| 东祁连山北缘断裂带基于深度学习的密集台阵地震事件快速检测与定位研究 |
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| 引用本文： | 杨少博,王炳文,高级,张海江.东祁连山北缘断裂带基于深度学习的密集台阵地震事件快速检测与定位研究[J].震灾防御技术,2022,17(1):38-45. |
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| 作者姓名： | 杨少博 王炳文 高级 张海江 |
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| 作者单位： | 1.中国科学技术大学地球和空间科学学院, 合肥 230026 |
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| 基金项目： | 国家重点研发计划（2018YFC1504102） |
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| 摘 要： | 为监测东祁连山北缘断裂带附近的地震活动性，布设包含240台短周期地震仪的面状密集台阵，进行约30 d的连续观测。首先使用基于深度学习的多台站地震事件检测算法（CNNDetector）进行地震事件检测，然后使用震相拾取网络（PhaseNet）对地震事件进行P波和S波到时拾取，其次使用震相关联算法（REAL）进行震相关联及初定位，最后使用双差定位（hypoDD）进行地震重定位，最终的精定位地震目录中共有517个地震。在密集台阵观测期间，中国地震台网正式地震目录中共有39个位于台阵内的地震事件，相比而言，密集台阵检测到大量小于0级的地震。因此通过布设密集台阵，可提高活动断裂微地震活动性的监测能力。与历史地震空间分布相比，密集台阵地震精定位分布具有较好的一致性，表现出更明显的线性分布特征。基于地震分布，发现研究区域存在与地表断层迹线走向不同的隐伏活跃断裂。
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| 关 键 词： | 密集台阵 深度学习 地震定位 活动断裂 |
| 收稿时间： | 2022-02-06 |
Rapid Earthquake Detection and Location for Dense Array Data in the Fault Zone of the Northern Margin of the East Qilian Mountains Based on Deep Learning |
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| Yang Shaobo,Wang Bingwen,Gao Ji,Zhang Haijiang.Rapid Earthquake Detection and Location for Dense Array Data in the Fault Zone of the Northern Margin of the East Qilian Mountains Based on Deep Learning[J].Technology for Earthquake Disaster Prevention,2022,17(1):38-45. |
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| Authors: | Yang Shaobo Wang Bingwen Gao Ji Zhang Haijiang |
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| Institution: | 1.School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China2.Mengcheng National Geophysical Observatory, University of Science and Technology of China, Hefei 230026, China |
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| Abstract: | For monitoring the earthquake activity of the northern margin of east Qilianshan fault belt, we have deployed a dense seismic array consisting of 240 short-period seismic instruments for about one month observation. We first used the deep learning based multi-station event detector (CNNDetector) algorithm to detect seismic events. Then we used PhaseNet to pick first P and S arrivals for these events. To remove unreliable picks, we adopted the REAL algorithm for phase association and coarse event location. For the final step, we relocated 517 events by the double-difference seismic location method (hypoDD). For the same monitoring period, the China Earthquake Administration (CEA) catalog monitored 39 seismic events within the array. In comparison, our dense seismic array detected more weak events with magnitudes lower than zero, thus having higher monitoring ability for earthquake activity. Compared to historical earthquakes, seismic events detected by our dense array have consistent spatial distributions but more linear features. Based on the earthquake locations, we find there are active blind faults that have different strikes with surface fault traces. |
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| Keywords: | Dense array Deep learning Earthquake location Active fracture |
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