Application of active-source full waveform inversion in multi-resource exploration in Juyanhai depression of Inner Mongolia
Full waveform inversion(FWI)is a high-resolution tomography method based on waveform matching,which has been increasingly used in practical applications in near-surface high-resolution imaging in recent years.However,due to the strong nonlinear characteristics caused by cycle skipping and other factors,FWI is highly dependent on the initial model and the quality of seismic data.Large difference between the initial model and the actual subsurface structure,the noise in the data and lack of low-frequency contents can all result in convergence into local minima in the inversion process.Currently,FWI is primarily used for processing marine data,and it still faces significant challenges when applied to short-offset land data.The Juyanhai depression region in Inner Mongolia has a large amount of coal,oil and gas resources,with substantial potentials for exploration and development.However,due to its complex geological conditions and obscure structural formations,it is urgent to carry out high-resolution subsurface imaging with great accuracy for detailed research.In this study,using controlled-source seismic exploration data from this region,we apply FWI with multiple objective functions to obtain an accurate P-wave velocity structure for depth down to 2 km.The accuracy of the velocity model is verified through waveform comparisons,and Kirchhoff pre-stack depth migration is carried out based on the velocity model obtained by waveform inversion,to obtain a high-resolution depth domain reflection profile.The imaging results nicely reveal the four main sedimentary strata in the study area,and provide crucial foundations for further research in the region.This study also offers a reliable high-resolution inversion workflow for active-source explorations in different tectonically active regions in China by sharing an open-sourced software package.
Full waveform inversionLand active source explorationPre-stack depth migrationJuyanhai depression
万方数据
维普
