are identified [11]. However, diffraction and refraction waves are generated in the heterogeneities of the velocity distributions, and it is difficult for the TDOA method to be conducted with element or model tests if velocity distributions are updated. Moreover, progress failures possibly form the heterogeneity in existing structures and are expected to decrease the accuracy of health monitoring based on the TDOA method. In recent approaches, [PERSON] et al. identified a defects area, and the AE source localization was conducted with the considered defects area [12]. Furthermore, [PERSON] et al. applied Gaussian process regression to Bayesian source localization to localize sources generated in complex structures [13]. Furthermore, these approaches use elastic wave arrival times in which the input data are the same as in the TDOA method. Since huge numbers of AEs are generated in AE measurements [14; 15], the arrival time is generally detected by automatic picking algorithms. In automatic picking algorithms, the uses of characteristic functions have been applied to measured waveforms, and the time wherein the value of the functions exceeds the threshold has been defined as the wave arrival time [16; 17]. In addition, the Akaike information criterion (AIC) has been used for arrival-time detections conducted in measured waveforms [18; 19], and AIC pickers do not require the use of the threshold considered for the measurement conditions in the arrival-time detection since the arrival times are defined as evaluated boundaries between noises and signals. Although these automatic picking algorithms can detect accurate arrival times from high S/N signals, it is supposed that the accuracy of arrival-time detections is decreased in low S/N signals since the index of arrival times is computed from amplitude differences in the waveform. According to the Huygens-Fresnel principle, the amplitude is attenuated with an angle of the diffraction. Thus, it implies that the accuracy of an arrival time detected from a diffraction wave is decreased, and it is then challenging for an arrival time detected from a diffraction wave to be applied to source localizations.