Probabilistic waveform inversion: Quest for the law

Shigapov, R.

doi:10.5445/ir/1000091433

Cited by 3 publications

(2 citation statements)

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“…Overall, the trade-off (RSD) is low throughout the whole model, while two trenches, especially the one locates near the boundary (X = 31 m) of the survey area, have relatively higher trade-offs compared to the surrounding PAN ET AL. Shigapov, 2019).…”

Section: Pareto Solutions and Trade-off Analysismentioning

confidence: 99%

“…One common problem for the uncertainty estimation in FWI is the prohibitively high computational cost due to the curse of high dimensionality and the cost of the forward problem (Curtis & Lomax, 2001). Shigapov (2019) proposed a probabilistic framework that formulates FWI as a deterministic multi-objective optimization problem. proposed a multi-objective waveform inversion on shallow-seismic data and showed that the distribution of Pareto solution in the model space represents the uncertainty (trade-off) information.…”

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confidence: 99%

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Random‐Objective Waveform Inversion of 3D‐9C Shallow‐Seismic Field Data

2021

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Full-waveform inversion (FWI) has gained increasing attention in the reconstruction of Earth's structures. One typical problem FWI faces is the ill-posedness of the inverse problem, which may make FWI converging to a local minimum. A common practical way to mitigate this problem is to use an appropriate initial model and adopt a multiscale strategy to progressively include the waveform of shorter wavelength (Bunks et al., 1995). Another approach is to use a more convex objective function that exhibits less local minima

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Section: Pareto Solutions and Trade-off Analysismentioning

confidence: 99%

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confidence: 99%

Random‐Objective Waveform Inversion of 3D‐9C Shallow‐Seismic Field Data

2021

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Individual and Joint Inversions of Shallow-Seismic Rayleigh and Love Waves: Full-Waveform Inversion Versus Random-Objective Waveform Inversion

Pan

Gao

2023

Surv Geophys

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Multi-objective waveform inversion of shallow seismic wavefields

Pan

Gao

Shigapov

2019

Geophysical Journal International

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SUMMARY It has been increasingly popular to use shallow-seismic full-waveform inversion (FWI) to reconstruct near-surface structures. Conventional FWI tries to resolve the earth model by minimizing the difference between observed and synthetic seismic data using a certain criterion (conventionally, l2-norm of waveform difference). In this paper, we propose a multi-objective waveform inversion (MOWI) in which the similarity of data is quantified and minimized using multiple criteria simultaneously. By doing so, we expand the dimensionality of objective space as well as the mapping from data space to objective space, which provides MOWI higher freedom in exploring the model space compared to single-objective FWI. We combine three different scalar-valued objective functions into a vector-valued multi-objective function which measures the similarity of the waveform, the waveform envelope, and the amplitude spectra of the data, respectively. This multi-objective function takes not only trace-based waveform and wave packet similarity but also the dispersion characteristics of surface waves into account. Furthermore, the uncertainty in the inversion result could be estimated and analysed quantitatively by the variance of the optimal models. We propose a modified ϵ-constraint algorithm to solve the multi-objective optimization problem. Two synthetic examples are used to show the advantages of using MOWI compared to single-objective FWI. We also test the efficiency of MOWI by using two synthetic shallow-seismic examples, which confirm that MOWI can converge to a better result compared to the conventional single-objective FWI.

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Probabilistic waveform inversion: Quest for the law

Cited by 3 publications

References 0 publications

Random‐Objective Waveform Inversion of 3D‐9C Shallow‐Seismic Field Data

Random‐Objective Waveform Inversion of 3D‐9C Shallow‐Seismic Field Data

Individual and Joint Inversions of Shallow-Seismic Rayleigh and Love Waves: Full-Waveform Inversion Versus Random-Objective Waveform Inversion

Multi-objective waveform inversion of shallow seismic wavefields

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