Abstract:

Using seismic data from the Tibet Regional Seismic Network between January 2021 and January 2025, we relocated 7, 951 earthquakes employing the double-difference algorithm. To ensure relocation reliability, we selected phase data with minimal travel-time residuals, constraining earthquake pairs to a maximum separation of 30km and requiring at least eight common phase arrivals per pair. This yielded 4, 370 high-precision relocations, with average relative errors of 0.130km(longitude), 0.131km(latitude), and 0.199km(depth). The relocated mainshock location is(28.501°N, 87.477°E)with a focal depth of 9.3km. The aftershock sequence extends approximately 70km in a nearly north-south direction, with depths mainly concentrated between 3 and 15km.

We conducted a detailed analysis of the foreshock activity preceding the M_S6.8 Dingri earthquake. Due to sparse station coverage near the epicenter, traditional seismic monitoring methods were insufficient for detecting small events. To address this, we applied the deep learning-based PhaseNet model to continuous waveform data from the nearest station(ZHF, ~50km from the epicenter), in combination with a single-station amplitude-magnitude empirical relationship for magnitude estimation. This approach significantly improved catalog completeness. Within the 56-hour window prior to the mainshock, we identified 90 seismic events, of which 80(88.9%)were microearthquakes with magnitudes M_L<2.0. In contrast, the regional network recorded only 8 events in the same period. A reliable single-station magnitude calibration was established(log₁₀A=0.77M_L+1.36) using 293 aftershocks. For commonly detected events with M_L<2.0, the average magnitude difference between the single-station and regional network methods was just 0.09, confirming the accuracy of the single-station approach. Based on the enhanced catalog and using the maximum curvature method accounting for magnitude uncertainty, the completeness magnitude was determined to be M_L1.10. The b-value, estimated via the maximum likelihood method, was 0.58±0.07. Relocation results show that foreshocks were spatially clustered within the eventual aftershock zone, approximately 20km from the mainshock epicenter. Eight foreshocks occurred within the final hour before the mainshock, with the largest(M_L3.7)occurring approximately one hour prior.

These findings demonstrate that deep learning-based, single-station detection methods can substantially enhance earthquake monitoring in regions with sparse seismic networks. The spatial and temporal characteristics of the foreshock sequence offer critical insights into earthquake preparation processes. The single-station magnitude estimation method presented here provides a valuable reference for seismic monitoring in similarly data-limited regions.

Key words: Dingri earthquake, seismogenic structure, deep learning detection, foreshock sequence, b value

摘要： 2025年1月7日西藏定日发生 M_S6.8 地震。文中采用双差定位法对主震及其前后7 951个地震进行重定位, 获得4 370个精确震源位置。重定位结果显示, 主震位置为(28.501°N, 87.477°E), 震源深度9.3km, 余震序列整体呈近SN向展布, 长约70km, 深度主要集中在3~15km。由于震中位于台站覆盖稀疏区域, 传统台网监测难以全面捕捉地震前的小地震活动。为此, 文中创新性地将深度学习方法PhaseNet与单台振幅-震级估算相结合, 对距震中最近的ZHF台站(震中距约50km)连续波形数据进行系统分析。结果表明, 在主震前56h内共识别出90次地震事件, 其中88.89%为M_L<2.0的小地震, 大大超过了同期区域台网识别的8次地震。对293次已知震级的余震进行分析, 建立了可靠的单台震级标定关系(log₁₀ A=0.77M_L+1.36)。基于改进后的地震目录, 在考虑震级误差的情况下采用最大曲率法确定完备震级为M_L1.10, 通过最大似然法计算得到b值为 0.58±0.07。从空间分布来看, 精定位结果显示这些前震事件主要发生在主震震中约20km范围内的余震区内; 时间演化特征表明, 主震前1h内记录到8次地震事件, 其中最大前震(M_L3.7)发生在主震前约1h。文中结果验证了基于深度学习的单台检测方法在台站稀疏区域的有效性, 为研究地震孕育过程提供了重要观测资料。这一方法对其他台站覆盖稀疏地区的地震监测具有重要的借鉴意义。

关键词: 定日地震, 发震构造, 深度学习检测, 前震序列, b值