المؤلفون: Zhao, Xuebin, Curtis, Andrew

مصطلحات موضوعية: Statistics - Methodology, Mathematical Physics, Physics - Geophysics

الوصف: Many scientific investigations require that the values of a set of model parameters are estimated using recorded data. In Bayesian inference, information from both observed data and prior knowledge is combined to update model parameters probabilistically. Prior information represents our belief about the range of values that the variables can take, and their relative probabilities when considered independently of recorded data. Situations arise in which we wish to change prior information: (i) the subjective nature of prior information, (ii) cases in which we wish to test different states of prior information as hypothesis tests, and (iii) information from new studies may emerge so prior information may evolve over time. Estimating the solution to any single inference problem is usually computationally costly, as it typically requires thousands of model samples and their forward simulations. Therefore, recalculating the Bayesian solution every time prior information changes can be extremely expensive. We develop a mathematical formulation that allows prior information to be changed in a solution using variational methods, without performing Bayesian inference on each occasion. In this method, existing prior information is removed from a previously obtained posterior distribution and is replaced by new prior information. We therefore call the methodology variational prior replacement (VPR). We demonstrate VPR using a 2D seismic full waveform inversion example, where VPR provides almost identical posterior solutions compared to those obtained by solving independent inference problems using different priors. The former can be completed within minutes even on a laptop whereas the latter requires days of computations using high-performance computing resources. We demonstrate the value of the method by comparing the posterior solutions obtained using three different types of prior information.

الوصول الحر: http://arxiv.org/abs/2406.04072Test

المؤلفون: Wang, Jingxian, Curtis, Andrew G., Yim, Mark, Rubenstein, Michael

مصطلحات موضوعية: Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control

الوصف: Communication and position sensing are among the most important capabilities for swarm robots to interact with their peers and perform tasks collaboratively. However, the hardware required to facilitate communication and position sensing is often too complicated, expensive, and bulky to be carried on swarm robots. Here we present Maneuverable Piccolissimo 3 (MP3), a minimalist, single motor drone capable of executing inter-robot communication via infrared light and triangulation-based sensing of relative bearing, distance, and elevation using message arrival time. Thanks to its novel design, MP3 can communicate with peers and localize itself using simple components, keeping its size and mass small and making it inherently safe for human interaction. We present the hardware and software design of MP3 and demonstrate its capability to localize itself, fly stably, and maneuver in the environment using peer-to-peer communication and sensing.

الوصول الحر: http://arxiv.org/abs/2405.14144Test

المؤلفون: Curtis, Andrew G., Yim, Mark, Rubenstein, Michael

مصطلحات موضوعية: Computer Science - Robotics

الوصف: Despite their growing popularity, swarms of robots remain limited by the operating time of each individual. We present algorithms which allow a human to sculpt a swarm of robots into a shape that persists in space perpetually, independent of onboard energy constraints such as batteries. Robots generate a path through a shape such that robots cycle in and out of the shape. Robots inside the shape react to human initiated changes and adapt the path through the shape accordingly. Robots outside the shape recharge and return to the shape so that the shape can persist indefinitely. The presented algorithms communicate shape changes throughout the swarm using message passing and robot motion. These algorithms enable the swarm to persist through any arbitrary changes to the shape. We describe these algorithms in detail and present their performance in simulation and on a swarm of mobile robots. The result is a swarm behavior more suitable for extended duration, dynamic shape-based tasks in applications such as agriculture and emergency response.
Comment: 20 pages, 17 figures

الوصول الحر: http://arxiv.org/abs/2404.02265Test

المؤلفون: Curtis, Andrew John Bathgate

مرشدي الرسالة: カーティス, アンドリュー ジョン バスゲイト

مصطلحات موضوعية: Energy resource exports, Energy security, Decarbonisation, Exporter vulnerability, Hydrogen, 501

الوصف: 甲第24924号
エネ博第466号
新制||エネ||87(附属図書館)
(主査)教授 MCLELLAN Benjamin, 教授 宇根﨑 博信, 教授 河本 晴雄
学位規則第4条第1項該当
Doctor of Energy Science
Kyoto University
DFAM

