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Cooperative joint inversion of 3D seismic and magnetotelluric data: With application in a mineral province

地质学 大地电磁法 岩石物理学 地震反演 反演(地质) 地震学 钻孔 地震模拟 地形 矿产勘查 合成地震记录 接头(建筑物) 地球物理学 电阻率和电导率 方位角 岩土工程 构造学 地图学 物理 天文 地理 电气工程 工程类 建筑工程 多孔性
作者
Eric M. Takam Takougang,Brett Harris,Anton Kepic,Cường Văn Anh Lê
出处
期刊:Geophysics [Society of Exploration Geophysicists]
卷期号:80 (4): R175-R187 被引量:26
标识
DOI:10.1190/geo2014-0252.1
摘要

The integration of different geophysical data has the potential to provide more accurate estimate of subsurface rock properties. Several methodologies and attempts have been developed over the years with the objective of reducing exploration risk. We have developed a cooperative joint-inversion approach intended to facilitate recovery of acoustic impedance (AI) using seismic and magnetotelluric (MT) data. In this approach, the MT data provided a pathway for iteratively building large-scale low-frequency information content not directly recoverable from the seismic data themselves. The MT data provided complementary information to seismic, especially in seismically complex terrains such as overthrust belts, subbasalt and subsalt, carbonate reefs or for targets below deep cover containing limestone, concretionary layers, or basalt. On the other hand, the seismic data provided structural information necessary to derive accurate resistivity models from MT inversion and small-scale features during seismic impedance inversion. The connections between resistivity and the elastic property of rocks are obtained from petrophysical relationships derived from available borehole data, or if not available, from empirical relationships. We tested our technique on synthetic and field data. The application of cooperative joint inversion to 3D seismic and MT data sets acquired in a mineral province made it possible to recover AI distribution across a wide range of geologic environments. The resulting rock property images provided a direct link to geology that is exceedingly difficult, if not impossible, to extract from the individual data sets.

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