MCMC Method of Inverse Problems Using a Neural Network—Application in GPR Crosshole Full Waveform Inversion: A Numerical Simulation Study

Wang, Shengchao; Han, Liguo; Gong, Xun; Zhang, Shaoyue; Huang, Xingguo; Zhang, Pan

doi:10.3390/rs14061320

Cited by 5 publications

(2 citation statements)

References 32 publications

(34 reference statements)

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“…Sun et al [24] accomplished low-frequency extrapolation of multicomponent data in Elastic FWI using deep learning. Wang et al [25] used the MCMC inverse problem method of neural networks to perform numerical simulations in GPR cross-hole full waveform inversion. Liu et al [26] used fine-tuned FPN to achieve microseismic first-arrival pickup.…”

Section: Introductionmentioning

confidence: 99%

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Zhao,

Han,

Zhang

et al. 2024

Applied Sciences

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In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent noise. These artifacts and coherent noise interfere with the valid information in the virtual shot record, making the virtual shot record a poorer presentation of subsurface information. In this paper, we utilize the powerful learning and data processing abilities of convolutional neural networks to process virtual shot recordings of sources in undesirable situations. We add an adaptive attention mechanism to the network so that it can automatically lock the positions that need special attention and processing in the virtual shot records. After testing, the trained network can eliminate coherent noise and artifacts and restore real reflected waves. Protecting valid signals means restoring valid signals with waveform anomalies to a reasonable shape.

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Section: Introductionmentioning

confidence: 99%

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Zhao,

Han,

Zhang

et al. 2024

Applied Sciences

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“…Neural networks are widely employed to calculate various models and algorithms for the underwater remote sensing of objects and bottom layers (see, e.g., [25][26][27][28]). Despite this, the use of the mismatch function, built on a neural basis for detecting and evaluating the parameters of received signals against various background noises, in our opinion, was proposed for the first time in [17].…”

Section: Introductionmentioning

confidence: 99%

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

et al. 2022

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The well-known method of detecting a useful signal in the presence of noise during underwater remote sensing, based on the matched filtering of the received signal with the test signal, provides the maximum signal-to-noise ratio (SNR) at the receiver output. To do this, a correlation-type criterion function (CF) is constructed for the received and test signals. In the case of large volumes of processed data, this method requires the use of large computing resources. The search for a data processing method with lower computational costs, as well as the effective application of artificial neural networks to array signal processing, motivates the authors to propose an alternative approach to the CF construction based on the McCulloch–Pitts neuron model. Such a neuron-like CF is based on a specific nonlinear transformation of the input and test signals and uses only logical operations, which require much less computational resources. The ratio of the output signal amplitude to the input noise level is indeed the maximum with matched filtering. Studies have shown that it is not this parameter that should be considered, but statistical characteristics, on the basis of which the thresholds for detecting a signal in the presence of noise are determined. Such characteristics include the probability density distributions of correlation and neuron-like CFs in the presence and absence of noise. In this case, the signal detection thresholds will be lower for the neuron-like CF than for the conventional correlation CF. The aim of this research is to increase the accuracy of the selection of a useful signal against the intense noise background when using a processor based on the neuron-like CF and to determine the conditions when the input SNR, at which signal detection is possible, is lower compared to the correlation CF. The comparative results of stochastic modeling show the effectiveness of using a new neuron-like approach to reduce the detection threshold when a chirp signal is received against a background of unsteady Gaussian noise. The advantages of the neuron-like method become significant when the statistical distribution of the additive noise does not change, but its variance increases or decreases. In order to confirm the presence of non-stationarity in real noises, experimental data obtained from the remote sounding of bottom sediments in the Black Sea are presented. The results obtained are considered to be applicable in a wide range of practical situations related to remote sensing in non-stationary environments, long-range sonar and sea bottom exploration.

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Laplace based Bayesian inference for ordinary differential equation models using regularized artificial neural networks

Kwok,

Streftaris,

Dass

2023

Stat Comput

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Parameter estimation and associated uncertainty quantification is an important problem in dynamical systems characterised by ordinary differential equation (ODE) models that are often nonlinear. Typically, such models have analytically intractable trajectories which result in likelihoods and posterior distributions that are similarly intractable. Bayesian inference for ODE systems via simulation methods require numerical approximations to produce inference with high accuracy at a cost of heavy computational power and slow convergence. At the same time, Artificial Neural Networks (ANN) offer tractability that can be utilized to construct an approximate but tractable likelihood and posterior distribution. In this paper we propose a hybrid approach, where Laplace-based Bayesian inference is combined with an ANN architecture for obtaining approximations to the ODE trajectories as a function of the unknown initial values and system parameters. Suitable choices of customized loss functions are proposed to fine tune the approximated ODE trajectories and the subsequent Laplace approximation procedure. The effectiveness of our proposed methods is demonstrated using an epidemiological system with non-analytical solutions—the Susceptible-Infectious-Removed (SIR) model for infectious diseases—based on simulated and real-life influenza datasets. The novelty and attractiveness of our proposed approach include (i) a new development of Bayesian inference using ANN architectures for ODE based dynamical systems, and (ii) a computationally fast posterior inference by avoiding convergence issues of benchmark Markov Chain Monte Carlo methods. These two features establish the developed approach as an accurate alternative to traditional Bayesian computational methods, with improved computational cost.

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MCMC Method of Inverse Problems Using a Neural Network—Application in GPR Crosshole Full Waveform Inversion: A Numerical Simulation Study

Cited by 5 publications

References 32 publications

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

Laplace based Bayesian inference for ordinary differential equation models using regularized artificial neural networks

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