Machine learning methods modeling waveform, multi-parameter full waveform inversion, and uncertainty quantification

Full waveform inversion (FWI) is a potent technology capable of estimating subsurface parameters using seismic records. However, several challenges hinder its widespread application in large-scale field operations. In this thesis, I introduce strategies that leverage machine learning techniques to address the challenges faced with FWI, making FWI more feasible to implement in complex media and providing a confidence analysis for the inversion results. A primary concern is the necessity for accurate initial models in FWI; without these, FWI risks becoming ensnared in local minima. In this thesis, I propose recurrent neural network (RNN) isotropic elastic FWI. Then, I proposed the elastic implicit full waveform inversion. Instead of directly updating the elastic parameters like in the conventional FWI, I use neural networks to generate elastic models and update the weights in the neural network to decrease data misfits. Furthermore, how to develop a feasible uncertainty quantification method for FWI out- comes remains an open question. The estimation of the prior uncertainty and a method that effectively evaluates the inverse Hessian matrix are critical steps for the uncertainty quantification under the Bayesian inference. I introduce a method that uses the Bayesian neural network (BNN) to provide prior uncertainty for the elastic models and an algorithm that efficiently approximates inverse Hessian. During the inversion process, simplifications in wave propagation physics are often made to enhance computational efficiency. Yet, the extent to which such simplifications (mod- elling errors) impact the inversion results is seldom addressed. I proposed a method for the viscoelastic FWI that can quantify a specific type of modelling error, which can quantify the modelling error caused by the insufficient ability of the relaxation variables to model the constant Q model or a Q model that we desire, i.e., obtained from the field records. I also analyze the effect on the inversion results of such modelling errors. Our community is strongly motivated to find means of accurately computing wavefields with minimal computational expense. I propose the one-connection Fourier neural operator (OCFNO). I test the ability of such a network to “learn” to solve elastic wave equations. Computational speed-ups are significant.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Geoscience

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2024

Author dc:creator

• Zhang, Tianze

Advisor dc:contributor.advisor

• Innanen, Kristopher

Committee members dc:contributor.committeemember

• Trad, Daniel
• Dettmer, Jan
• Karchewski, Brandon
• Zhu, Tieyuan

• Full Waveform inversion
• machine learning
• uncertainty quantification

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

en

DOI

https://doi.org/10.11575/PRISM/42714

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/117871