Acoustic emission (AE) event location plays an important role in structural safety assessments. However, accurately locating an AE event is usually difficult, especially for a small structure. Interestingly, a pencil-lead break (PLB) experiment shows that the P-wave arrival time of a sensor is always significantly different (~260 µs) from that of the other sensors. This time difference is much greater than the P-wave travel time in the range of the experimental sample. Therefore, it can be inferred that there is a P-wave arrival time system error (PATSE) for each sensor. The PATSE may be due to a combined result of the sensor site effect and signal transfer delay time from the sensor to the signal storage. To handle this, a Bayesian inversion framework was built to estimate PATSEs. A synthetic test demonstrated the effectiveness of the proposed Bayesian method for noisy P-wave arrival time data. Then, Bayesian inversion was applied to 15 PLB events, which confirmed the existence of PATSE in an AE experiment for the first time. The average PATSE reached 1.47 µs without considering the P-wave arrival time significantly different sensor. The average location error of 25 PLB events was 14.30 mm and 6.58 mm for PATSE unremoved and removed data, respectively. To achieve this, a high-precision virtual field optimization location method (VFOM) was used. This demonstrates the necessity of removing the PATSEs. Finally, the AE event location performance for the PATSE unremoved and removed data was compared, where the AE events were obtained from the uniaxial compression of a red sandstone sample. The results indicated that there was a higher location detection success rate for the corrected data. The AE locations based on the corrected data were in a better correlation with the rock sample failure mode than that without correction. Moreover, increase the signal sampling frequency for AE event identification, use a real-time inverted 3D velocity model and update the PATSEs in real time could be used to further improve the AE event location accuracy.

声发射 (AE) 事件定位在结构安全评估中起着重要作用。但是，准确定位 AE 事件通常很困难，尤其是对于小型结构。有趣的是，铅笔芯断裂 (PLB) 实验表明，传感器的 P 波到达时间始终与其他传感器的 P 波到达时间明显不同（~260 µs）。这个时间差远大于实验样本范围内的 P 波传播时间。因此，可以推断每个传感器都存在纵波到达时间系统误差（PATSE）。PATSE 可能是传感器位置效应和从传感器到信号存储的信号传输延迟时间的综合结果。为了解决这个问题，构建了一个贝叶斯反演框架来估计 PATSE。综合测试证明了所提出的贝叶斯方法对噪声 P 波到达时间数据的有效性。然后将贝叶斯反演应用于15个PLB事件，首次在AE实验中证实了PATSE的存在。平均 PATSE 达到 1.47 µs，而不考虑 P 波到达时间显着不同的传感器。对于 PATSE 未删除和已删除数据，25 个 PLB 事件的平均位置误差分别为 14.30 毫米和 6.58 毫米。为此，使用了高精度虚拟场优化定位方法 (VFOM)。这表明删除 PATSE 的必要性。最后，比较了 PATSE 未删除和已删除数据的 AE 事件定位性能，其中 AE 事件是从红砂岩样品的单轴压缩中获得的。结果表明，校正后的数据有较高的位置检测成功率。基于校正数据的声发射位置与未校正的岩样破坏模式具有更好的相关性。此外，提高AE事件识别的信号采样频率，使用实时反演3D速度模型并实时更新PATSE，可以进一步提高AE事件定位精度。