An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning

Silva, Sérgio Luiz E. F. da; Carvalho, P.; Costa, Carlos A. N. da; Araújo, João M. de; Corso, Gilberto

doi:10.1371/journal.pone.0240999

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…In order to demonstrate the robustness of our proposal, we consider a portion of the 2D Marmousi model [32,33]. Marmousi is a realistic geological model based on the Kwanza Basin (Angola) [34] which is largely used for testing new seismic imaging methodologies [35][36][37]. The area of study consists of water, gas, and oil sand channels, in addition to many reflectors and several geological strata, as depicted in Figure 2a.…”

Section: Numerical Resultsmentioning

confidence: 99%

Improving Seismic Inversion Robustness via Deformed Jackson Gaussian

Silva

Souza

et al. 2021

Entropy

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The seismic data inversion from observations contaminated by spurious measures (outliers) remains a significant challenge for the industrial and scientific communities. This difficulty is due to slow processing work to mitigate the influence of the outliers. In this work, we introduce a robust formulation to mitigate the influence of spurious measurements in the seismic inversion process. In this regard, we put forth an outlier-resistant seismic inversion methodology for model estimation based on the deformed Jackson Gaussian distribution. To demonstrate the effectiveness of our proposal, we investigated a classic geophysical data-inverse problem in three different scenarios: (i) in the first one, we analyzed the sensitivity of the seismic inversion to incorrect seismic sources; (ii) in the second one, we considered a dataset polluted by Gaussian errors with different noise intensities; and (iii) in the last one we considered a dataset contaminated by many outliers. The results reveal that the deformed Jackson Gaussian outperforms the classical approach, which is based on the standard Gaussian distribution.

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Section: Numerical Resultsmentioning

confidence: 99%

Improving Seismic Inversion Robustness via Deformed Jackson Gaussian

Silva

Souza

et al. 2021

Entropy

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“…where Γ s,r p s and d s,r represent modeled and observed data, respectively, in which Γ s,r is an extracting operator onto the receiver r of the source s (da Silva et al, 2020).…”

Section: Full-waveform Inversion (Fwi)mentioning

confidence: 99%

Assessing Time-Lapse Full-Waveform Inversion Strategies in a Brazilian Pre-Salt Setting

Hochwart,

Silva,

Cetale

et al. 2024

Braz J Geophys

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We conducted a comparative study of time-lapse full-waveform inversion (time-lapse FWI) strategies, considering a typical deep-water Brazilian pre-salt setting. This study employed a realistic P-wave model, ocean bottom nodes (OBN) acquisition, noisy data, and a Gaussian anomaly to represent time-lapse model changes. We evaluated the four most commonly used time-lapse FWI schemes. In the first, known as parallel time-lapse FWI, two independent FWI processes are performed from the same initial model, utilizing baseline and monitor datasets. In the second strategy, namely sequential time-lapse FWI, the retrieved baseline model serves as the starting model for inverting monitor data. In both cases, the time-lapse model is derived by subtracting the retrieved baseline model from the retrieved monitor model. The remaining two methods were double-difference and central-difference time-lapse FWI. Our findings demonstrate that all these schemes can detect model variations of 3%. Remarkably, the central-difference time-lapse FWI method demonstrated superior accuracy in producing time-lapse models and, as such, presents itself as a promising strategy for implementation in time-lapse studies within Brazilian pre-salt regions.

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“…The estimation of physical model parameters from observed data is a frequent problem in many areas, such as in machine learning [ 1 , 2 ], geophysics [ 3 , 4 ], biology [ 5 , 6 ], physics [ 7 , 8 ], among others [ 9 – 11 ]. Such a task is solved through the so-called inverse problem, which consists of identifying physical parameters that can not be directly measured from the observations [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Generalized statistics: Applications to data inverse problems with outlier-resistance

Lima

Araújo

et al. 2023

PLoS ONE

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The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associated with generalized Gaussian distributions in the context of Rényi, Tsallis and Kaniadakis statistics. In this regard, we analytically analyze the outlier-resistance of each proposal through the so-called influence function. In this way, we formulate inverse problems by constructing objective functions linked to the maximum likelihood estimators. To demonstrate the robustness of the generalized methodologies, we consider an important geophysical inverse problem with high noisy data with spikes. The results reveal that the best data inversion performance occurs when the entropic index from each generalized statistic is associated with objective functions proportional to the inverse of the error amplitude. We argue that in such a limit the three approaches are resistant to outliers and are also equivalent, which suggests a lower computational cost for the inversion process due to the reduction of numerical simulations to be performed and the fast convergence of the optimization process.

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An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning

Cited by 9 publications

References 35 publications

Improving Seismic Inversion Robustness via Deformed Jackson Gaussian

Improving Seismic Inversion Robustness via Deformed Jackson Gaussian

Assessing Time-Lapse Full-Waveform Inversion Strategies in a Brazilian Pre-Salt Setting

Generalized statistics: Applications to data inverse problems with outlier-resistance

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