الإتاحة: http://hdl.handle.net/2433/285858Test

المؤلفون: Zhao, Xuebin, Curtis, Andrew

مصطلحات موضوعية: Physics - Geophysics

الوصف: Geoscientists use observed data to estimate properties of the Earth's interior. This often requires non-linear inverse problems to be solved and uncertainties to be estimated. Bayesian inference solves inverse problems under a probabilistic framework, in which uncertainty is represented by a so-called posterior probability distribution. Recently, variational inference has emerged as an efficient method to estimate Bayesian solutions. By seeking the closest approximation to the posterior distribution within any chosen family of distributions, variational inference yields a fully probabilistic solution. It is important to define expressive variational families so that the posterior distribution can be represented accurately. We introduce boosting variational inference (BVI) as a computationally efficient means to construct a flexible approximating family comprising all possible finite mixtures of simpler component distributions. We use Gaussian mixture components due to their fully parametric nature and the ease with which they can be optimised. We apply BVI to seismic travel time tomography and full waveform inversion, comparing its performance with other methods of solution. The results demonstrate that BVI achieves reasonable efficiency and accuracy while enabling the construction of a fully analytic expression for the posterior distribution. Samples that represent major components of uncertainty in the solution can be obtained analytically from each mixture component. We demonstrate that these samples can be used to solve an interrogation problem: to assess the size of a subsurface target structure. To the best of our knowledge, this is the first method in geophysics that provides both analytic and reasonably accurate probabilistic solutions to fully non-linear, high-dimensional Bayesian full waveform inversion problems.

الوصول الحر: http://arxiv.org/abs/2312.17646Test

المؤلفون: Zhang, Xin, Curtis, Andrew

مصطلحات موضوعية: Physics - Geophysics

الوصف: Bayesian inference has become an important tool to solve inverse problems and to quantify uncertainties in their solutions. Variational inference is a method that provides probabilistic, Bayesian solutions efficiently by using optimization. In this study we present a Python Variational Inversion Package (VIP), to solve inverse problems using variational inference methods. The package includes automatic differential variational inference (ADVI), Stein variational gradient descent (SVGD) and stochastic SVGD (sSVGD), and provides implementations of 2D travel time tomography and 2D full waveform inversion including test examples and solutions. Users can solve their own problems by supplying an appropriate forward function and a gradient calculation code. In addition, the package provides a scalable implementation which can be deployed easily on a desktop machine or using modern high performance computational facilities. The examples demonstrate that VIP is an efficient, scalable, extensible and user-friendly package, and can be used to solve a wide range of low or high dimensional inverse problems in practice.

الوصول الحر: http://arxiv.org/abs/2310.13325Test

المؤلفون: Zhang, Xin, Curtis, Andrew

مصطلحات موضوعية: Physics - Geophysics, Physics - Data Analysis, Statistics and Probability

الوصف: Time-lapse seismic full-waveform inversion (FWI) provides estimates of dynamic changes in the subsurface by performing multiple seismic surveys at different times. Since FWI problems are highly non-linear and non-unique, it is important to quantify uncertainties in such estimates to allow robust decision making. Markov chain Monte Carlo (McMC) methods have been used for this purpose, but due to their high computational cost, those studies often require an accurate baseline model and estimates of the locations of potential velocity changes, and neglect uncertainty in the baseline velocity model. Such detailed and accurate prior information is not always available in practice. In this study we use an efficient optimization method called stochastic Stein variational gradient descent (sSVGD) to solve time-lapse FWI problems without assuming such prior knowledge, and to estimate uncertainty both in the baseline velocity model and the velocity change. We test two Bayesian strategies: separate Bayesian inversions for each seismic survey, and a single join inversion for baseline and repeat surveys, and compare the methods with the standard linearised double difference inversion. The results demonstrate that all three methods can produce accurate velocity change estimates in the case of having fixed (exactly repeatable) acquisition geometries, but that the two Bayesian methods generate more accurate results when the acquisition geometry changes between surveys. Furthermore the joint inversion provides the most accurate velocity change and uncertainty estimates in all cases. We therefore conclude that Bayesian time-lapse inversion, especially adopting a joint inversion strategy, may be useful to image and monitor the subsurface changes, in particular where uncertainty in the results might lead to significantly different decisions.

