Deep Learning‐BasedH‐κMethod (HkNet) for Estimating Crustal Thickness andVp/VsRatio From Receiver Functions

Abstract

A receiver function (RF) is the response of the Earth's structure below a seismometer to an incident teleseismic wave and consists of a series of P-to-S (Ps) or S-to-P (Sp) converted waves generated at structural interfaces (Langston, 1979), mainly those generated by the velocity discontinuities in the crust and upper mantle below the seismometer. Crustal thickness (H) and P-wave and S-wave velocity ratio (κ) are important parameters reflecting the crustal structure and internal material composition and can provide an important basis for regional tectonics and dynamics. Zhu and Kanamori (2000) noted that when the Moho converted phase Ps and crustal multiple waves are considered at the same time, H and κ can be estimated under the assumption of the average crustal P wave velocity. This is currently the most commonly used H-κ method. The H-κ algorithm stacks the amplitudes of RFs at predicted arrival times for Ps and crustal multiples (PpPs and PpSs + PsPs; here referred to as M1 and M2, respectively, for convenience, as in J. Li et al., 2019) for different values of H and κ in a grid search.The assumptions of the H-κ method are very simple, which leads to its limitations. It assumes that the crustal medium is isotropic, the Moho surface is horizontal, and the P-wave velocity of the crust is fixed. However, the above assumptions cannot be satisfied under complex geological settings, so the results of the H-κ method may be inaccurate. The Chinese continent is known to have highly heterogeneous crustal structure and extremely variable Moho depth, as revealed for example, by seismic tomography (

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

“…Most studies in this category design deep neural networks that are capable to capture the complex transformation from the measured data space to the desired model parameter space, train these machines using paired models and their corresponding synthetic data, and apply the trained machines to field datasets. Applications of such a framework range across the whole spectrum of geophysical inverse problems, including surface wave dispersion inversion and tomography (Cai et al., 2022; X. Zhang & Curtis, 2021), seismic‐to‐petrophysics inversion (Xiong et al., 2021; C. Zou et al., 2021), crustal thickness and Vp / Vs estimation from receiver functions (F. Wang et al., 2022), earthquake and microseismicity moment tensor inversion (Chen et al., 2022; Steinberg et al., 2021), magnetic, gravity, and ground‐penetrating radar (GPR) data inversion (R. Huang et al., 2021; Leong & Zhu, 2021; Nurindrawati & Sun, 2020), and thermal evolution estimation for Mars (Agarwal et al., 2021). Y. Wu et al.…”

“…Zhang & Curtis, 2021), seismic-to-petrophysics inversion (Xiong et al, 2021;C. Zou et al, 2021), crustal thickness and Vp/Vs estimation from receiver functions (F. Wang et al, 2022), earthquake and microseismicity moment tensor inversion (Chen et al, 2022;Steinberg et al, 2021), magnetic, gravity, and ground-penetrating radar (GPR) data inversion (R. Leong & Zhu, 2021;Nurindrawati & Sun, 2020), and thermal evolution estimation for Mars (Agarwal et al, 2021). Y.…”

Seismically Informed Reference Models Enhance AI‐Based Earthquake Prediction Systems

“…With the exponential increase in digital data volumes and astounding progress in the developments of artificial intelligence (AI), artificial neural network (ANN) models, which are essentially data‐driven and computer‐based models, open new opportunities for developments and applications in Geosciences, including geophysics. This powerful go‐to technique has been applied to many problems including predictions of the magnetization directions of magnetic source (Nurindrawati & Sun, 2020), detection of volcanic surface deformation (Sun et al., 2020), seismic inversion (Chen & Saygin, 2021), identification of faults (Granat et al., 2021; Mattéo et al., 2021; Vega‐Ramírez et al., 2021), prediction of marine sediment density (Graw et al., 2021), inversion of gravity data (Huang et al., 2021), inversion of Ground Penetrating Radar Date (Leong & Zhu, 2021), prediction of geothermal heat flow (Lösing & Ebbing, 2021), estimation of seismic moment tensors (Steinberg et al., 2021), classification of weather phenomenon (Xiao et al., 2021), declustering of earthquake catalogs (Aden‐Antoniów et al., 2022), microseismic monitoring (Chen et al., 2022), seismic phase picking (Feng et al., 2022; Lapins et al., 2021), monitoring fracture saturation (Nolte & Pyrak‐Nolte, 2022), estimating crustal thickness and Vp/Vs ratio (Wang et al., 2022), and so on. There is a collection of machine learning for solid earth observation, modeling, and understanding for the Journal of Geophysical Research: Solid Earth, which is available at https://agupubs.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)2169-9356.MACHLRN1.…”

Fault2SeisGAN: A method for the expansion of fault datasets based on generative adversarial networks

“…In recent years, deep learning technology has been developed rapidly and applied in various fields. In contrast to traditional model-driven methods, deep learning is data-driven and has been well applied by geophysicists in various branches including end-to-end seismic data denoising (Herrmann and Hennenfent, 2008;Zhang et al, 2017;Yu et al, 2019;Zhu et al, 2019), missing data recovery and reconstruction (Mandelli et al, 2018;Wang et al, 2019;Wang et al, 2020), first arrival picking (Wu et al, 2019a;Hu et al, 2019;Yuan et al, 2020), deeplearning velocity inversion (Araya-Polo et al, 2018;Adler et al, 2019;Cai et al, 2022), deep-learning seismology inversion (Wang et al, 2022) and fault interpretation (Wu et al, 2019c;Wu et al, 2019d;Cunha et al, 2020;Yang et al, 2022).…”