Article
Geochemistry & Geophysics
Scott D. Keating, Kristopher A. Innanen
Summary: Prior knowledge in seismic inversion can improve the accuracy of models compared to using seismic data alone. This study proposes an optimization strategy for full waveform inversion (FWI) that incorporates global regularization information and allows models to 'tunnel' between basins to honor prior information.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geosciences, Multidisciplinary
Junxiao Li, Herurisa Rusmanugroho, Mahesh Kalita, Kefeng Xin, Farah Syazana Dzulkefli
Summary: Seismic imaging and inversion face challenges when dealing with salt structures. Conventional state-of-the-art full-waveform inversion (FWI) fails to recover salt features. A widely used solution involves human interpretation. This study introduces a regularized isotropic FWI to recover the top parts of salt bodies and an automatic salt flooding to reconstruct deeper parts. An anisotropic FWI is used to update the velocity model and improve the accuracy. The approach showed satisfying results in both synthetic and field datasets.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Geochemistry & Geophysics
J. H. E. de Jong, H. Paulssen, T. van Leeuwen, J. Trampert
Summary: Receiver functions have long been used to study Earth's major discontinuities. The traditional assumptions for mapping locations in the subsurface have been found to have limitations, but the use of adjoint tomography provides a potential solution. Sensitivity kernels for P-to-S converted waves have been calculated, revealing differences in sensitivity to P-wave speed and S-wave speed. The well-known trade-off between depth of the discontinuity and wave speed has also been observed, but can be significantly reduced by considering longer waveforms that include more surface reverberations.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Environmental Sciences
Pan Zhang, Ru-Shan Wu, Liguo Han, Yixiu Zhou
Summary: In this study, the elastic direct envelope inversion with anisotropic total variation constraint (EDEI-ATV) is proposed to improve the inversion effects of inner velocity and bottom boundaries of strong scatterers. The use of anisotropic total variation (ATV) constraint during iterations helps achieve uniform velocity inside the layer and sharper boundaries. Reliable large-scale Vp and Vs structures are obtained using this method. The effectiveness of the proposed method is verified through numerical examples.
Article
Geochemistry & Geophysics
Youngjae Shin, Ju-Won Oh, Dong-Joo Min
Summary: The pressure-based acoustic approximation of the elastic wave equations in anisotropic media has advantages, but the numerical scattering potentials are inconsistent with the elastic scattering theory. Choosing a suitable parameterization is important for successful anisotropic parameter estimation in multiparameter FWI. The proposed method addresses the issue of inaccurate scattered wavefields by combining pressure- and vector-based acoustic wave equations.
Article
Geochemistry & Geophysics
Weiguang He, Guanghui Hu, Bing Zhang
Summary: The conventional least squares full waveform inversion (FWI) lacks coherency along the spatial and temporal axes. This study proposes an optimal matching function that represents each receiver using extended vector attributes and calculates a cost matrix to determine receiver pairs. The algorithm avoids point-by-point comparison and reduces computation, and has been extensively validated in multiple models.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Tong Zhou, Jiaqi Li, Ziyi Xi, Guoliang Li, Min Chen
Summary: This study presents a new shear wave speed model of the upper mantle in the contiguous US and surrounding regions, revealing the tectonic history and interactions between subducting slabs and cratons. The model exhibits clear shear wave speed anomalies correlating with tectonic units, including the North America Craton.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2022)
Article
Geochemistry & Geophysics
T. M. Irnaka, R. Brossier, L. Metivier, T. Bohlen, Y. Pan
Summary: In this study, we investigate the methodological development and application of viscoelastic full waveform inversion on a multicomponent data set at the near-surface scale. Our results demonstrate the successful reconstruction of P-wave and S-wave velocities, as well as the precise location of an ancient trench buried at a depth of 1 meter underground.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Chemistry, Analytical
Ju Ma, Shuang Wu, Yuan Zhao, Guoyan Zhao
Summary: Precise stochastic approaches can quantitatively calculate source uncertainties and eliminate the influence of anisotropy on moment tensor inversion. Results show that anisotropy and noise have a minimal effect on fault plane rotation for a pure shear source. Complex sources are more sensitive to velocity models and full moment tensor solutions exhibit larger fault plane rotation than double couple solutions. Collaborative inversion improves fault plane solution accuracy and reduces spurious components.
