We propose a new automated procedure for using continuous seismic waveforms recorded by a dense array and its nearby regional stations for P‐wave arrival identification, location, and magnitude estimation of small earthquakes. The method is illustrated with a one‐day waveform dataset recorded by a dense array with 99 sensors near Anza, California, and 24 surrounding regional stations within 50 km of the dense array. We search a wide range of epicentral locations and apparent horizontal slowness values (⁠0–15 s/km⁠) in the 15–25 Hz range and time shift the dense array waveforms accordingly. For each location–slowness combination, the average neighboring station waveform similarity (avgCC) of station pairs <150 m apart is calculated for each nonoverlapping 0.5 s time window. Applying the local maximum detection algorithm gives 966 detections. Each detection has a best‐fitting location–slowness combination with the largest avgCC. Of 331 detections with slowness <0.4 s/km⁠, 324 (about six times the catalog events and 98% accuracy) are found to be earthquake P‐wave arrivals. By associating the dense array P‐wave arrivals and the P‐ and S‐wave arrivals from the surrounding stations using a 1D velocity model, 197 detections (⁠∼4 times of the catalog events) have well‐estimated locations and magnitudes. Combining the small spacing of the array and the large aperture of the regional stations, the method achieves automated earthquake detection and location with high sensitivity in time and high resolution in space. Because no preknowledge of seismic‐waveform features or local velocity model is required for the dense array, this automated algorithm can be robustly implemented in other locations.

中文翻译：

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使用密集地震阵列和附近台站数据编制小地震目录的自动化方法

我们提出了一种新的自动化程序，用于使用由密集阵列及其附近区域台站记录的连续地震波形来识别小地震的 P 波到达识别、位置和震级。该方法通过密集阵列记录的一天波形数据集进行说明，该阵列具有加利福尼亚州安扎附近的 99 个传感器以及密集阵列 50 公里范围内的 24 个周边区域站。我们在 15–25 Hz 范围内搜索了范围广泛的震中位置和明显的水平慢度值 (?0–15 s/km?)，并相应地对密集阵列波形进行时移。对于每个位置-慢度组合，对于每个非重叠的 0.5 s 时间窗口，计算相距 <150 m 的台站对的平均相邻台站波形相似度 (avgCC)。应用局部最大检测算法可得到 966 次检测。每个检测都有一个最合适的位置 - 慢速组合，具有最大的 avgCC。在慢度 <0.4 s/km 的 331 次检测中，发现 324 次（大约是目录事件的六倍，准确度为 98%）是地震 P 波到达。通过使用一维速度模型将密集阵列 P 波到达与来自周围台站的 P 波和 S 波到达相关联，197 次探测（~目录事件的 4 倍）具有很好的估计位置和震级。该方法结合阵列的小间距和区域站的大口径，实现了时间上灵敏度高、空间分辨率高的地震自动检测和定位。因为密集阵列不需要地震波形特征或局部速度模型的预知，