Thrust faulting and crust--upper mantle structure in East Australia

Mills, Joseph M.; Fitch, Thomas J.

doi:10.1111/j.1365-246x.1977.tb03677.x

Cited by 27 publications

(7 citation statements)

References 42 publications

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“…Nevertheless, uniform random search methods still found applications. In addition to the regional and global travel time studies, other applications of MCI have included electromagnetic induction [ Anderssen , 1970], Rayleigh wave attenuation [ Mills and Fitch , 1977; Mills and Hales , 1978], regional magnetotelluric studies [ Hermance and Grillot , 1974; Jones and Hutton , 1979], estimation of mantle viscosities [ Ricard et al ., 1989], and estimation of plate rotation vectors [ Jestin et al ., 1994].…”

Section: A Brief History Of Monte Carlo Inversion In Geophysicsmentioning

confidence: 99%

Monte Carlo Methods in Geophysical Inverse Problems

Sambridge

Mosegaard

2002

Reviews of Geophysics

654

395

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[1] Monte Carlo inversion techniques were first used by Earth scientists more than 30 years ago. Since that time they have been applied to a wide range of problems, from the inversion of free oscillation data for whole Earth seismic structure to studies at the meter-scale lengths encountered in exploration seismology. This paper traces the development and application of Monte Carlo methods for inverse problems in the Earth sciences and in particular geophysics. The major developments in theory and application are traced from the earliest work of the Russian school and the pioneering studies in the west by Press [1968] to modern importance sampling and ensemble inference methods. The paper is divided into two parts. The first is a literature review, and the second is a summary of Monte Carlo techniques that are currently popular in geophysics. These include simulated annealing, genetic algorithms, and other importance sampling approaches. The objective is to act as both an introduction for newcomers to the field and a comprehensive reference source for researchers already familiar with Monte Carlo inversion. It is our hope that the paper will serve as a timely summary of an expanding and versatile methodology and also encourage applications to new areas of the Earth sciences. INDEX TERMS:3260

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Section: A Brief History Of Monte Carlo Inversion In Geophysicsmentioning

confidence: 99%

Monte Carlo Methods in Geophysical Inverse Problems

Sambridge

Mosegaard

2002

Reviews of Geophysics

654

395

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“…(c) Modeled stress orientations from modeling by Müller et al []. Full list of references for all stress data in Figure 2a: [ Blumling , ; Denham , ; Denham et al ., , ; Enever et al ., , ; Enever and Wooltorton , ; Hillis et al ., ; Hillis and Reynolds , ; McCue et al ., , ; Michael‐Leiba and Dent , ; Mills and Fitch , ; Worotnicki and Denham , ].…”

Section: Introductionmentioning

confidence: 99%

Influence of basement structures on in situ stresses over the Surat Basin, southeast Queensland

et al. 2015

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The Jurassic to Cretaceous sedimentary rocks of the Surat Basin in southeast Queensland host a significant volume of coal seam gas resources. Consequently, knowledge of the in situ stress is important for coal permeability enhancement and wellbore stability. Using wireline log data and direct stress measurements, we have calculated stress orientations from 36 wells and stress magnitudes from 7 wells across the Surat Basin. Our results reveal a relationship between high tectonic stress and proximity to structures within the underlying “basement” rocks. The influence of tectonic stresses is diminished with depth in areas with thicker sedimentary cover that are relatively far from the basement structures. We suggest that this relationship is due to the redistribution of in situ stresses around areas where basement is shallower and where basement structures, such as the Leichhardt‐Burunga Fault System, are present. This behavior is explained by a lower rigidity in the thickest basin cover, which reduces the ability to maintain higher tectonic stress. Over the entire Surat Basin, a significant amount of variability in in situ stress orientation is observed. The authors attribute this stress variability to complex plate boundary interactions on the northern and eastern margins of the Indo‐Australian Plate.

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“…MC methods can be applied to a wide range of problems in physics, chemistry, economics, biology, and mathematics. In geophysics, MC methods have proved to be a powerful tool to solve optimisation problems in different geophysical fields, including electromagnetic induction (Anderssen ), Rayleigh wave attenuation (Mills and Fitch ), regional magnetotelluric studies (Hermance and Grillot ), estimation of plate rotation vectors (Jestin, Huchon and Gaulier ), and exploration and applied geophysics (Sen and Stoffa ). The first applications of MC methods to geophysics were proposed in the 1960s (Keilis‐Borok and Yanovskaya ; Backus and Gilbert ), whereas the first application to seismology was by Press (), who inverted travel times of body waves and 97 eigenperiods of the Earth's free oscillations for variations in the Earth's compressional wave velocity, shear wave velocity, and density as a function of depth.…”

Section: Introductionmentioning

confidence: 99%

Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion, and residual static computation

Sajeva

Aleardi

Galuzzi

et al. 2017

Geophysical Prospecting

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We compare the performances of four different stochastic optimisation methods using four analytic objective functions and two highly non-linear geophysical optimisation problems: 1D elastic full-waveform inversion (FWI) and residual static computation. The four methods we consider, namely, adaptive simulated annealing (ASA), genetic algorithm (GA), neighbourhood algorithm (NA), and particle swarm optimisation (PSO), are frequently employed for solving geophysical inverse problems. Because geophysical optimisations typically involve many unknown model parameters, we are particularly interested in comparing the performances of these stochastic methods as the number of unknown parameters increases. The four analytic functions we choose simulate common types of objective functions encountered in solving geophysical optimisations: a convex function, two multi-minima functions that differ in the distribution of minima, and a nearly flat function. Similar to the analytic tests, the two seismic optimisation problems we analyse are characterized by very different objective functions. The first problem is a 1D elastic FWI, which is strongly ill-conditioned and exhibits a nearly flat objective function, with a valley of minima extended along the density direction. The second problem is the residual static computation, which is characterized by a multi-minima objective function produced by the so-called cycle-skipping phenomenon. According to the tests on the analytic functions and on the seismic data, GA generally displays the best scaling with the number of parameters. It encounters problems only in the case of irregular distribution of minima, that is, when the global minimum is at the border of the search space and a number of important local minima are distant from the global minimum. The ASA method is often the best-performing method for low-dimensional model spaces, but its performance worsens as the number of unknowns increases. The PSO is effective in finding the global minimum in the case of low-dimensional model spaces with few local minima or in the case of a narrow flat valley. Finally, the NA method is competitive with the other methods only for low-dimensional model spaces; its performance stability sensibly worsens in the case of multi-minima objective functions

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Thrust faulting and crust--upper mantle structure in East Australia

Cited by 27 publications

References 42 publications

Monte Carlo Methods in Geophysical Inverse Problems

Monte Carlo Methods in Geophysical Inverse Problems

Influence of basement structures on in situ stresses over the Surat Basin, southeast Queensland

Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion, and residual static computation

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