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基于模型驱动的煤层强反射能量衰减方法 被引量:6

Model-driven energy attenuation method for coal seam strong reflection
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摘要 地震数据中煤层反射波具有能量强的特征,使邻近地层的反射被煤层反射所掩盖,导致邻近层难以成像,岩性解释工作存在困难。针对此问题,提出基于模型驱动的煤层强反射能量衰减方法。利用测井数据和煤矿井下钻孔实测的煤厚数据与煤层反射振幅绘制交会图,拟合振幅与厚度的关系式,构建数字模型并模拟煤层反射,在振幅残差阈值的控制下迭代求取全区煤层厚度,使用求取的模型煤层反射数据对实际数据中的煤层振幅进行衰减。通过模型试算表明,该方法在理论上具有可行性;通过对鄂尔多斯市某煤矿地震数据中3-1号煤层反射压制的应用表明,该方法对实际数据中的煤层反射压制效果良好,煤层附近的弱反射层成像质量得到提升,为岩性解释提供基础数据。 The reflection of coal seam in seismic data has the characteristics of strong energy,which makes the reflection of adjacent strata often concealed by the reflection of coal seam.This makes it difficult to image the adjacent weak layer and lithological interpretation difficult.Aiming at this problem,a model-driven energy attenuation method for coal seam strong reflection was proposed.This method uses the coal thickness calculated by logging and measured by borehole in underground coal mine to construct cross plot with reflection amplitude of coal seam,and fit the relationship formula between amplitude and thickness.The digital model was constructed and the reflection of coal seam was simulated.The thickness of coal seam in the whole area was calculated iteratively by using the threshold value of amplitude residual.The strong amplitude of coal seam in the actual data was attenuated by using the data simulated by the model.The experimental results of the model data shows that the method is feasible in theory;The suppression effect of the strong reflection of coal seam 3–1 in the seismic data of a coal mine in Ordos shows that the practical application effect was good,and the imaging quality of weak reflection layer near the coal seam was improved,which provides the basic data for the lithological interpretation.
作者 张宪旭 ZHANG Xianxu(Xi’an Research Institute Co.Ltd.,China Coal Technology and Engineering Group Corp.,Xi’an 710077,China)
出处 《煤田地质与勘探》 CAS CSCD 北大核心 2020年第3期188-194,共7页 Coal Geology & Exploration
基金 中煤科工集团西安研究院有限公司科技创新基金项目(2019XAYMS28)。
关键词 模型驱动 强能量衰减 煤厚预测 相干性叠加 井下钻孔数据 model-driven strong energy attenuation coal thickness prediction coherence stack underground borehole data
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