Elastic prestack seismic inversion through discrete cosine transform reparameterization and convolutional neural networks

Aleardi, Mattia; Salusti, Alessandro

doi:10.1190/geo2020-0313.1

Cited by 34 publications

(14 citation statements)

References 65 publications

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“…e output results are calculated by logarithm and discrete cosine transform [12,13], and the oral English pronunciation features are extracted. e conversion formula between frequency and Mel frequency in this process is…”

Section: Oral Pronunciation Feature Extractionmentioning

confidence: 99%

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Blended Oral English Teaching Based on Core Competence Training Model

Jia-hui

2022

Mobile Information Systems

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The traditional mixed oral English teaching model has many obvious shortcomings, such as the inability to correct the students’ oral pronunciation errors and feed them back in time, which leads to the slow improvement of students’ English learning level. For this reason, this paper proposes a guided teaching model based on core literacy. According to the structure of the oral English mixed teaching model, determine the application plan of the oral English mixed teaching model, design the development environment, obtain the corpus, design the oral training model, extract the oral features, identify the wrong pronunciation and correct it in time, clarify the evaluation purpose, obtain preliminary evaluation indicators, reduce evaluation indicators and determine indicator weights, obtain indicator feature information, generate fuzzy rules, obtain fuzzy matrices, achieve quantitative evaluation, and synthesize all evaluation scores to construct a result vector matrix to realize the study of mixed spoken language teaching mode. Research shows that the mixed teaching method is effective and feasible and can effectively improve the accuracy of the evaluation results of the mixed oral English teaching model.

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Section: Oral Pronunciation Feature Extractionmentioning

confidence: 99%

“…In equation (12), η represents the learning coefficient, k represents the number of iterations, ω ij represents audio collection weight, d j represents the distance between audio nodes, y j represents the audio output value, u ij indicates the audio collection speed, and x ij represents the measured audio node.…”

Section: False Pronunciation Recognitionmentioning

confidence: 99%

Blended Oral English Teaching Based on Core Competence Training Model

Jia-hui

2022

Mobile Information Systems

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“…The ANN inversion seems to be well‐suited for uncertainty estimation via Markov Chain Monte Carlo algorithms (see, e.g. Hansen and Cordua, 2017; Aleardi and Salusti, 2021) due to the computational efficiency of ANN. For an accurate uncertainty appraisal using ANN inversion, both data noise and modelling errors introduced by the network approximation must be projected onto the model space.…”

Section: Discussionmentioning

confidence: 99%

An artificial neural network approach for the inversion of surface wave dispersion curves

Yablokov

Serdyukov

Loginov

et al. 2021

Geophysical Prospecting

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We describe a new algorithm for the inversion of one‐dimensional shear‐wave velocity profiles from dispersion curves of the fundamental mode of Rayleigh surface waves. The novelties of our approach are that the layer velocities and thicknesses are set as unknowns, and an artificial neural network is proposed to solve the inverse problem. We suggest that training data should be calculated for a set of random synthetic velocity layered models, while layer thicknesses and velocities should be set to fixed intervals, with ranges estimated based on the systematic application of empirical relations between Rayleigh and S‐wave velocities to the dispersion data. Our main challenge is a total overhaul of the artificial neural network, which includes selecting the optimal artificial neural network architecture and parameters by performing a large number of numerical experiments. Our synthetic results show that the accuracy of the proposed approach outperforms that of the Monte Carlo approach. We illustrate our proposed method with West Siberia data processing obtained from an area of approximately 800 km2. From a user perspective, the main strength of our method is the computationally efficient processing of large amounts of dispersion data, which make it well suited for four‐dimensional near‐surface monitoring.

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“…(2019), Wu and McMechan (2019), Puzyrev (2019), Park and Sacchi (2020), Sun et al. (), Aleardi (2020), Aleardi and Salusti (2020b) and Moghadas (2020).…”

Section: Introductionmentioning

confidence: 99%

Elastic properties and litho‐fluid facies estimation from pre‐stack seismic data through bidirectional long short‐term memory networks

Aleardi

2022

Geophysical Prospecting

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One of the main goals of pre‐stack seismic inversion is the estimation of elastic properties (i.e. P‐, S‐ wave velocities and density) and litho‐fluid classes in the investigated area. To this end, many inversion strategies have been proposed, but the most popular is based on a two‐step inversion approach: First, elastic properties are inferred from pre‐stack data, and then a classification algorithm is used to convert the outcomes of the first stage into litho‐fluid facies. In this work, we propose an alternative approach based on recurrent neural networks. We train two bidirectional long short‐term memory networks to predict the inverse mappings from pre‐stack seismic data to elastic properties, and litho‐fluid classes. In the elastic inversion, we also use a Monte Carlo simulation approach to properly propagate onto the model space both the uncertainties related to noise contamination in the data and to the modelling error introduced by the network approximation. One crucial aspect of any machine‐learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are drawn according to a previously defined elastic and facies prior models derived from actual well log recordings. In particular, we assume a Gaussian‐mixture elastic prior, and we also take into account the uncertainties affecting the estimation of the transition probabilities of facies. We invert each seismic gather independently, and in this context, the generation of the training set, and the learning process can be accomplished with a very limited computational effort on a common notebook. Once trained, the networks estimate the elastic properties, the litho‐fluid facies and the related uncertainties from the pre‐stack data in near real‐time. Synthetic and field data inversions are used to validate the proposed method. The network predictions are also benchmarked against the outcomes of a more standard two‐step approach that combines a linear elastic inversion and a subsequent point‐wise Bayesian classification. Our results demonstrate that the implemented algorithm guarantees more accurate elastic property estimations and facies predictions than the standard inversion strategy. In particular, the predictions provided by the long short‐term memory network are less affected by erroneous assumptions on the noise statistics and prior model and by errors in the estimated source wavelet.

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Elastic prestack seismic inversion through discrete cosine transform reparameterization and convolutional neural networks

Cited by 34 publications

References 65 publications

Blended Oral English Teaching Based on Core Competence Training Model

Blended Oral English Teaching Based on Core Competence Training Model

An artificial neural network approach for the inversion of surface wave dispersion curves

Elastic properties and litho‐fluid facies estimation from pre‐stack seismic data through bidirectional long short‐term memory networks

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