A deep learning approach to design a borehole instrument for geosteering

Geophysics ◽  
2021 ◽  
pp. 1-34
Author(s):  
M. Shahriari ◽  
A. Hazra ◽  
D. Pardo
Keyword(s):  
Deep Neural Network ◽  
Synthetic Data ◽  
Learning Approach ◽  
Valuable Insight ◽  
Large Set ◽  
Measurement Systems ◽  
Borehole Logging ◽  
Resistivity Measurements ◽  
Online Evaluation ◽  
Electromagnetic Measurement

Deep Neural Network (DNN)-based methods are suitable for the rapid inversion of borehole resistivity measurements. They approximate the forward and the inverse problem offline during the training phase and they only require a fraction of a second for the online evaluation (aka prediction). Herein, we propose a DNN-based iterative algorithm to design a borehole instrument such that the inverse solution is unique for a given earth parametrization. We select a large set of electromagnetic measurement systems routinely employed in logging operations, and our proposed DNN algorithm selects a subset of measurements that are suitable for inversion purposes. Numerical results with synthetic data confirm that this approach can provide valuable insight when designing borehole logging instruments.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Learning Adaptive Coarse Spaces of BDDC Algorithms for Stochastic Elliptic Problems with Oscillatory and High Contrast Coefficients

2021 ◽  
Vol 26 (2) ◽  
pp. 44
Author(s):  
Eric Chung ◽  
Hyea-Hyun Kim ◽  
Ming-Fai Lam ◽  
Lina Zhao
Keyword(s):  
Deep Neural Network ◽  
Elliptic Problems ◽  
Learning Approach ◽  
High Contrast ◽  
Spectral Problems ◽  
Significant Challenge ◽  
Machine Learning Approach ◽  
Balancing Domain Decomposition ◽  
Computationally Expensive ◽  
Coarse Space

In this paper, we consider the balancing domain decomposition by constraints (BDDC) algorithm with adaptive coarse spaces for a class of stochastic elliptic problems. The key ingredient in the construction of the coarse space is the solutions of local spectral problems, which depend on the coefficient of the PDE. This poses a significant challenge for stochastic coefficients as it is computationally expensive to solve the local spectral problems for every realization of the coefficient. To tackle this computational burden, we propose a machine learning approach. Our method is based on the use of a deep neural network (DNN) to approximate the relation between the stochastic coefficients and the coarse spaces. For the input of the DNN, we apply the Karhunen–Loève expansion and use the first few dominant terms in the expansion. The output of the DNN is the resulting coarse space, which is then applied with the standard adaptive BDDC algorithm. We will present some numerical results with oscillatory and high contrast coefficients to show the efficiency and robustness of the proposed scheme.

scholarly journals A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data

2021 ◽  
Vol 210 ◽  
pp. 106371
Author(s):  
Elisa Moya-Sáez ◽  
Óscar Peña-Nogales ◽  
Rodrigo de Luis-García ◽  
Carlos Alberola-López
Keyword(s):  
Deep Learning ◽  
Synthetic Data ◽  
Learning Approach ◽  
And Training ◽  
Synthetic Mri

REAL-TIME 2.5D INVERSION OF LWD RESISTIVITY MEASUREMENTS USING DEEP LEARNING FOR GEOSTEERING APPLICATIONS ACROSS FAULTED FORMATIONS

2021 ◽  
Author(s):  
Kyubo Noh ◽  
◽  
Carlos Torres-Verdín ◽  
David Pardo ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Fault Plane ◽  
Synthetic Data ◽  
Field Measurements ◽  
Geological Structure ◽  
Inversion Method ◽  
Adaptive Finite Element ◽  
Resistivity Measurements ◽  
Lwd Resistivity

