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首页> 外文期刊>Journal of Applied Geophysics >Regional and residual anomaly separation in microgravity maps for cave detection: The case study of Gruta de las Maravillas (SW Spain)
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Regional and residual anomaly separation in microgravity maps for cave detection: The case study of Gruta de las Maravillas (SW Spain)

机译:微重力地图中用于探洞的区域和残留异常分离:Gruta de las Maravillas的案例研究(西班牙西南部)

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摘要

Gravity can be considered an optimal geophysical method for cave detection, given the high density contrast between an empty cavity and the surrounding materials. A number of methods can be used for regional and residual gravity anomaly separation, although they have not been tested in natural scenarios. With the purpose of comparing the different methods, we calculate the residual anomalies associated with the karst system of Gruta de las Maravillas whose cave morphology and dimensions are well-known. A total of 1857 field measurements, mostly distributed in a regular grid of 10 x 10 m, cover the studied area. The microgravity data were acquired using a Scintrex CG5 gravimeter and topography control was carried out with a differential GPS. Regional anomaly maps were calculated by means of several algorithms to generate the corresponding residual gravimetric maps: polynomial first-order fitting, fast Fourier transformation with an upward continuation filter, moving average, minimum curvature and kriging methods. Results are analysed and discussed in terms of resolution, implying the capacity to detect shallow voids. We propose that polynomial fitting is the best technique when microgravity data are used to obtain the residual anomaly maps for cave detection. (C) 2015 Elsevier B.V. All rights reserved.
机译:考虑到空洞与周围材料之间的高密度对比,重力可以被认为是探洞的最佳地球物理方法。尽管尚未在自然场景中进行测试,但仍有许多方法可用于区域和残余重力异常分离。为了比较不同方法,我们计算了与洞穴形貌和尺度众所周知的Gruta de las Maravillas岩溶系统相关的残余异常。总共1857个现场测量值覆盖了研究区域,其中大多数分布在10 x 10 m的规则网格中。使用Scintrex CG5重力仪获取微重力数据,并使用差分GPS进行形貌控制。通过几种算法来计算区域异常图,以生成相应的残留重力图:多项式一阶拟合,具有向上连续滤波器的快速傅立叶变换,移动平均,最小曲率和克里金法。分析结果并按照分辨率进行讨论,这暗示了检测浅层空隙的能力。我们建议多项式拟合是使用微重力数据获取残余异常图进行洞穴检测时的最佳技术。 (C)2015 Elsevier B.V.保留所有权利。

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