基于井周三台站短周期观测的微震事件集构建及其活动性与簇发性特征分析——以深地川科一井为例

Construction of a Microseismic Event Catalog Based on Short-Period Observations at Jingzhou-Santai Station and Analysis of Its Seismic Activity and Burstiness Characteristics—A Case Study of the Shendichuanke 1 Well

摘要: 深地工程钻井活动可能诱发井周微震/小震，构建可靠的微震事件集并分析其活动性与簇发性特征，对井区应力扰动响应和风险评估至关重要。本文以深地川科一井为例，基于井周三台站短周期地震观测数据，针对区域台网目录不完备与事件漏检问题，提出了一种适用于小台站条件的微震事件集构建方法。通过对连续波形进行预处理与触发检测，采用三台站一致触发策略提取候选事件，并基于信噪比与事件窗图像进行质量分级(A/B/C)。在此基础上，统计分析事件率、累计事件数及日/月/年尺度的变化，分析事件的时间演化特征。同时，使用事件间隔分布、变异系数(CV)、Fano因子等方法评估簇发性，并基于滑动事件率提取簇发段。本文提供了一个适用于微震活动分析的技术流程，为深地工程地震风险识别与监测优化提供了参考。

Abstract: Deep underground drilling activities may induce microseismic or small earthquake events around the well, and constructing a reliable microseismic event catalog and analyzing its activity and burst characteristics are crucial for understanding the stress disturbance response and risk assessment of the well area. Taking the Shendichuanke 1 Well as an example, this study proposes a method for constructing a microseismic event catalog suitable for small station conditions, based on short-period seismic observation data from three nearby stations around the well, addressing issues of incomplete regional network catalogs and missed events. The method involves preprocessing continuous waveforms and trigger detection, using a three-station consistent triggering strategy to extract candidate events, and applying quality grading (A/B/C) based on signal-to-noise ratio and event window images. Based on this, statistical analysis of event rates, cumulative event counts, and variations at daily, monthly, and yearly scales is conducted to analyze the temporal evolution of events. Additionally, methods such as event interval distribution, coefficient of variation (CV), and Fano factor are used to assess burstiness, and burst segments are extracted based on sliding event rates. This paper provides a technical process suitable for microseismic activity analysis and offers a reference for seismic risk identification and monitoring optimization in deep engineering projects.