We develop a Deep Learning (DL) inversion method for the interpretation of 2.5-dimensional (2.5D) borehole resistivity measurements that requires negligible online computational costs. The method is successfully verified with the inversion of triaxial LWD resistivity measurements acquired across faulted and anisotropic formations. Our DL inversion workflow employs four independent DL architectures. The first one identifies the type of geological structure among several predefined types. Subsequently, the second, third, and fourth architectures estimate the corresponding spatial resistivity distributions that are parameterized (1) without the crossings of bed boundaries or fault plane, (2) with the crossing of a bed boundary but without the crossing of a fault plane, and (3) with the crossing of the fault plane, respectively. Each DL architecture employs convolutional layers and is trained with synthetic data obtained from an accurate high-order, mesh-adaptive finite-element forward numerical simulator. Numerical results confirm the importance of using multi-component resistivity measurements -specifically cross-coupling resistivity components- for the successful reconstruction of 2.5D resistivity distributions adjacent to the well trajectory. The feasibility and effectiveness of the developed inversion workflow is assessed with two synthetic examples inspired by actual field measurements. Results confirm that the proposed DL method successfully reconstructs 2.5D resistivity distributions, location and dip angles of bed boundaries, and the location of the fault plane, and is therefore reliable for real-time well geosteering applications.

scholarly journals Genre of Bangla Music: A Machine Classification Learning Approach

2019 ◽  
Vol 18 (2) ◽  
pp. 66-72
Author(s):  
Abhijit Bhowmik ◽  
AZM Ehtesham Chowdhury
Keyword(s):  
Classification Systems ◽  
Learning Approach ◽  
Large Set ◽  
Automated Classification ◽  
Classification Learning ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Music Databases ◽  
East Asian Language

The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.

LOCAL COORDINATES ALIGNMENT (LCA): A NOVEL MANIFOLD LEARNING APPROACH

2008 ◽  
Vol 22 (04) ◽  
pp. 667-690 ◽  
Author(s):  
TIANHAO ZHANG ◽  
XUELONG LI ◽  
DACHENG TAO ◽  
JIE YANG
Keyword(s):  
Manifold Learning ◽  
Geometrical Structure ◽  
Empirical Studies ◽  
Synthetic Data ◽  
Learning Approach ◽  
Real World Data ◽  
Local Coordinates ◽  
Out Of Sample ◽  
Local Patch ◽  
Alignment Technique

Manifold learning has been demonstrated as an effective way to represent intrinsic geometrical structure of samples. In this paper, a new manifold learning approach, named Local Coordinates Alignment (LCA), is developed based on the alignment technique. LCA first obtains local coordinates as representations of local neighborhood by preserving proximity relations on a patch, which is Euclidean. Then, these extracted local coordinates are aligned to yield the global embeddings. To solve the out of sample problem, linearization of LCA (LLCA) is proposed. In addition, in order to solve the non-Euclidean problem in real world data when building the locality, kernel techniques are utilized to represent similarity of the pairwise points on a local patch. Empirical studies on both synthetic data and face image sets show effectiveness of the developed approaches.

scholarly journals A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Ingenius ◽  
2021 ◽  
Author(s):  
Lucas C. Lampier ◽  
Yves L. Coelho ◽  
Eliete M. O. Caldeira ◽  
Teodiano Bastos-Filho
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Deep Learning ◽  
Open Access ◽  
Respiratory Rate ◽  
Analytical Methods ◽  
Deep Neural Network ◽  
Learning Approach ◽  
Genetic Optimization ◽  
Median Error

This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.

scholarly journals Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

2021 ◽  
Vol 14 (6) ◽  
pp. 4083-4110
Author(s):  
Meng Gao ◽  
Bryan A. Franz ◽  
Kirk Knobelspiesse ◽  
Peng-Wang Zhai ◽  
Vanderlei Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Deep Neural Network ◽  
Ocean Color ◽  
Synthetic Data ◽  
Forward Model ◽  
High Spectral Resolution ◽  
Acceptable Error ◽  
Microphysical Properties ◽  
Aerosol Properties

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

scholarly journals Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 554 ◽  
Author(s):  
Rashmi Sharan Sinha ◽  
Sang-Moon Lee ◽  
Minjoong Rim ◽  
Seung-Hoon Hwang
Keyword(s):  
Deep Learning ◽  
Indoor Environment ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Indoor Localization ◽  
Reference Points ◽  
Test Accuracy ◽  
Learning Approach ◽  
Location Error ◽  
User Location

In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices.

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