Abstract: To address the problems of large storage overhead, heavy reading burden, and low data-access efficiency of large-scale three-dimensional traveltime tables in microseismic localization, an integrated technical workflow of “traveltime-table computation, compressed storage, decompression recovery, and localization call” was established by introducing the SZ error-bounded predictive compression algorithm based on regular-grid traveltime tables generated by the fast marching method. The compressed traveltime tables were further embedded into migration-stacking and joint source-mechanism inversion localization programs. Combined with a GPU-accelerated migration-stacking localization framework, the influences of compressed traveltime tables on event detection, imaging focusing, and overall workflow efficiency were analyzed from both the data-access layer and the localization-solving layer. Results based on the three-dimensional Overthrust model, synthetic microseismic data, and field monitoring data show that the SZ algorithm can significantly reduce the size of traveltime tables under controlled error conditions. In contrast, the compressed traveltime tables remain highly consistent with the original traveltime tables in terms of event-detection curves, imaging-peak positions, and spatial localization results. These results demonstrate that the error-bounded predictive compression method can significantly improve the overall processing efficiency of large-scale microseismic localization while preserving localization effectiveness.