In the Koyna–Warna region, western India, an enormous number of microearthquakes was detected automatically on borehole records. Most of these events could not be identified on the surface network by a routine approach based on visual inspection primarily due to signal attenuation and the presence of noise. In this work, we implemented an automatic detection workflow to analyze the time series of an earthquake sequence that has clear foreshock and aftershock activity associated with an Mw 4.0 earthquake that occurred on 3 June 2017. Further, we applied a nested grid‐search algorithm to constrain the absolute earthquake locations. For about one month of data, a total of ∼1500 earthquakes were detected based on the automatic detection process, out of which ∼1000 earthquakes were locatable. All event detections, P‐wave and S‐wave phase readings were manually inspected and refined to ensure their quality. Previously, only about 435 events were well located based on the visual inspection approach for the same time period. Also, we analyzed repeated earthquakes based on waveform similarity leading to an improvement in the relocations of earthquakes of the aforementioned earthquake sequence. The relocated seismicity aligns parallel to a deep‐reaching lineament derived from recent investigations using airborne light detection and ranging measurements.

中文翻译：

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2017年6月在印度西部的科伊纳-瓦尔纳钻孔地震网络上记录的地震序列的相对位置

在印度西部的科伊纳-瓦尔纳地区，在井眼记录中自动检测到大量微地震。这些主要是由于信号衰减和噪声的存在，无法通过基于目视检查的常规方法在地面网络上识别出大多数这些事件。在这项工作中，我们实施了自动检测工作流程来分析与2017年6月3日发生的Mw 4.0地震相关的具有明显前震和余震活动的地震序列的时间序列。此外，我们将嵌套网格搜索算法应用于限制地震的绝对位置。根据自动检测过程，在大约一个月的数据中，共检测到约1500次地震，其中可定位约1000次地震。所有事件检测 手动检查了P波和S波相位读数并进行了改进，以确保其质量。以前，根据视觉检查方法，在同一时间段内只有大约435个事件位于适当位置。此外，我们根据波形相似性分析了重复地震，从而改善了上述地震序列中地震的位置。重新定位的地震活动与从最近使用机载光检测和测距测量的调查得出的深层线平行。我们基于波形相似性分析了重复地震，从而改善了上述地震序列中地震的位置。重新定位的地震活动与从最近使用机载光检测和测距测量的调查得出的深层线平行。我们根据波形相似性分析了重复地震，从而改善了上述地震序列中地震的位置。重新定位的地震活动与从最近使用机载光检测和测距测量的调查得出的深层线平行。