Article
Geochemistry & Geophysics
Donggeon Kim, Jongha Hwang, Dong-Joo Min, Ju-Won Oh, Tariq Alkhalifah
Summary: Full waveform inversion (FWI) is a highly non-linear optimization problem that aims to reconstruct high-resolution subsurface structures. In this study, a two-step strategy is proposed to separate a given model into reflectivity and background velocity models and alternately update them using diffraction-angle filtering (DAF) based scale-separation technique, which effectively controls the wavenumber components and reconstructs low-wavenumber structures from the reflected waves.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Yujiang Xie, Catherine A. Rychert, Nicholas Harmon
Summary: The elastic and anelastic structures of the Earth provide fundamental constraints for understanding its physical and chemical properties. Deciphering small variations in seismic wave velocity and amplitude can be challenging, but advanced techniques such as full-waveform inversion can be useful.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Mohsen Kazemnia Kakhki, Ahmadreza Mokhtari, Webe Joao Mansur, Vincenzo Del Gaudio
Summary: In this paper, a novel time-frequency polarization filter based on three-component sparse adaptive S transform (3C-SAST) is proposed, which demonstrates high efficiency and flexibility in seismic surface wave separation.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Katrin Hannemann, Tom Eulenfeld, Frank Krueger, Torsten Dahm
Summary: This study provides estimates of the scattering and absorption of high-frequency seismic waves in the oceanic lithosphere using data obtained from a seismological array in the Eastern North Atlantic. The results indicate that the Atlantic Ocean has a higher attenuation compared to the Pacific Ocean, and intrinsic attenuation is equal to or slightly stronger than scattering attenuation for frequencies above 3 Hz.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Tae-Kyung Hong, Seongjun Park, Dongchan Chung, Byeongwoo Kim
Summary: Thunder-induced seismic waves recorded at dense seismic stations in Seoul are analysed for inversion of thunder source spectra. A theory is introduced for the inversion of acoustic source spectra from thunder-induced seismic waves, considering the propagation and acoustic-to-seismic coupling effects. Thunder-induced seismic signals were identified and equivalent to the ground motion levels induced by a moderate-size earthquake. Acoustic thunder source spectra were determined by stacking the inverted acoustic spectra at all stations.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Nori Nakata, Rie Nakata, Ayato Kato, Ziqiu Xue, Malcolm C. A. White
Summary: Enigmatically strong tube waves continue to exist long after the direct wave during repeat crosswell-monitoring surveys at the Nagaoka CO2 injection site in Japan. The tube waves, which have linear moveouts with velocities of 1.29 and 1.41 km s(-1) at plastic and steel casings, respectively, are generated by double scattering on the shallow side of the wells. These tube waves are able to provide valuable information for correcting location errors, estimating scatterer locations and strengths, and accurately estimating subsurface velocities along the wells.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Can Oren, Jeffrey Shragge
Summary: The study proposes a methodology for microseismic image-domain wavefield tomography using the elastic wave equation and zero-lag and extended source images to improve the accuracy of elastic velocity models and event location precision.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Huachen Yang, Jianzhong Zhang, Kai Ren, Changbo Wang
Summary: A non-iterative first-arrival traveltime inversion method (NFTI) is proposed for building smooth velocity models using seismic diving waves observed on irregular surface. The new equations and methods have been validated in tests, showing effectiveness and superiority, but also limitations in abrupt velocity model scenarios.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geochemistry & Geophysics
Ehsan Moradian Bajestani, Anooshiravan Ansari, Ehsan Karkooti
Summary: A model for frequency-dependent local and regional P-wave attenuation in continental paths in the Iranian Plateau is estimated, showing three distinct sections of attenuation and faster attenuation of P-wave amplitude compared to global models at local distances.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geochemistry & Geophysics
Iris Hartstra, Kees Wapenaar
Summary: This study analyzes the impact of scattering on the performance of seismic interferometry applications for retrieving body-wave reflections. The results show a trade-off between the quality of the retrieved virtual primary reflection and the scattering strength of the overburden. The full-field MDD method proves to be the most resilient to the negative effects of multiple scattering.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
S. Adourian, C. Lyu, Y. Masson, F. Munch, B. Romanowicz
