scholarly journals Resolution enhancement of non-stationary seismic data using amplitude-frequency partition

2014 ◽  
Vol 200 (2) ◽  
pp. 773-778 ◽  
Author(s):  
Yujiang Xie ◽  
Gao Liu
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

Seismic resolution enhancement in shale-oil reservoirs

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. B281-B287 ◽  
Author(s):  
Xiwu Liu ◽  
Fengxia Gao ◽  
Yuanyin Zhang ◽  
Ying Rao ◽  
Yanghua Wang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Oil Reservoirs ◽  
Inversion Method ◽  
Shale Oil ◽  
Data Set ◽  
Extension Method ◽  
Seismic Resolution ◽  
Full Band ◽  
Shale Oil Reservoirs

We developed a case study of seismic resolution enhancement for shale-oil reservoirs in the Q Depression, China, featured by rhythmic bedding. We proposed an innovative method for resolution enhancement, called the full-band extension method. We implemented this method in three consecutive steps: wavelet extraction, filter construction, and data filtering. First, we extracted a constant-phase wavelet from the entire seismic data set. Then, we constructed the full-band extension filter in the frequency domain using the least-squares inversion method. Finally, we applied the band extension filter to the entire seismic data set. We determined that this full-band extension method, with a stretched frequency band from 7–70 to 2–90 Hz, may significantly enhance 3D seismic resolution and distinguish reflection events of rhythmite groups in shale-oil reservoirs.

Deep learning for multitrace sparse-spike deconvolution

Geophysics ◽  
2021 ◽  
pp. 1-64
Author(s):  
Xintao Chai ◽  
Genyang Tang ◽  
Kai Lin ◽  
Zhe Yan ◽  
Hanming Gu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Resolution Enhancement ◽  
Parameter Tuning ◽  
Seismic Exploration ◽  
3D Seismic ◽  
Spatial Continuity ◽  
Additional Information ◽  
3D Data ◽  
3D Seismic Data

Sparse-spike deconvolution (SSD) is an important method for seismic resolution enhancement. With the wavelet given, many trace-by-trace SSD methods have been proposed for extracting an estimate of the reflection-coefficient series from stacked traces. The main drawbacks of the trace-by-trace methods are that they neither use the information from the adjacent seismograms and nor take full advantage of the inherent spatial continuity of the seismic data. Although several multitrace methods have been consequently proposed, these methods generally rely on different assumptions and theories and require different parameter settings for different data applications. Therefore, the traditional methods demand intensive human-computer interaction. This requirement undoubtedly does not fit the current dominant trend of intelligent seismic exploration. Therefore, we have developed a deep learning (DL)-based multitrace SSD approach. The approach transforms the input 2D/3D seismic data into the corresponding SSD result by training end-to-end encoder-decoder-style 2D/3D convolutional neural networks (CNNs). Our key motivations are that DL is effective for mining complicated relations from data, the 2D/3D CNNs can take multitrace information into account naturally, the additional information contributes to the SSD result with better spatial continuity, and parameter tuning is not necessary for CNN predictions. We report the significance of the learning rate for the training process's convergence. Benchmarking tests on the field 2D/3D seismic data confirm that the approach yields accurate high-resolution results that are mostly in agreement with the well logs; the DL-based multitrace SSD results generated by the 2D/3D CNNs are better than the trace-by-trace SSD results; and the 3D CNN outperforms the 2D CNN for 3D data application.

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.

Vertical resolution enhancement of seismic data with convolutional U-net

2019 ◽  
Author(s):  
Yonggyu Choi ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Vertical Resolution

scholarly journals Virtual resolution enhancement: A new enhancement tool for seismic data

2020 ◽  
Vol 12 (1) ◽  
pp. 363-375
Author(s):  
Mohamed A. Rashed ◽  
Ali H. Atef
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Computational Time ◽  
Seismic Events ◽  
Seismic Section ◽  
Mathematical Rule ◽  
Different Types ◽  
Seismic Sections ◽  
Data Acquisition And Processing ◽  
Near Future

AbstractVirtual resolution enhancement (VRE) is a new poststack cosmetic tool that can be applied to different types of seismic data. VRE emphasizes major reflections, enhances temporal resolution of seismic events, and suppresses reverberation noise, leading to better visualization of the entire seismic data. VRE is based on simple mathematical rule, and its parameters can be tweaked to suite the vast variety of seismic data available today. Although VRE does not reveal new or hidden features on seismic section, it significantly enhances the existing ones, which improves the interpretation and assists automatic horizon picking process. The only disadvantage of VRE is the long computational time. However, given the giant advances in the computational power and speed expected in the near future, this problem should be negligible. Tests conducted on seismic sections, collected from different regions in the world and went through different data acquisition and processing routines, prove the effectiveness of the VRE procedure.

Resolution enhancement with relative amplitude preservation for unconventional targets

2018 ◽  
Vol 6 (3) ◽  
pp. SH59-SH71 ◽  
Author(s):  
Anna Kwietniak ◽  
Kamil Cichostępski ◽  
Kaja Pietsch
Keyword(s):  
Seismic Data ◽  
Amplitude Ratio ◽  
Resolution Enhancement ◽  
Seismic Inversion ◽  
Primary Objective ◽  
Thin Layers ◽  
Relative Amplitude ◽  
Baltic Basin ◽  
Structural Interpretation ◽  
Amplitude Preservation

Our primary objective was to evaluate a method that enhances the resolution of 3D seismic data that does not disturb the relative amplitude preservation. The formations that are the subject of the analysis are Lower Silurian: the Jantar Formation and the Ordovician Sasino Formation (the onshore part of the Baltic Basin, northern Poland). Both formations are seismically thin layers and have been recent targets for unconventional exploration. Resolution enhancement designed to help the structural interpretation may enable precise structural interpretation of thinly layered intervals. The method that we applied is poststack spectral blueing. To verify the effectiveness of the spectral blueing procedure, we designed an algorithm that compares the amplitude values along evenly distributed seismic traces. The algorithm addresses the preservation of the relative amplitude ratio. We did not want to disturb the amplitude values by the enhancement algorithm and introduce information that would be false for seismic inversion analysis. Hence, it was crucial for us to obtain the enhanced seismic volume suitable for structural interpretation that holds relative amplitude relation criterion. The algorithm helped obtain the optimal enhanced seismic volume that is preferable for the structural interpretation of seismic data and possibly could be used successfully for a seismic inversion process. With the optimal enhanced seismic volume, we were able to conduct a more accurate structural interpretation — an entirely new seismic horizon that indicates that the top of one of the formations under analysis was clearly visible and thus possible for interpretation. We applied the acoustic inversion to the original and the enhanced seismic data — the latter enabled the determination of two additional anomalous zones that had not been previously possible to distinguish within the seismic volume.

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