Deep learning reservoir porosity prediction based on multilayer long short-term memory network
The cost of obtaining a complete porosity value using traditional coring methods is relatively
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
Improving the signal‐to‐noise ratio of seismological datasets by unsupervised machine learning
Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong
noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon …
noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon …
Random noise attenuation based on residual convolutional neural network in seismic datasets
Seismic random noise attenuation is a key step in seismic data processing. The random
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …
Dictionary learning based on dip patch selection training for random noise attenuation
In recent years, sparse representation is seeing increasing application to fundamental signal
and image-processing tasks. In sparse representation, a signal can be expressed as a linear …
and image-processing tasks. In sparse representation, a signal can be expressed as a linear …
Fast waveform detection for microseismic imaging using unsupervised machine learning
Y Chen - Geophysical Journal International, 2018 - academic.oup.com
Automatic arrival picking of certain seismic or microseismic phases has been studied for
decades. However, automatic detection of continuous signal waveforms has been seldom …
decades. However, automatic detection of continuous signal waveforms has been seldom …
Deblending of simultaneous source data using a structure-oriented space-varying median filter
In seismic data processing, the median filter is usually applied along the structural direction
of seismic data in order to attenuate erratic or spike-like noise. The performance of a …
of seismic data in order to attenuate erratic or spike-like noise. The performance of a …
High-order directional total variation for seismic noise attenuation
X Liu, Q Li, C Yuan, J Li, X Chen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
High-amplitude noise could interfere with useful seismic signals, affecting our ability in
processing and interpreting seismic data. Thus, attenuating seismic noise is an important …
processing and interpreting seismic data. Thus, attenuating seismic noise is an important …
Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint
As full-waveform inversion (FWI) is a nonunique and typically ill-posed inversion problem, it
needs proper regularization to avoid cycle skipping. To reduce the nonlinearity of FWI, we …
needs proper regularization to avoid cycle skipping. To reduce the nonlinearity of FWI, we …
An anti-aliasing POCS interpolation method for regularly undersampled seismic data using curvelet transform
H Zhang, H Zhang, J Zhang, Y Hao, B Wang - Journal of Applied …, 2020 - Elsevier
Seismic data interpolation are considered the key step in data pre-processing. Most current
interpolation methods are just suitable for random undersampled cases. To deal with regular …
interpolation methods are just suitable for random undersampled cases. To deal with regular …
Automatic high-resolution microseismic event detection via supervised machine learning
Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic
status during the development of unconventional reservoirs. However, unlike the active …
status during the development of unconventional reservoirs. However, unlike the active …