Abstract
Full-waveform inversion is an important and widely used method to reconstruct subsurface velocity images. Waveform inversion is a typical nonlinear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle-skipping and local-minima issues, and do not mention that solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform-inversion problem. However, most deep-learning frameworks suffer from the generalization and overfitting issue. In this article, we developed a real-time data-driven technique and we call it VelocityGAN, to reconstruct accurately the subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end to end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoder–decoder-based data-driven seismic waveform-inversion approaches, our VelocityGAN learns regularization from data and further imposes the regularization to the generator so that inversion accuracy is improved. We further develop a transfer-learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments is conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with the existing physics-driven approaches and data-driven frameworks but also conduct several transfer-learning experiments. The experimental results show that VelocityGAN achieves the state-of-the-art performance among the baselines and can improve the generalization results to some extent.
Highlights
S Eismic full-waveform inversion techniques are commonly used in geophysical exploration to determine site geology, stratigraphy, and rock quality
We observe that VelocityGAN with a combination of mae and mse loss can get better prediction results than a single loss
Though the VelocityGAN with a single loss achieve relatively higher scores in some measurements such as rel, acc. (t=1.10), the VelocityGAN with a combination of mae and mse loss obtains a better tradeoff under all the metrices
Summary
S Eismic full-waveform inversion techniques are commonly used in geophysical exploration to determine site geology, stratigraphy, and rock quality. It costs O(n3) to obtain the gradient, provided with a 2-D n × n subsurface model To mitigate those aforementioned issues, many regularization approaches have been proposed and developed in recent years, which includes Tikhonov-like regularization [5, 16, 34], total-variation regularization [2, 11, 24, 25, 26], high-order regularization techniques [42], and prior-based methods [30, 52]. Our GAN-based inverse problem model is an end-to-end framework which is similar to image-to-image translation problem from computer vision[9, 18], which means the velocity map can be output in real-time once the training is completed.
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