###### Abstract  
Acoustic emission tomography (\"AET\") is used to visualize internal structural damage. In this study, we aimed to improve the accuracy of the identified elastic wave velocity distribution when including errors in the observed arrival time, which is used as the observation value in AET. Weightsovercome this difficulty [10]. AET is structured via a combination of the source location technique of AE testing and elastic wave tomography and makes it possible to identify the elastic wave velocity distribution even if the excitation location and time are unknown by only using the arrival times of AEs at the sensors. This technique was extended by adopting the ray-trace technique [11; 12] and new source localization technique that considers the heterogeneity of elastic wave velocity distribution [13]. As one of the applications, this technique was adopted to visualize the fractures in the rock specimen [14]. Since AET only takes the arrival times as input values, it is not necessary to know the excitation location and time in advance as with elastic wave tomography. Although AET effectively detects structural damage, the resolution of the source locations and the number of sensors in the AET algorithm, cells in the analysis model, and excitations are arbitrary parameters, and the analysis accuracy depends on these parameters. According to [PERSON] et al. [15], a higher source location resolution improves the accuracy of the identified elastic wave velocity distribution. However, these studies have only examined the effect of the source location resolution and rarely those of observation errors. On the other hand, the accuracy of AE arrival times is crucial for identifying the source location via AE testing and the elastic wave velocity distribution via AET. The Akaike information criterion (AIC) [16] is widely used for detecting the arrival time from the measured waveforms [17; 18; 19; 20]. The boundary between the noise and signal in arrival time detection is defined via the AIC picker and adopted as the arrival time of the measured wave form. However, it is sometimes difficult to clearly define the boundary if the signal to noise ratio (\"S/N ratio\") is low. This happens especially if the elastic wave is diffracted from low velocity areas, e.g., a damaged area or void. In this case, the accuracy of the detected arrival time is expected to be lower. Using inaccurate arrival times for source localization degrades the quality of the identified source locations. Consequently, [PERSON] et al. proposed a technique that adopts a self-organizing map (SOM) [21] to classify the elastic waves and avoid this issue [17]. SOM is an unsupervised learning method that visualizes data relations and selects the measured waveforms in which the arrival time is accurately detected. By using this technique, it is expected that source localization can be performed without inaccurate arrival times and the source location accuracy would be subsequently improved. In a previous study, artificial AE signals were classified via SOM, and source localization was conducted using a source localization technique based on ray tracing [13] to improve the accuracy of source localization on heterogeneous velocity distributions [22]. As introduced above, the previous challenge was selecting sufficient waveforms that provide accurate arrival times to improve source location accuracy. However, in AET applications, this strategy sometimes causes problems in identifying the elastic wave velocity distribution. Since the diffracted wave yields information about the existing damaged areas, the information is not considered if the wave is omitted. Furthermore, if the sensor and the location of the excitation are close to each other, the first travel time decreases, and the effect of the error on the arrival time is expected to be relatively large. Therefore, past trials have attempted to improve the accuracy of identified elastic wave velocity distribution by removing elastic wave events when the excitations and sensor are closely located to each other.