Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy

Abstract

A B S T R A C TTo analyse and invert refraction seismic travel time data, different approaches and techniques have been proposed. One common approach is to invert first-break travel times employing local optimization approaches. However, these approaches result in a single velocity model, and it is difficult to assess the quality and to quantify uncertainties and non-uniqueness of the found solution. To address these problems, we propose an inversion strategy relying on a global optimization approach known as particle swarm optimization. With this approach we generate an ensemble of acceptable velocity models, i.e., models explaining our data equally well. We test and evaluate our approach using synthetic seismic travel times and field data collected across a creeping hillslope in the Austrian Alps. Our synthetic study mimics a layered near-surface environment, including a sharp velocity increase with depth and complex refractor topography. Analysing the generated ensemble of acceptable solutions using different statistical measures demonstrates that our inversion strategy is able to reconstruct the input velocity model, including reasonable, quantitative estimates of uncertainty. Our field data set is inverted, employing the same strategy, and we further compare our results with the velocity model obtained by a standard local optimization approach and the information from a nearby borehole. This comparison shows that both inversion strategies result in geologically reasonable models (in agreement with the borehole information). However, analysing the model variability of the ensemble generated using our global approach indicates that the result of the local optimization approach is part of this model ensemble. Our results show the benefit of employing a global inversion strategy to generate near-surface velocity models from refraction seismic data sets, especially in cases where no detailed a priori information regarding subsurface structures and velocity variations is available.

Exploring the capabilities of electrical resistivity tomography to study subsea permafrost

“…In such environments, we expect sharp boundaries and variations along the interfaces due to inundated thermokarst structures (Angelopoulos et al, 2021), pingo-like features, bottom-fast ice versus floating ice regime transitions in winter, or changes in the ratio of coastal erosion vs. degradation rate; i.e., changing from a period of fast thawing and low coastal erosion to a period of fast coastal erosion and slow thawing can result in a heterogeneous structure of the IBPT (e.g., Overduin et al, 2016). Because we expect some of these processes and structures at our field sites, we adopt a strategy based on the sums of arctangent functions because it allows for abrupt and smooth changes along the interfaces (e.g., Roy et al, 2005;Rumpf and Tronicke, 2015). Following Arboleda-Zapata et al (2022), the sums of arctangent functions for a single interface can be written as…”

Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method

“…The uncertainty in geophysical inverse problems was estimated by various stochastic inversion algorithms, such as MCMC (Liu and Stock, 1993;Malinverno and Briggs, 2004;Chen and Dickens, 2009;Gunning et al, 2010;Kwon and Snieder, 2011), SA (Dosso, 2002;Dosso and Nielsen, 2002;Bhattacharya et al, 2003;Roy et al, 2005;Varela et al, 2006), PSO (Fernández-Martínez et al, 2012;Rumpf and Tronicke, 2015), and rjMCMC Reading and Gallagher, 2013;Dadi, 2014;Galetti et al, 2015;Dadi et al, 2015).…”

Towards large‐scale stochastic refraction tomography: a comparison of three evolutionary algorithms

“…These algorithms were originally designed for optimization and are now used to quantify uncertainties (Fernández Martínez et al . ; Tronicke, Paasche and Böniger ; Rumpf and Tronicke ). Luu et al .…”

“…These three EAs are becoming more and more popular in the geophysical community because their implementations are straightforward, they require less tuning, they are by nature parallel algorithms and their convergence rates is much higher compared to classical global optimization methods (Angeline 1998;. These algorithms were originally designed for optimization and are now used to quantify uncertainties (Fernández Martínez et al 2012;Tronicke, Paasche and Böniger 2012;Rumpf and Tronicke 2015). Luu et al (2018) showed on a simple real data example that CPSO does sample properly the velocity model parameter space to provide reliable estimates of uncertainty; results were similar to those obtained by MCMC at a much lower computational cost.…”

“…() applied an hybrid scheme based on a Monte Carlo approach and the simplex optimization technique to derive an initial background velocity model from first arrival traveltime data. Rumpf and Tronicke () used a particle swarm optimizer to solve the tomographic problem and quantify uncertainties using a one‐dimensional layer‐based model parameterization. More recently, Ryberg and Haberland () directly applied a Bayesian MCMC formalism to invert refraction data and parameterized the velocity model using Voronoi tesselation and triangulated meshes.…”