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Using Automated, High-precision Repicking to Improve Delineation of Microseismic Structures at the Soultz Geothermal Reservoir

机译:使用自动化高精度摘录改善Soultz地热储层微震构造的轮廓

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An automatic, adaptive, correlation-based algorithm for adjusting phase picks in large digital seismic data sets provides significant improvement in resolution of microseismic structures using only a small fraction of the time and manpower which would be required to re-analyze waveforms manually or semi-automatically. We apply this technique to induced seismicity at the Soultz-sous-Forets geothermal site, France. The method is first applied to a small, previously manually repicked subset of the catalogue so that we may compare our results to those obtained from painstaking, visual, cross-correlation-based techniques. Relative centroid-adjusted hypocenters show a decrease in median mislocation from 31 to 7 m for preliminary and automatically adjusted picks, respectively, compared to the manual results. Narrow, intersecting joint features not observed in the preliminary hypocenter cloud, but revealed through manual repicking, are also recovered using the automatic method. We then address a larger catalogue of ~7000 microearthquakes. After relocating the events using automatic repicks, the percentage of events clustering within 5 m of their nearest neighbor increases form 5 to 26% of the catalogue. Hypocenter relocations delineate narrow, linear features previously obscured within the seismic cloud, interpreted as faults or fractures which may correspond to fluid propagation paths, or to changes in stress as a result of elevated pore pressures. RMS travel-time residuals for the larger data set are reduced by only 0.2%; however, phase-pick biases in the preliminary catalogue have influenced both the velocity model and station correction calculations, which will affect location residuals. These pick biases are apparent on the adjusted, stacked waveforms and correcting them will be important prior to future velocity model refinements.
机译:一种自动的,基于相关性的,基于相关性的自适应算法,可在大型数字地震数据集中调整相位拾取,从而仅需一小部分时间和人力即可重新改善微震结构的分辨率,而人工和半波形重新分析所需的时间和人力却很小自动。我们将此技术应用于法国Soultz-sous-Forets地热站点的诱发地震活动。该方法首先应用于以前手工挑选的一小部分目录,以便我们可以将我们的结果与通过艰苦的,基于视觉的,基于互相关的技术获得的结果进行比较。与手动结果相比,相对质心调整后的震源分别将初次和自动调整的镐的中位错位从31 m减少到7 m。初步的震中云中未观察到的狭窄,相交的关节特征,但通过手动拾取显示出来,也可以使用自动方法恢复。然后,我们介绍了约7000种微地震的较大目录。使用自动重新定位重新定位事件后,聚集在其最近邻居附近5 m内的事件的百分比从目录的5%上升到26%。震源重定位描绘了以前在地震云中被遮盖的狭窄的线性特征,被解释为可能对应于流体传播路径或由于孔隙压力升高而引起的应力变化的断层或裂缝。较大数据集的RMS传播时间残差仅减少了0.2%;但是,初步目录中的相位拾取偏差已经影响了速度模型和测站校正计算,这将影响位置残差。这些拾取偏差在调整后的叠加波形上很明显,因此在将来对速度模型进行改进之前,对其进行校正非常重要。

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