Physics-guided data-driven seismic inversion
WebbABSTRACT Velocity model inversion is one of the most important tasks in seismic exploration. Full-waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion methods, but it heavily depends on initial models and is computationally expensive. In recent years, a large number of deep-learning (DL)-based velocity model … Webb1 jan. 2024 · Physics-Guided Data-Driven Seismic Inversion: Recent progress and future opportunities in full-waveform inversion January 2024 DOI: 10.1109/MSP.2024.3217658 …
Physics-guided data-driven seismic inversion
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WebbDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning methods that use seismic data as the only input lead to difficult training and unstable inversion results (i.e., transverse discontinuity or geologic unreliability). Webb5 juli 2024 · An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave velocity ( V P ), S-wave velocity ( V S ), and density ( ρ) of the earth’s …
Webbgreater generalization ability than purely physics-based and purely data-driven approaches. 1 Introduction Seismic full-waveform inversion (FWI) attempts to reconstruct an image of the subsurface geology from measurements of natural or artificially produced seismic waves that have travelled through the subsurface. Webb15 sep. 2024 · A pre-stack inversion is performed to estimate elastic properties like VP, VS, r of the earth’s subsurface. Pre-stack inversions are generally solved employing a global or local optimization technique and performed on each CDPs (common-depth-point) separately to estimate the elastic properties.
WebbDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning … WebbCurrently, most seismic inversion problems are addressed by: physics-driven seismic inversion based on adjoint theory (commonly used in the geophysical community). This method attempts to minimize iteratively a cost function defined by the differences between the observed and calculated data (e.g., \(l^2\)-norm).
Webb2 Deep learning techniques for electromagnetic forward modeling + Show details-Hide details p. 25 –65 (41) In this chapter, we introduce the approaches of applying deep learning techniques to electromagnetic forward modeling. These approaches are divided into three types: fully data-driven forward modeling, deep learning-assisted forward …
WebbSeismic Converted Waves Velocity Model Building using VSP-driven Approach Ali Abdulla Shaiban (Saudi Aramco) 14:35 - 14:55 Coffee Break - 20 min Session 3 IMPACT OF SEISMIC ACQUISITION AND PROCESSING ON QI -PART 2 14:55 - 16:10 Session Chairs: Mohamed Zainal (Saudi Aramco) & TBC Impact of Pre-Stack Seismic Data Conditioning … json整形 サクラエディタWebb2 jan. 2024 · Abstract: The goal of seismic inversion is to obtain subsurface properties from surface measurements. Seismic images have proven valuable, even crucial, for a variety of applications, including subsurface energy exploration, earthquake early warning, carbon capture and sequestration, estimating pathways of subsurface contaminant … adolescent pregnancy 2022 statisticsWebbIn traditional model-driven impedance inversion methods, the low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. json整形サービスWebb22 nov. 2024 · Abstract: The low-frequency seismic data provide crucial information for guiding the full-waveform inversion (FWI), especially when strong reflectors exist in the velocity model. However, hardware limitations make it difficult to acquire low-frequency data. To overcome the nonlinearity and ill-posedness caused by the absence of the low … json 改行できないWebbSeismic inversion is the inverse problem: given actual surface measurements, infer what subsurface configuration would give rise to those measurements. Like most inverse … json 形式 オブジェクト 配列WebbWe propose a hybrid network design, involving both deterministic, physics-based modelling and data-driven deep learning components. From an optimization standpoint, both a data-driven model misfit (i.e., standard deep learning), and now a physics-guided data residual (i.e., a wave propagation network), are simultaneously minimized during the training of … json 整形 サクラエディタ マクロWebbABSTRACT Seismic velocity inversion plays a vital role in various applied seismology processes. A series of deep learning methods have been developed that rely purely on … adolescent pregnancy ppt