Micro‐earthquake monitoring with sparsely‐sampled data
Micro‐earthquake monitoring with sparsely‐sampled data
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DOI：
10.1190/1.3059164
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发表时间：
2008
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影响因子：
--
通讯作者：
P. Sava
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文献类型：
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作者：
P. Sava
微地震活动可用于监测脆性地层的储层生产和水力压裂作业期间流体的运移，或用于研究断层带中自然发生的地震。假设密集采样数据和已知速度，可以使用爆炸反射器模型下的波动方程成像来推断微地震位置。地震活动通常通过稀疏的地震传感器网络进行监测，例如位于钻孔中的地震传感器网络。即使速度模型是准确已知的，传感器网络本身的稀疏性也会降低估计位置的准确性。这一限制限制了推断流体路径的分辨率。使用稀疏阵列记录的数据以已知速度重建的波场可以描述为由于波分量的不完全干涉而具有随机特征。类似地，由于波分量的干扰不一致，使用密集阵列记录的数据以未知速度重建的波场可以被描述为具有随机特征。在这两种情况下，随机波动都会阻碍在源位置发生的聚焦。在成像过程中使用干涉测量可以改善这种情况。使用干涉成像条件的逆时成像可以减弱随机波动，从而产生更清晰的图像，支持稳健的自动微地震定位过程。本文通过一个实际的综合例子说明了模型波动和稀疏采集引起的随机波场波动的相似性。
Micro-seismicity can be used to monitor the migration of fluids during reservoir production and hydrofracturing operations in brittle formations or for studies of naturally occurring earthquakes in fault zones. Microearthquake locations can be inferred using wave-equation imaging under the exploding reflector model, assuming densely sampled data and known velocity. Seismicity is usually monitored with sparse networks of seismic sensors, for example located in boreholes. The sparsity of the sensor network itself degrades the accuracy of the estimated locations, even when the velocity model is accurately known. This constraint limits the resolution at which fluid pathways can be inferred. Wavefields reconstructed in known velocity using data recorded with sparse arrays can be described as having a random character due to the incomplete interference of wave components. Similarly, wavefields reconstructed in unknown velocity using data recorded with dense arrays can be described as having a random character due to the inconsistent interference of wave components. In both cases, the random fluctuations obstruct focusing that occurs at source locations. This situation can be improved using interferometry in the imaging process. Reverse-time imaging with an interferometric imaging condition attenuates random fluctuations, thus producing crisper images which support the process of robust automatic micro-earthquake location. The similarity of random wavefield fluctuations due to model fluctuations and sparse acquisition is illustrated in this paper with a realistic synthetic example.