Abstract
Ocean bottom seismometer (OBS) can record both pressure and displacement data by modern marine seismic acquisitions with four-component (4C) sensors. Elastic full-waveform inversion (EFWI) has shown to recover high-accuracy parameter models from multicomponent seismic data. However, due to limitation of the standard elastic wave equation, EFWI can hardly simulate and utilize the pressure components. To remedy this problem, we propose an elastic full-waveform inversion method based on a modified acoustic-elastic coupled (AEC) equation. Our method adopts a new misfit function to account for both 1C pressure and 3C displacement data, which can easily adjust the weight of different data components and eliminate the differences in the order of magnitude. Owing to the modified AEC equation, our method can simultaneously generate pressure and displacement records and avoid explicit implementation of the boundary condition at the seabed. Besides, we also derive a new preconditioned truncated Gauss–Newton algorithm to consider the Hessian associated with ocean bottom seismic 4C data. We analyze the multiparameter sensitivity kernels of pressure and displacement components and use two numerical experiments to demonstrate that the proposed method can provide more accurate multiparameter inversions with higher resolution and convergence rate.
Highlights
Ocean bottom seismic survey is a modern platform for exploring the Earth’s interior, locating seismometers at the seabed for all-weather, long-term, continuous, real-time observations
We propose a new elastic full-waveform inversion (EFWI) method based on a modified acoustic-elastic coupled (AEC) equation, which can reconstruct multiple elastic parameters from Ocean bottom seismometer (OBS) 4C data
We first use the Overthrust model to demonstrate the effectiveness of the proposed preconditioned truncated Gauss–Newton (PTGN) algorithm for OBC 4C data
Summary
Ocean bottom seismic survey is a modern platform for exploring the Earth’s interior, locating seismometers at the seabed for all-weather, long-term, continuous, real-time observations. The observed multicomponent data contain plenty of elastic properties of subsurface media, which can be used to deduct the lithology, fluid content, and pore pressure of rocks [2]. As governed by the elastic wave equation, EFWI can interpret multiple elastic wave phenomena, i.e., wave-mode conversion and AVO effects [5], and provide quantitative estimations for subsurface parameter distributions. It costs a large number of computing resources in wavefield simulations, its excellent performance still makes it more and more attractive [6,7,8,9,10]
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