Summary: In this study, we propose a general framework called 'box tomography' that couples different numerical seismic wave propagation solvers. This approach aims to reduce the computational cost of full-waveform inversion for structures within a target region when sources and receivers are located far from the region. We extend the implementation of this approach to a 3-D global elastic earth model, where both sources and stations are outside the target region. We demonstrate the efficiency and versatility of this approach through benchmark tests and believe it has great potential for imaging remote target regions in the deep mantle.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Wenqiang Li, Hengshan Hu
Summary: The reflection and transmission of elastic waves at imperfectly bonded interfaces in stressed media are studied, considering the influence of initial stress on the equation of motion, the elastic properties of the medium, and the boundary conditions at the interface. The results show that the energy reflection and transmission coefficients depend on various factors including the elastic properties of the incident and transmitted media, the initial stress, the interfacial compliance, the frequency, and the propagation direction.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Can Oren, Jeffrey Shragge
Summary: Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images compared to conventional surface seismic data sets. However, building a accurate velocity model is a main difficulty for generating high-quality images. In this study, we adopt a 3-D image-domain elastic transmission tomography technique to construct plausible velocity models using active-source DAS VSP data. The results demonstrate the efficacy of the adopted elastic inversion technique in obtaining accurate subsurface images.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Vadim Monteiller, Stephen Beller, Bastien Plazolles, Sebastien Chevrot
Summary: Injection methods are efficient means to compute synthetic seismograms of short-period teleseismic body waves in 3-D regional models, using either an incident plane wave or a complete wavefield. The effects of wave front and Earth curvature are negligible for moderate size regional domains and periods larger than 2 seconds.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geochemistry & Geophysics
Simanchal Padhy, Nampally Subhadra
Summary: The study suggests that using a layered model provides more accurate attenuation estimates compared to a uniform model, as the latter tends to overestimate intrinsic and scattering coefficients. By comparing analytical and numerical experiments, it was found that the frequency-dependent factor for the uniform model ranges from 4.0 to 7.5 below 3 Hz and 2.8 to 4.0 above 3 Hz for intrinsic attenuation, and 1.1 to 2.5 for scattering coefficient at 1-12 Hz. Additionally, under the assumption of a layered model, strong scattering in the upper crust and weak mantle leakage in the frequency range 1.5-24 Hz were observed.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geochemistry & Geophysics
Chao Song, Tariq Alkhalifah, Umair Bin Waheed
Summary: This paper introduces a framework based on physics-informed neural networks for solving the frequency-domain wave equation. The proposed method trains neural networks to generate wavefield solutions that satisfy the wave equation. The results show that this method is flexible and versatile in various media and models with irregular topography.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Donggeon Kim, Jongha Hwang, Dong-Joo Min, Ju-Won Oh, Tariq Alkhalifah
Summary: Full waveform inversion (FWI) is a highly non-linear optimization problem that aims to reconstruct high-resolution subsurface structures. In this study, a two-step strategy is proposed to separate a given model into reflectivity and background velocity models and alternately update them using diffraction-angle filtering (DAF) based scale-separation technique, which effectively controls the wavenumber components and reconstructs low-wavenumber structures from the reflected waves.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Ali Can Bekar, Erdogan Madenci, Ehsan Haghighat, Umair bin Waheed, Tariq Alkhalifah
Summary: The computation of travel times for P and S waves is crucial for earthquake and exploration seismology applications. However, solving the eikonal equation in anisotropic media with a complex phase velocity field is challenging. This study presents a new method using the peridynamic differential operator (PDDO) to solve the eikonal equation, which provides a non-local form and is immune to discontinuities.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Isa Eren Yildirim, Tariq Alkhalifah, Ertugrul Umut Yildirim
Summary: This paper introduces gradient-based traveltime tomography and its limitations, and proposes a novel approach using data-driven inversion techniques based on deep convolutional neural networks to estimate velocity models. The method can effectively address the limitations of conventional methods, making it particularly suitable for near-surface applications.
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
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)