scholarly journals Deep Learning Approach for Processing Fiber-Optic DAS Seismic Data

2018 ◽  
Author(s):  
Lihi Shiloh ◽  
Avishay Eyal ◽  
Raja Giryes
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Fiber Optic ◽  
Learning Approach

Swell-noise attenuation: A deep learning approach

2019 ◽  
Vol 38 (12) ◽  
pp. 934-942 ◽  
Author(s):  
Xing Zhao ◽  
Ping Lu ◽  
Yanyan Zhang ◽  
Jianxiong Chen ◽  
Xiaoyang Li
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Learning Model ◽  
Noise Model ◽  
Learning Approach ◽  
Data Sets ◽  
Great Success ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Deep Learning Model

Noise attenuation for ordinary images using machine learning technology has achieved great success in the computer vision field. However, directly applying these models to seismic data would not be effective since the evaluation criteria from the geophysical domain require a high-quality visualized image and the ability to maintain original seismic signals from the contaminated wavelets. This paper introduces an approach equipped with a specially designed deep learning model that can effectively attenuate swell noise with different intensities and characteristics from shot gathers with a relatively simple workflow applicable to marine seismic data sets. Three significant benefits are introduced from the proposed deep learning model. First, our deep learning model doesn't need to consume a pure swell-noise model. Instead, a contaminated swell-noise model derived from field data sets (which may contain other noises or primary signals) can be used for training. Second, inspired by the conventional algorithm for coherent noise attenuation, our neural network model is designed to learn and detect the swell noise rather than inferring the attenuated seismic data. Third, several comparisons (signal-to-noise ratio, mean squared error, and intensities of residual swell noises) indicate that the deep learning approach has the capability to remove swell noise without harming the primary signals. The proposed deep learning-based approach can be considered as an alternative approach that combines and takes advantage of both the conventional and data-driven method to better serve swell-noise attenuation. The comparable results also indicate that the deep learning method has strong potential to solve other coherent noise-attenuation tasks for seismic data.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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