... As one of the primary approaches for property estimation in subsurface interpretation and characterization, stochastic seismic inversion provides a measurement of uncertainty but is a complicated process and requires heavy computation. To accelerate the task with deep learning, we propose...

... Regional seismic imaging of continental margins from ultra long-offset sparse ocean-bottom node (OBN) data by Fullwaveform inversion (FWI) is challenging because the complexity of the wavefields and the large number of propagated wavelengths significantly amplify the nonlinearity of the inverse...

... tomography and finite-frequency wavepath inversion at great depth. geology tarim basin near-surface condition complex near-surface condition tomography velocity model applied geoscience & energy 10 seismic imaging geologist overcoming complex near-surface condition geological...

... Elastic Full Waveform Inversion (EFWI) is increasingly being viewed as a key technology for seismic imaging. Most successful FWIs require a good set of initial models. Obtaining these models can be time-consuming and ad hoc methods are often used. Several ideas have been recently proposed...

... Dispersion curve inversion is a key step in obtaining the acoustic velocity of formations around wells and information evaluation. Traditional dispersion inversion methods, based on wellbore sound propagation theory, suffer from low efficiency and multimodality issues. Methods based on deep...

... full waveform inversion (JDFWI) for velocity model update, and one way wave equation multiple migration (OWEMM) method were used to generate final imaging. The results provide client with good quality imaging in a relatively large subsurface area. geologist vertical seismic profile migration...

... Acoustic full-waveform inversion is often able to build highquality velocity models for sub-salt imaging using only acoustic wave propagation. This is achieved by adaptively matching acoustic data to the real-world elastic field data. Here we show both acoustic and elastic adaptive waveform...

... be incorporated in various forms and for the stochastic technique, the statistics followed by the logs can be used. In case of fractal based inversion, we used the fractal characteristics of the well log to produce fractal-based models. The inversion module minimizes a suitable objective function using very fast...

... Full waveform inversion (FWI) is crucial for reconstructing high-resolution subsurface models, but it is hindered by its computational intensity, especially for real-time applications. Leveraging learned wavefield solutions using a Fourier neural operator (FNO), we introduce a novel physics...

... subsurface, we use dense 3D node active-source and ambient noise seismic data. We outline dedicated interferometry based processing and inversion workflows for the reconstruction of P- and Swave velocity models. The potential and limitations of waveequation tomography for this kind of datasets and targets...

... Full waveform inversion (FWI) has great potential in reconstructing high-resolution subsurface models. However, due to limitations in observation, this inverse problem is highly ill-posed, especially for recovering a 3D velocity model. To solve this problem, regularization is often used, like...

... Recent work using stress inversions has emphasized the lack of stability of stress inversions from earthquakes with fault planes of limited diversity. We explore this question using data recorded over an enhanced monitoring array and address how we can answer questions on stability. We show...

... Majority of full waveform inversion (FWI) implementations are based on local optimization methods. However, due to the high nonlinearity of the forward problem, FWI is challenged with the well-known local minima issue. Traditional FWI methods often require observed data to have low frequency...

... interpretation alkhalifah petroleum geology machine learning exploration geophysicist seismic data inversion pitfall ravasi Fourth International Meeting for Applied Geoscience & Energy 10.1190/image2024-4101010.1 © 2024 Society of Exploration Geophysicists and the American Association of Petroleum...

... different sensitivities in depth – Love waves being more sensitive to shallower depths, and Rayleigh waves to greater depths in the same frequency band – jointly inverting both Rayleigh and Love waves improves the vertical resolution. Besides, joint inversion also reduces the number of solutions...

... Full waveform inversion (FWI) is becoming an increasingly dominant force in velocity model building and seismic imaging, often providing us with unrivaled focusing and resolution of the subsurface image. The superior velocity models and seismic images achieved through FWI also enable serious...

... velocities accordingly, considering the model resolution. Strategies for optimizing forward modeling and inversion have been implemented. Results show that first arrival tomography can update top salt velocities with large offset acquisition geometries. tomography arrival tomography salt tectonics...

... Uncertainty quantification (UQ) plays a vital role in addressing the challenges and limitations encountered in full-waveform inversion (FWI). Most UQ methods require parameter sampling which requires many forward and adjoint solves. This often results in very high computational overhead compared...

... By formulating the full waveform inversion (FWI) in the Bayesian inference framework, uncertainties are implicitly considered as the covariance matrix of the posterior probability distribution (PPD). As a sampling-based method to solve the Bayesian FWI problem, the Markov chain Monte Carlo (MCMC...

... geologist geological subdiscipline flexure exploration geophysicist reservoir characterization potential field applied geoscience & energy 10 ambiguity altitude inversion gravity data deconvolution interpretation geophysical prospecting information geophysics application Assessing...