Zobrazeno 1 - 10
of 524
pro vyhledávání: '"Alkhalifah, Tariq"'
Full Waveform Inversion (FWI) is a technique employed to attain a high resolution subsurface velocity model. However, FWI results are effected by the limited illumination of the model domain and the quality of that illumination, which is related to t
Externí odkaz:
http://arxiv.org/abs/2408.09975
Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can
Externí odkaz:
http://arxiv.org/abs/2408.09767
Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate and robust monitoring system to ensure the effectiveness of CCS and mi
Externí odkaz:
http://arxiv.org/abs/2407.18426
Publikováno v:
Geophysics 89 (2024) 1-62
Distributed Acoustic Sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signals compared to the background noise, especially in tough installation e
Externí odkaz:
http://arxiv.org/abs/2405.07660
Autor:
Huang, Xinquan, Alkhalifah, Tariq
Solving the wave equation is essential to seismic imaging and inversion. The numerical solution of the Helmholtz equation, fundamental to this process, often encounters significant computational and memory challenges. We propose an innovative frequen
Externí odkaz:
http://arxiv.org/abs/2405.01272
Autor:
Cheng, Shijun, Alkhalifah, Tariq
Using symbolic regression to discover physical laws from observed data is an emerging field. In previous work, we combined genetic algorithm (GA) and machine learning to present a data-driven method for discovering a wave equation. Although it manage
Externí odkaz:
http://arxiv.org/abs/2404.17971
Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep learning techniques offer promising solutions for reconstructing missing data parts by leveraging existing information. However,
Externí odkaz:
http://arxiv.org/abs/2404.02632
Full Waveform Inversion (FWI) is a technique widely used in geophysics to obtain high-resolution subsurface velocity models from waveform seismic data. Due to its large computation cost, most flavors of FWI rely only on the computation of the gradien
Externí odkaz:
http://arxiv.org/abs/2403.17518
Publikováno v:
JGR: Machine learning and Computation, 2024
Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards. Machine learning-based inversion algorithms have shown promising performance in regional (i.e
Externí odkaz:
http://arxiv.org/abs/2402.06277
Autor:
Cheng, Shijun, Alkhalifah, Tariq
Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applic
Externí odkaz:
http://arxiv.org/abs/2401.11502