Research note: Application of refraction full‐waveform inversion of ocean bottom node data using a squared‐slowness model parameterization

Abstract

Full‐waveform inversion is a wave equation–based imaging technique for obtaining subsurface model parameters by matching modelled with field data. Full‐waveform inversion is often formulated as a local optimization problem in which the model parameterization influences the gradient preconditioner and the convergence rate associated with the full‐waveform inversion objective function. Model parameterization governs the radiation pattern of the so‐called secondary Born source. In this work, we assess model parameterization effects on the estimation of P‐wave velocities using a three‐dimensional acoustic time‐domain full‐waveform inversion procedure. These include the three commonly used parameterization: velocity, slowness and squared slowness. In this context, we consider a field data set from a deepwater Brazilian pre‐salt field using a recently introduced circular shot ocean bottom node acquisition which favours refracted waves. The results reveal that the squared slowness model parameterization provides a satisfactory trade‐off between the reconstruction of the deep pre‐salt target area and convergence rate, saving 50% of runtime compared to the velocity and slowness cases.

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

“…In this work we consider a Fletcher-Reeves nonlinear conjugate gradient method (see, for example, Nocedal and Wright (2006)) to solve the FWI problem. We chose to use this nonlinear conjugate gradient algorithm because it has been shown to work well when analyzing real data from the pre-salt region of Brazil, as recently presented by da Silva et al (2024). This optimization method involves updating the subsurface model by minimizing an objective function ϕ in the following way:…”

“…The Tupi Nodes pilot project demonstrated a highly favorable time-lapse response by leveraging full-waveform inversion (FWI) (Virieux and Operto, 2009) as an integral component of the timelapse seismic processing toolkit. FWI enables precise estimation of rock property changes (Warner et al, 2013;Górszczyk et al, 2021;da Silva et al, 2024), such as P-wave velocity alterations, further enhancing subsurface analysis accuracy in dynamic geological environments. Consequently, operating the FWI technique to analyze OBN data can yield significantly more precise subsurface models.…”

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

“…In the field of geological exploration, seismic signals provide a reliable means of geophysical detection for the exploration and development of resources such as oil and natural gas [3,4]. By analyzing the propagation velocities, reflections, refractions, and other characteristics of seismic signals, explorers can deduce the types, thicknesses, and structures of subsurface rock layers, thereby determining the potential locations and scales of oil and gas reservoirs [5,6]. However, seismic data are often contaminated by various types of noise, such as surface noise (e.g., wind and traffic), instrument noise (e.g., noise from the sensors themselves), and other man-made interference (e.g., electromagnetic interference and human activities).…”

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

“…The adjoint-state method reduces the computational complexity of the gradient calculation, making practical application of the FWI algorithm possible [7]. In recent years, acoustic FWI has successfully inverted industrial-scale real 3D marine data [8] and refracted waves in ocean bottom node data [9]. However, modeling of elastic wave propagation through elastodynamic theory is required for reflection analysis [10,11].…”