الوصول الحر: http://arxiv.org/abs/2308.08805Test

المؤلفون: Strutz, Dominik, Curtis, Andrew

مصطلحات موضوعية: Physics - Geophysics

الوصف: This paper introduces variational design methods that are novel to Geophysics, and discusses their benefits and limitations in the context of geophysical applications and more established design methods. Variational methods rely on functional approximations to probability distributions and model-data relationships. They can be used to design experiments that best resolve either all model parameters, or the answer to specific questions about the system to be interrogated. The methods are tested in three schematic geophysical applications: (i) estimating a source location given arrival times at sensor locations, and (ii) estimating the contrast in seismic wavefield velocity across a stratal interface given measurements of the amplitudes of seismic wavefield reflections from that interface, and (iii) designing a survey to best constrain CO2 saturation in a subsurface storage scenario. Variational methods allow the value of an experiment to be calculated and optimised simultaneously, which results in substantial savings in computational cost. In the context of designing a survey to best constrain CO2 in a subsurface storage scenario, we show that optimal designs may change substantially depending on the questions of interest. Overall, this work indicates that optimal design methods should be used more widely in Geophysics, as they are in other scientifically advanced fields.
Comment: Submitted to Geophysical Journal International in June 2023

الوصول الحر: http://arxiv.org/abs/2307.01039Test

المؤلفون: Ludlam, James, Tant, Katherine, Dolean, Victorita, Curtis, Andrew

مصطلحات موضوعية: Physics - Geophysics, 65N06 (Primary) 86-10, 00A69, 35L05 (Secondary)

الوصف: Wavefield travel time tomography is used for a variety of purposes in acoustics, geophysics and non-destructive testing. Since the problem is non-linear, assessing uncertainty in the results requires many forward evaluations. It is therefore important that the forward evaluation of travel times and ray paths is efficient, which is challenging in anisotropic media. Given a computed travel time field, ray tracing can be performed to obtain the fastest ray path from any point in the medium to the source of the travel time field. These rays can then be used to speed up gradient based inversion methods. We present a forward modeller for calculating travel time fields by localised estimation of wavefronts, and a novel approach to ray tracing through travel time fields. These methods have been tested in a complex anisotropic weld and give travel times comparable to those obtained using finite element modelling while being computationally cheaper.

الوصول الحر: http://arxiv.org/abs/2302.10988Test

المؤلفون: Bloem, Hugo, Curtis, Andrew, Tetzlaff, Daniel

المصدر: BRE-092-2023

مصطلحات موضوعية: Physics - Geophysics

الوصف: Geological process models simulate a range of dynamic processes to evolve a base topography into a final 2D cross-section or 3D geological scenario. In principle, process parameters may be updated to better align with observed geophysical or geological data; however, many realisations of process models that embody different conceptual models may provide similar consistency with observed data, and finding all such realisations may be infeasible due to the computational demands of the task. Alternatively, geophysical probabilistic tomographic methods may be used to estimate the family of models of a target subsurface structure that are consistent both with information obtained from previous experiments and with new data (the Bayesian posterior distribution). However, this family seldom embodies geologically reasonable images. We show that the posterior distribution of tomographic images can be enhanced by injecting geological prior information into Bayesian inference, and we can do this near-instantaneously by trained MDNs. We invoke two geological concepts as geological prior information: a braided river system, and a set of marine parasequences, each parameterised by a GAN. Data from a target structure can then be used to infer the image parameter values using either geological concept using MDNs. Our MDN solutions closely resemble those found using expensive McMC methods, and while the use of incorrect geological conceptual models provides less accurate results the mean structures still approximate the target. We then show that a classification network can infer the correct geological conceptual model. Thus, imposing even incorrect geological prior information may improve geophysical tomographic images compared to those obtained without geological prior information, and in principle geological conceptual models can be inferred directly from geophysical travel time data.

الوصول الحر: http://arxiv.org/abs/2210.07892Test