A Genetic-Evolutionary ADAM (G-ADAM) optimizer for semisupervised convolutional neural network-based acoustic impedance inversion

Recent developments in deep learning have shown significant improvement in the accuracy of acoustic impedance inversion results. However, the conventional gradient-based optimizers such as root mean square propagation (RMSProp), momentum, adaptive moment estimation (ADAM), etc., used in the deep learning framework, inherently tend to converge at the nearest optimum point, thereby compromising the solution by not attaining the global minimum. We apply a hybrid global optimizer, genetic-evolutionary ADAM (GADAM) to address the issue of convergence at a local optimum in a semi-supervised deep sequential convolution network-based learning framework to solve the non-convex seismic impedance inversion problem. GADAM combines the advantages of adaptive learning of ADAM and genetic evolution of genetic algorithm (GA), which facilitates faster convergence, and avoids sinking into the local minima. The efficacy of GADAM is tested on synthetic benchmark data and field examples. The results are compared with that obtained from a widely used ADAM optimizer and conventional least-squares method. In addition, uncertainty analysis is performed to check the implication of the optimizer's choice in obtaining efficient and accurate seismic impedance values. Results show that the level of uncertainty and minima of loss function attained using the GADAM optimizer are comparatively lower than that for ADAM. Thus, the present study demonstrates that the hybrid optimizer, i.e., GADAM is more efficient than the extensively used ADAM optimizer in impedance estimation from seismic data in a deep learning framework.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Genetic-Evolutionary ADAM (G-ADAM) optimizer for semisupervised convolutional neural network-based acoustic impedance inversion

Cited by 3 publications

References 6 publications

A comprehensive review of seismic inversion based on neural networks

A comprehensive review of seismic inversion based on neural networks

Deep Learning Accelerated Blind Seismic Acoustic-Impedance Inversion

Genetic-evolutionary adaptive moment estimation-based semisupervised deep sequential convolution network for seismic impedance inversion: Application and uncertainty analysis

Contact Info

Product

Resources

About