微震去噪与定位方法研究

微震震源信息可以反映岩层破裂位置及应力状态，在矿山安全开采和页岩气压裂效果评价中具有重要应用价值。当前，微震监测已广泛应用于工业生产及火山活动监测中。由于微震释放的能量小，微震信号的信噪比相对较低，直接影响了震源参数反演精度。因此，开发高精度微震信号去噪方法具有重要意义。在实际应用中，微震的发震时间和空间位置可直观反映监测区应力场变化和风险时空演化，发展更高精度的微震定位方法同样至关重要。因此，本论文主要围绕微震去噪和定位方法来开展研究。

  微震数据信噪比是影响震源参数反演精度的关键因素。针对传统时频阈值去噪方法中存在噪音阈值估计不准、无法处理噪音残留的问题，我们提出了一种融合同步挤压小波变换与图像处理技术的微震信号去噪新方法。该方法通过方差比获取纯背景噪音，在此基础上利用不同频率的背景噪音经验累积概率分布函数，得到更准确的小波系数阈值，最后引入图像处理技术中的像素连通性来去除相对孤立的残留噪音，实现微震信号的精准去噪。合成数据和实际观测数据测试结果验证了新提出的去噪方法的有效性和实用性，矿山微震信号自动处理结果表明该去噪方法可以显著提高微震信号的检测成功率。

  基于到时拾取的定位方法因其原理简单、计算速度快而广泛应用于矿山微震监测等对实时性要求高的工业生产中。在实际应用中，部分地震道的初至拾取可能存在较大误差，针对此问题，我们通过构建台站对等时差（Equal Differential Time，EDT）曲面概率分布函数，提出了一种概率成像定位新方法。该概率分布函数利用微震信号信噪比信息，当信噪比高时，到时准确度大，等时差曲面宽度收窄，表现为非重尾分布特征；当信噪比低时，到时准确度小，等时差曲面宽度变宽，表现为重尾分布特征，实现了对分布函数的自适应调整，自动降低了到时异常数据在目标函数的权重。最后采用相乘形式的概率定位成像函数，降低异常数据对定位精度的影响，压制由叠加产生的定位概率极大值伪影，从而提高定位精度和降低震源搜索复杂度。合成数据和实际矿山微震数据测试结果表明该定位方法具有较高的定位精度和稳健性，定位结果可为矿山巷道中高风险区域的圈定提供可靠信息。

  基于波形叠加的偏移成像定位方法因无需拾取初至、可自动化操作而广泛应用于水力压裂微震监测中。但由于不同方位角的检波器记录可能存在极性反转现象，相干叠加增强信号的效果会受到影响，制约了定位成像分辨率和精度。基于极性相反的两个信号相位差恒为180度这一特征，我们开发了一种基于希尔伯特变换极性校正的波形叠加定位新方法。该方法利用瞬时相位信息直接在数据域中完成极性一致性校正，并保留了噪声的不相干性。偏移叠加极性校正后的波形，有效地提高震源成像分辨率和定位精度。合成数据和实际数据测试表明该方法可以成功检测并准确定位因极性反转而被传统方法遗漏的微震事件。在实际煤层气压裂数据处理中，该方法定位的微震事件位置聚集于实际压裂目标层，可为实际生产活动提供指示信息。

  综上，本论文针对微震去噪和定位方法中存在的具体问题开展了研究。提出了一种融合同步挤压小波变换与图像处理技术的地震信号去噪新方法；构建了台站对等时差曲面概率分布函数，发展了新型概率成像定位算法；开发了一种基于希尔伯特变换极性校正的波形叠加定位新技术。合成数据和实测数据分析表明，本论文所开发的新方法可显著提高微震检测率和定位精度。相关方法已应用于实际矿山安全监测及页岩气、煤层气生产数据分析处理中。

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