scholarly journals Inverse Problems and Artificial Intelligence

2021 ◽  
pp. 33-43
Author(s):  
С.И. Кабанихин
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
A Priori ◽  
Stability And Convergence ◽  
Computational Algorithms ◽  
Conditional Stability ◽  
A Priori Information ◽  
Sensitivity Data ◽  
Priori Information ◽  
The Relationship

В данной работе приведен анализ взаимосвязей теории обратных и некорректных задач и математических аспектов искусственного интеллекта. Показано, что при анализе вычислительных алгоритмов, которые условно можно отнести к вычислительному искусственному интеллекту (машинное обучение, природоподобные алгоритмы, методы анализа и обработки данных), возможно, а подчас и необходимо, использовать результаты и подходы, развитые в теории и численных методах решения обратных и некорректных задач, такие как регуляризация, условная устойчивость и сходимость, использование априорной информации, идентифицируемость, чувствительность, усвоение данных. This paper analyzes the relationship between the theory of inverse and incorrect problems and the mathematical aspects of artificial intelligence. It is shown that computational algorithms that can be categorized as computational artificial intelligence (machine learning, nature-like algorithms, data analysis and processing) can or should be analyzed with the approaches developed for the theory and numerical methods for solving inverse and incorrect problems. They are regularization, conditional stability and convergence, the use of a priori information, identifiability, sensitivity, data assimilation.

Deep learning spectral enhancement considering features of seismic field data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Yonggyu Choi ◽  
Yeonghwa Jo ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Input Data ◽  
Field Data ◽  
A Priori ◽  
Resolution Enhancement ◽  
Training Data ◽  
A Priori Information ◽  
Spectral Enhancement ◽  
Priori Information

The resolution of seismic data dictates the ability to identify individual features or details in a given image, and the temporal (vertical) resolution is a function of the frequency content of a signal. To improve thin-bed resolution, broadening of the frequency spectrum is required; this has been one of the major objectives in seismic data processing. Recently, many researchers have proposed machine learning based resolution enhancement and showed their applicability. However, since the performance of machine learning depends on what the model has learned, output from training data with features different from the target field data may be poor. Thus, we present a machine learning based spectral enhancement technique considering features of seismic field data. We used a convolutional U-Net model, which preserves the temporal connectivity and resolution of the input data, and generated numerous synthetic input traces and their corresponding spectrally broadened traces for training the model. A priori information from field data, such as the estimated source wavelet and reflectivity distribution, was considered when generating the input data for complementing the field features. Using synthetic tests and field post-stack seismic data examples, we showed that the trained model with a priori information outperforms the models trained without a priori information in terms of the accuracy of enhanced signals. In addition, our new spectral enhancing method was verified through the application to the high-cut filtered data and its promising features were presented through the comparison with well log data.

Particle swarm optimization of 2D magnetotelluric data

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. E125-E141 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
A Priori ◽  
Particle Swarm ◽  
Stability And Convergence ◽  
Real Field ◽  
A Priori Information ◽  
Data Set ◽  
Swarm Optimization ◽  
Priori Information

We implement the particle swarm optimization (PSO) algorithm for the two-dimensional (2D) magnetotelluric (MT) inverse problem. We first validate PSO on two synthetic models of different complexity and then apply it to an MT benchmark for real-field data, the COPROD2 data set (Canada). We pay particular attention to the selection of the PSO input parameters to properly address the complexity of the 2D MT inverse problem. We enhance the stability and convergence of the solution of the geophysical problem by applying the hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC). Moreover, we parallelize the code to reduce the computation time because PSO is a computationally demanding global search algorithm. The inverse problem was solved for the synthetic data both by giving a priori information at the beginning and by using a random initialization. The a priori information was given to a small number of particles as the initial position within the search space of solutions, so that the swarming behavior was only slightly influenced. We have demonstrated that there is no need for the a priori initialization to obtain robust 2D models because the results are largely comparable with the results from randomly initialized PSO. The optimization of the COPROD2 data set provides a resistivity model of the earth in line with results from previous interpretations. Our results suggest that the 2D MT inverse problem can be successfully addressed by means of computational swarm intelligence.

Multilayer Neural Networks and Function Reconstruction By Using a Priori Knowledge

1999 ◽  
Vol 09 (03) ◽  
pp. 251-256 ◽  
Author(s):  
L.C. PEDROZA ◽  
C.E. PEDREIRA
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
A Priori Information ◽  
Multilayer Neural Networks ◽  
Priori Information ◽  
And Function ◽  
Limited Spectrum ◽  
The Relationship

This paper proposes a new methodology to approximate functions by incorporating a priori information. The relationship between the proposed scheme and multilayer neural networks is explored theoretically and numerically. This approach is particularly interesting for the very relevant class of limited spectrum functions. The number of free parameters is smaller if compared to Back-Propagation Algorithm opening the way for better generalization results.

Wavelength and vulnerability to atrial fibrillation: Insights from a computer model of human atria

EP Europace ◽  
2005 ◽  
Vol 7 (s2) ◽  
pp. S83-S92 ◽  
Author(s):  
Vincent Jacquemet ◽  
Nathalie Virag ◽  
Lukas Kappenberger
Keyword(s):  
Atrial Fibrillation ◽  
Computer Model ◽  
A Priori ◽  
Temporal Organization ◽  
A Priori Information ◽  
Clinical Methods ◽  
Spatio Temporal ◽  
Priori Information ◽  
The Impact ◽  
The Relationship

Abstract Aim To explain the contradictory results related to the concept of critical cardiac wavelength in the initiation and perpetuation of atrial fibrillation (AF). Methods A biophysically based computer model was used to: (1) study the relationship between wavelength and AF perpetuation in the presence of multiple re-entrant wavelets, (2) evaluate the performance of different existing methods for wavelength estimation in the presence of different arrhythmogenic substrates, and (3) document the impact of either heterogeneities in refractoriness or the presence of a mother rotor on wavelength estimation. Results The simulations confirmed that the wavelength must be below a critical value for AF to be sustained, when the perpetuation mechanism relies on multiple re-entrant wavelets. The estimated value of wavelength was not the same for all methods tested and depended in part on the nature of the spatio-temporal organization of the AF dynamics. Conclusion A priori information about the underlying wavelet dynamics is needed for a correct interpretation of the cardiac wavelength as estimated by the current clinical methods.

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