2024 Deep learning geoacoustic inversion

Deep learning geoacoustic inversion

Author: kpql

August undefined, 2024

WebAbstract. This paper develops a general trans-dimensional Bayesian methodology for geoacoustic inversion. Trans-dimensional inverse problems are a generalization of fixed-dimensional inversion that includes the number and type of model parameters as unknowns in the problem. By extending the inversion state space to multiple subspaces of ... WebThe key to model-based Bayesian geoacoustic inversion is to solve the posterior probability distributions (PPDs) of parameters. In order to obtain PPDs more efficiently and accurately, the state-of-the-art Markov chain Monte Carlo (MCMC) method, multiple-try differential evolution adaptive Metropolis(ZS) (MT-DREAM(ZS)), is integrated to the …

Bayesian geoacoustic inversion using mixture density network

WebApr 8, 2024 · Physics-Constrained Deep Learning of Geomechanical Logs. 地震数据点云上采样. Deep Learning for Irregularly and Regularly Missing 3-D Data Reconstruction. 地震检测. Intelligent Real-Time Earthquake Detection by Recurrent Neural Networks. 地震数据反演. Well-Logging Constrained Seismic Inversion Based on Closed-Loop ... root pack cuphead plush

Geoacoustic Inversion Based on Neural Network IEEE …

WebJul 27, 2024 · Abstract. This paper reviews the progress in geoacoustic inversion over the past several decades. The review is separated into two parts. The first part reviews developments in model-based ... Weblr - learning rate for the optimizer of input tensor for model inversion. do_flip - will do random flipping between iterations; exp_name - name of the experiment, will create folder with this name in ./generations/ where intermediate generations will … WebJan 1, 2024 · The ray-based blind deconvolution algorithm can provide an estimate of the channel impulse responses (CIRs) between a shipping source of opportunity and the elements of a receiving array by estimating the unknown phase of this random source through wideband beamforming along a well-resolved ray path. root pack cuphead

‪Mingda Liu‬ - ‪Google Scholar‬

WebABSTRACT The lack of low-frequency signals in seismic data makes the full-waveform inversion (FWI) procedure easily fall into local minima leading to unreliable results. To reconstruct the missing low-frequency signals more accurately and effectively, we have developed a data-driven low-frequency recovery method based on deep learning from … WebNov 1, 2024 · Deep learning inversion flowchart. The entire DL network performs nonlinear changes to the original input sea clutter data layer by layer (Jiaxuan, 2024); thus, it continuously fits the relationship between sea clutter power and profile parameters. Each layer of the network consists of a large number of neurons, with a large number of ... root pack namesWebA multi-range vertical array data processing (MRP) method based on a convolutional neural network (CNN) is proposed to estimate geoacoustic parameters in shallow water. The network input is the normalized sample covariance matrices of the broadband multi-range data received by a vertical line array. Since the geoacoustic parameters (e.g ... root pack plush

"WebNov 30, 2024 · 1 College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310000, China; 2 Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA; b) Electronic mail: [email protected], ORCID: 0000-0001-9041-1513. c) ORCID: 0000-0002-0471-062X. This paper is part of a … " - Deep learning geoacoustic inversion

Deep learning geoacoustic inversion

Range-dependent geoacoustic inversion using equivalent …

WebDec 1, 2024 · Geoacoustic inversion is an efficient method to study the physical properties and structure of ocean bottom while sequential geoacoustic inversion is a challenging task due to the complexity and non-linearity of the underwater environment. WebJun 3, 2024 · We present a review of deep learning (DL), a popular AI technique, for geophysical readers to understand recent advances, …

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WebA multi-range vertical array data processing (MRP) method based on a convolutional neural network (CNN) is proposed to estimate geoacoustic parameters in shallow water. The network input is the normalized sample covariance matrices of the broadband multi-range data received by a vertical line array. WebDeep-learning geoacoustic inversion using multi-range vertical array data in shallow water. M Liu, H Niu, Z Li, Y Liu, Q Zhang. The Journal of the Acoustical Society of America 151 (3), 2101-2116, 2024. 3: 2024: Feature visualizations in geoacoustic inversion using convolutional neural network.

WebSep 23, 2024 · Geoacoustic Inversion Based on Neural Network Abstract: Traditional inversion methods, such as the matched field inversion, modal dispersion inversion, have been proposed and got good results. Still, the computing time of these methods is long due to large search space. WebMar 1, 1998 · A Tabu-based method is developed for source localization and geoacoustic inversion with underwater sound data; the method relies on memory to guide the multiparameter search and is found to be superior to the simulated annealing variant implemented here in terms both of accuracy and efficiency.

WebOct 29, 2024 · The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information about events observed in acoustic data. WebGeoacoustic inversion of vertical line array data in shallow water with an ice cover. Abstract: A technique for solving the inverse problem of estimating the effective acoustic parameters of the bottom is developed for shallow water with an ice cover.

Webinversion used a multi-layer perceptron-based technique composed of a much shallower network than the state-of-the-art deep neural network models (Park and Kennedy, 1996; Jain and Ali, 2006).

WebAn Optimization Method for Sound Speed Profile Inversion Using Empirical Orthogonal Function Analysis. ... Geoacoustic inversion based on matched impulse response processing for moving source. ... A robust traffic scene recognition algorithm based on deep learning and Markov localization. root page / redirects to: login.phpWebFeb 28, 2000 · Geoacoustic inversion Signal processing Sound velocity measurement ABSTRACT Matched-field processing (MFP) and global inversion techniques have been applied to vocalizations from four whales recorded on a 48-element tilted vertical array off the Channel Islands in 1996. root pack backgroundWebNov 1, 2024 · The model is applied to the inversion problem of atmospheric refractivity estimation, and the inversion results are analyzed to verify the feasibility of deep learning in the inversion problem. We herein report the high-precision inversion results of the atmospheric refractivity estimation. root pack carrotWebMatched-field acoustic inversion techniques were investigated using data from sparsely populated vertical receiving arrays and a single receiver element. The purpose of considering sparse data sets is to investigate, under simulation, the feasibility of reducing the number and complexity of acoustic measurements needed for geoacoustic inversion. root pack radishWebOct 10, 2024 · Geoacoustic Inversion Using an Autonomous Underwater Vehicle in Conjunction With Distributed Sensors. Abstract: A midfrequency (2-6 kHz) geoacoustic parameter inversion approach is proposed using a moving source and single receiver. The source motion creates a synthetic horizontal line array (SHLA), and the received signals, … root painterWebHybrid geoacoustic inversion of broadband Mediterranean Sea data This paper describes an acoustic experiment (PROSIM'97) carried out to investigate inversion for seabed properties at a site off the west coast of Italy where previous acoustic and geophysical studies have been performed. root packedWebOct 10, 2024 · A midfrequency (2-6 kHz) geoacoustic parameter inversion approach is proposed using a moving source and single receiver. The source motion creates a synthetic horizontal line array (SHLA), and the received signals, by source-receiver reciprocity, can be used to estimate the sediment/bottom parameters using matched field inversion (MFI). … root packed cuphead