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Exploring types of photonic neural networks for imaging and computing—a review
Photonic neural networks (PNNs), utilizing light-based technologies, show immense
potential in artificial intelligence (AI) and computing. Compared to traditional electronic …
potential in artificial intelligence (AI) and computing. Compared to traditional electronic …
Hybrid seismic denoising using higher‐order statistics and improved wavelet block thresholding
We introduce a nondiagonal seismic denoising method based on the continuous wavelet
transform with hybrid block thresholding (BT). Parameters for the BT step are adaptively …
transform with hybrid block thresholding (BT). Parameters for the BT step are adaptively …
Self-adaptive denoising net: Self-supervised learning for seismic migration artifacts and random noise attenuation
Seismic noise attenuation is essential for seismic interpretation and reservoir
characterization. Recently, many researchers have applied convolutional neural network …
characterization. Recently, many researchers have applied convolutional neural network …
Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression
We develop an automated strategy for discriminating deep microseismic events from
shallow ones on the basis of the waveforms recorded on a limited number of surface …
shallow ones on the basis of the waveforms recorded on a limited number of surface …
Residual learning of deep convolutional neural network for seismic random noise attenuation
Over the last decades, seismic random noise attenuation has been dominated by transform-
based denoising methods over the last decades. However, these methods usually need to …
based denoising methods over the last decades. However, these methods usually need to …
Adaptive noise estimation and suppression for improving microseismic event detection
Microseismic data recorded by surface arrays are often strongly contaminated by unwanted
noise. This background noise makes the detection of small magnitude events difficult. A …
noise. This background noise makes the detection of small magnitude events difficult. A …
Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria
The fracture porosity is estimated especially through the log data (density, neutron porosity
and transit time) and the characteristics of the mud (fluid density, transit time of the saturating …
and transit time) and the characteristics of the mud (fluid density, transit time of the saturating …
Seismic characterization of fault and fractures in deep buried carbonate reservoirs using CNN-LSTM based deep neural networks
Geologic structures such as faults and fractures play a vital role in reservoir development
studies. Fault and fracture identification requires high-quality seismic amplitude data with …
studies. Fault and fracture identification requires high-quality seismic amplitude data with …
Strategy for automated analysis of passive microseismic data based on S-transform, Otsu's thresholding, and higher order statistics
Small-magnitude seismic events, either natural or induced microearthquakes, have
increasingly been used in exploration seismology with applications ranging from …
increasingly been used in exploration seismology with applications ranging from …
[كتاب][B] Application of soft computing and intelligent methods in geophysics
Inferences regarding the interior structure of the Earth, ranging from over 6000 km to just the
first few hundred meters of depth, have to be made to assess the potential for geohazards as …
first few hundred meters of depth, have to be made to assess the potential for geohazards as …