Learned frequency-domain scattered wavefield solutions using neural operators
arxiv(2024)
摘要
Solving the wave equation is essential to seismic imaging and inversion. The
numerical solution of the Helmholtz equation, fundamental to this process,
often encounters significant computational and memory challenges. We propose an
innovative frequency-domain scattered wavefield modeling method employing
neural operators adaptable to diverse seismic velocities. The source location
and frequency information are embedded within the input background wavefield,
enhancing the neural operator's ability to process source configurations
effectively. In addition, we utilize a single reference frequency, which
enables scaling from larger-domain forward modeling to higher-frequency
scenarios, thereby improving our method's accuracy and generalization
capabilities for larger-domain applications. Several tests on the OpenFWI
datasets and realistic velocity models validate the accuracy and efficacy of
our method as a surrogate model, demonstrating its potential to address the
computational and memory limitations of numerical methods.
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