Article
Geochemistry & Geophysics
Tingting Liu, Thomas Bohlen
Summary: Full-waveform inversion (FWI) is a high-resolution imaging technique to recover geophysical parameters of the elastic subsurface from seismic signals. However, it is less accurate for estimating subsurface material properties with elastic constraints, especially for near-surface structures containing fluids. In this work, we propose a 2-D time-domain poroelastic FWI (PFWI) algorithm to capture the physical mechanism in the shallow subsurface by applying fluid-saturated poroelastic equations. We derive scattered P-SV&SH wavefields and Frechet kernels to analyze the sensitivities of the objective function to different poroelastic parameters, and verify the accuracy through model parameter reconstructions and numerical tests.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Dirk Philip van Herwaarden, Michael Afanasiev, Solvi Thrastarson, Andreas Fichtner
Summary: We propose a new approach to full-waveform inversion that allows for continuous assimilation of growing data volumes without the need to reinvert all data. Specifically designed for seismological applications, our method utilizes a dynamic mini-batch stochastic L-BFGS to sequentially add new data while maintaining convergence and consistency in model fit measurement.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geochemistry & Geophysics
Scott D. Keating, Kristopher A. Innanen
Summary: Full-waveform inversion (FWI) is an effective tool for recovering subsurface information, but it is subject to uncertainty due to noise in measurements and the approximate nature of numerical optimization techniques. The nonuniqueness of FWI solutions contributes to uncertainty, but focusing on specific aspects of uncertainty can reduce the dimensionality of the problem. Targeted uncertainty quantification, characterizing confidence in specific features of the subsurface model, can effectively address uncertainty associated with incomplete numerical optimization.
Article
Engineering, Multidisciplinary
Min Zhu, Shihang Feng, Youzuo Lin, Lu Lu
Summary: Full waveform inversion (FWI) uses seismic waveform data to infer subsurface structure information. We propose a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI, which can handle varying source frequencies and locations. Compared with existing data-driven FWI methods, Fourier-DeepONet achieves more accurate predictions of subsurface structures under diverse source parameters.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Acoustics
Yu Yuan, Yue Zhao, Nuomin Zhang, Yang Xiao, Jing Jin, Naizhang Feng, Yi Shen
Summary: The objective of this work was to incorporate the spatial shapes of the transducer elements into the framework of the full-waveform inversion. The elements are modeled as their cross-sections in the 2-D imaging plane, instead of simple point sources on their surface, to avoid staircasing artifacts. The approach helps reduce errors, increase the structural similarity of reconstructed images, and improve convergence stability and speed.
ULTRASOUND IN MEDICINE AND BIOLOGY
(2023)
Article
Geochemistry & Geophysics
Jian Sun, Kristopher Innanen, Tianze Zhang, Daniel Trad
Summary: Full waveform inversion (FWI) is a state-of-the-art method for imaging subsurface structures and physical parameters with seismic data, but it faces challenges in implementation and use. The implicit full waveform inversion (IFWI) algorithm, designed with deep neural representations, shows improved convergence and the ability to capture high-resolution subsurface structures. Although uncertainty analysis is not fully solved, IFWI addresses it meaningfully by approximating Bayesian inference. Numerical experimentation suggests that IFWI has a strong capacity for generalization and is suitable for multi-scale joint geophysical inversion.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2023)
Article
Geochemistry & Geophysics
Hanchen Wang, Tariq Alkhalifah, Umair bin Waheed, Claire Birnie
Summary: The microseismic monitoring technique is widely used in studying hydraulic fracturing. In this study, a deep convolutional neural network is proposed to predict the event location of microseismic data. The results demonstrate that the proposed approach provides accurate and efficient microseismic event localization.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yuanyuan Li, Tariq Alkhalifah
Summary: Characterizing the elastic properties of deep-buried reservoirs beneath complex overburden structures is a challenging task for seismic inversion. Elastic full-waveform inversion (FWI) can quantitatively estimate subsurface elastic properties with high resolution, but using high frequencies is computationally expensive and obtaining high-resolution inversion results for deep targets is difficult due to complex overburden structures. To address these limitations, a target-oriented high-resolution elastic FWI scheme is proposed, using estimated elastic data for a virtual survey deployed above a zone of interest.
Article
Geochemistry & Geophysics
Xinquan Huang, Tariq Alkhalifah
Summary: Seismic wave-equation based methods and physics-informed neural network (PINN) have great potential in illuminating the interior of Earth. However, their accuracy and training cost are limited when dealing with high-frequency wavefields. Therefore, a novel approach using frequency upscaling and neuron splitting is proposed to improve the accuracy and convergence speed of wavefield solutions.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2022)
Article
Geosciences, Multidisciplinary
Bingbing Sun, Tariq Alkhalifah
Summary: A robust misfit function is crucial for stable velocity model updates in full-waveform inversion. We propose ML-misfit, a machine learning-based approach, to learn a data-adaptive misfit function. The neural network architecture is designed to allow for global comparison of the predicted and measured data, guaranteeing efficient training. By training the network using a meta-learning framework, the ML-misfit automatically improves and provides robust updating of the velocity model.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Geochemistry & Geophysics
Haoran Zhang, Tariq Alkhalifah, Yang Liu, Claire Birnie, Xi Di
Summary: Seismic resolution enhancement is crucial for subsurface structure characterization. We propose a simple domain adaptation procedure called MLReal-Lite, which improves the performance of neural networks by bringing the distributions of real and synthetic data closer to each other through linear operations.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geosciences, Multidisciplinary
Claire Birnie, Tariq Alkhalifah
Summary: Noise is a common issue in seismic data, affecting the performance of supervised deep learning denoising. Self-supervised blind-spot networks can train directly on raw noisy data but struggle with correlated noise. We propose initial supervised training on synthetic data followed by self-supervised fine-tuning on field data, resulting in improved denoising performance.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Geosciences, Multidisciplinary
Denis Anikiev, Claire Birnie, Umair bin Waheed, Tariq Alkhalifah, Chen Gu, Dirk J. Verschuur, Leo Eisner
Summary: The combination of enhanced big data handling capabilities, improved instrumentation density and quality, and rapid advances in machine learning algorithms has opened the door for significant progress in Earth Sciences. Machine learning methods are increasingly gaining attention in the seismic community, particularly in microseismic monitoring where they have the potential to revolutionize real-time processing. Recent developments in microseismic monitoring have shown a strong trend towards utilizing machine learning techniques to enhance passive seismic data quality, detect microseismic events, and locate their hypocenters. Additionally, machine learning methods are being adopted for advanced event characterization and seismic velocity inversion, providing valuable by-products such as uncertainty analysis and data statistics. Future trends in machine learning utilization point towards its application on distributed acoustic sensing (DAS) data and real-time monitoring to handle the large amount of data acquired in these cases.
EARTH-SCIENCE REVIEWS
(2023)
Article
Multidisciplinary Sciences
Mohammad H. Taufik, Umair bin Waheed, Tariq A. Alkhalifah
Summary: We developed a neural network-based method for global traveltime computation, which can efficiently handle a large number of receivers provided by DAS arrays. This method allows rapid and accurate estimation of seismic wave propagation time, making it an essential tool for advancing seismological studies.
SCIENTIFIC REPORTS
(2023)
Article
Chemistry, Analytical
Donglin Zhu, Lei Fu, Vladimir Kazei, Weichang Li
Summary: This study pioneers the use of diffusion models for denoising DAS-VSP data, demonstrating their effectiveness in removing noise and surpassing conventional methods, and showcasing the potential of diffusion models in DAS processing.
Article
Geochemistry & Geophysics
Shijun Cheng, Xingchen Shi, Weijian Mao, Tariq A. Alkhalifah, Tao Yang, Yuzhu Liu, Heping Sun
Summary: The ocean-bottom node (OBN) seismic acquisition system aims to improve imaging quality through a deep learning-based method using a multiscale convolutional neural network (Ms-CNN) for sparse data acquisition. The Ms-CNN is trained to map sparse data images to dense data images, allowing for direct processing and improving event continuity and noise reduction in migration results.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Bingbing Sun, Tariq Alkhalifah
Summary: Due to the nature of the Earth's layering and conventional seismic wavelength, seismic waves often experience significant anisotropy in many subsurface areas, particularly vertical transverse isotropy (VTI). Inverting to this type of Earth model using waveforms poses challenges such as nonlinearity and parameter tradeoff. The optimal transport of the matching filter (OTMF) has been introduced as a robust misfit function for full-waveform inversion (FWI). Applying a VTI FWI with OTMF misfit on offshore Australian data yields geologically meaningful models and demonstrates the effectiveness of OTMF in mitigating cycle skipping.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Tariq Alkhalifah, Xinquan Huang
Summary: Imaging is a crucial task in various fields, and the exploding reflector assumption provides a direct imaging approach for zero-offset data. However, aliasing problems arise when the data are coarsely sampled. By formulating the frequency-domain wavefield as a neural network function, the physics-informed neural network framework enables subsurface imaging.
2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP
(2022)
Article
Computer Science, Artificial Intelligence
Tariq Alkhalifah, Oleg Ovcharenko
Summary: In this paper, a direct domain adaptation (DDA) approach is proposed to enhance the training of supervised neural networks on synthetic data by incorporating features from real-world data. The experimental results show that this method can improve the accuracy of the network and the input features from the source and target domains share similarities along the principal components after specific transformations.
FRONTIERS IN ARTIFICIAL INTELLIGENCE
(2022)
Article
Geochemistry & Geophysics
Mohammad Hasyim Taufik, Umair bin Waheed, Tariq A. A. Alkhalifah
Summary: The eikonal equation is important in various scientific and engineering fields, particularly in seismic wave propagation and travel time modeling. Traditional finite-difference methods suffer from numerical inaccuracies and high computational costs, while physics-informed neural networks offer higher accuracy and scalability. This article demonstrates the flexibility and interpolation-extrapolation capability of PINN solutions, particularly in handling gaps in velocity models.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Randy Harsuko, Tariq A. Alkhalifah
Summary: This paper presents a dataset-centric framework called StorSeismic for seismic data processing, which utilizes a neural network (NN) for preprocessing to extract and store geometrical features of the data. By pretraining and fine-tuning, this framework can be used for various seismic processing tasks such as denoising and velocity estimation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)