Broadband marine controlled-source electromagnetic for subsalt and around salt exploration

2016 ◽  
Vol 4 (4) ◽  
pp. T521-T531 ◽  
Author(s):  
Andrea Zerilli ◽  
Marco P. Buonora ◽  
Paulo T. L. Menezes ◽  
Tiziano Labruzzo ◽  
Adriano J. A. Marçal ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Signal To Noise ◽  
Data Set ◽  
Depth Imaging ◽  
Controlled Source ◽  
Research Survey ◽  
Starting Model ◽  
Seismic Images

Salt basins, mainly Tertiary basins with mobilized salt, are notoriously difficult places to explore because of the traditionally poor seismic images typically obtained around and below salt bodies. In areas where the salt structures are extremely complex, the seismic signal-to-noise ratio may still be limited and, therefore, complicate the estimation of the velocity field variations that could be used to migrate the seismic data correctly and recover a good image suitable for prospect generation. We have evaluated the results of an integrated seismic-electromagnetic (EM) two-step interpretation workflow that we applied to a broadband marine controlled-source EM (mCSEM) research survey acquired over a selected ultra-deepwater area of Espirito Santo Basin, Brazil. The presence of shallow allochthonous salt structures makes around salt and subsalt seismic depth imaging remarkably challenging. To illustrate the proposed workflow, we have concentrated on a subdomain of the mCSEM data set, in which a shallow allochthonous salt body has been interpreted before. In the first step, we applied a 3D pixel-based inversion to the mCSEM data intending to recover the first guess of the geometry and resistivity of the salt body, but also the background resistivity. As a starting model, we used a resistivity mesh given by seismic interpretation and resistivity information provided by available nearby wells. Then, we applied a structure-based inversion to the mCSEM data, in which the retrieved model in step one was used as an input. The goal of that second inversion was to recover the base of the salt interface. The top of the salt and the background resistivities remained fixed throughout the process. As a result, we were able to define better the base of the allochthonous salt body. That was reinterpreted approximately 300–700 m shallower than interpreted from narrow azimuth seismic.

scholarly journals First images and orientation of fine structure from a 3-D seismic oceanography data set

Ocean Science ◽  
2010 ◽  
Vol 6 (2) ◽  
pp. 431-439 ◽  
Author(s):  
T. M. Blacic ◽  
W. S. Holbrook
Keyword(s):  
Fine Structure ◽  
Internal Wave ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Quantitative Information ◽  
Data Set ◽  
Seismic Oceanography ◽  
Sinusoidal Surface ◽  
Seismic Images ◽  
Wide Swath

Abstract. We present 3-D images of ocean fine structure from a unique industry-collected 3-D multichannel seismic dataset from the Gulf of Mexico that includes expendable bathythermograph casts for both swaths. 2-D processing reveals strong laterally continuous reflections throughout the upper ~800 m as well as a few weaker but still distinct reflections as deep as ~1100 m. We interpret the reflections to be caused by reversible fine structure from internal wave strains. Two bright reflections are traced across the 225-m-wide swath to produce reflection surface images that illustrate the 3-D nature of ocean fine structure. We show that the orientation of linear features in a reflection can be obtained by calculating the orientations of contours of reflection relief, or more robustly, by fitting a sinusoidal surface to the reflection. Preliminary 3-D processing further illustrates the potential of 3-D seismic data in interpreting images of oceanic features such as internal wave strains. This work demonstrates the viability of imaging oceanic fine structure in 3-D and shows that, beyond simply providing a way visualize oceanic fine structure, quantitative information such as the spatial orientation of features like fronts and solitons can be obtained from 3-D seismic images. We expect complete, optimized 3-D processing to improve both the signal to noise ratio and spatial resolution of our images resulting in increased options for analysis and interpretation.

Deep denoising autoencoder for seismic random noise attenuation

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. V367-V376 ◽  
Author(s):  
Omar M. Saad ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Synthetic Data ◽  
Seismic Signal ◽  
Denoising Autoencoder ◽  
Levels Of Abstraction ◽  
Data Set ◽  
Effective Manner

Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data; following this, the pretrained model is used to denoise the field data set in an unsupervised scheme using a new customized loss function. We have assessed the proposed algorithm based on four synthetic data sets and two field examples, and we compare the results with several benchmark algorithms, such as f- x deconvolution ( f- x deconv) and the f- x singular spectrum analysis ( f- x SSA). As a result, our algorithm succeeds in attenuating the random noise in an effective manner.

scholarly journals ADAPTIVE SIGNAL DECOMPOSITION AND FILTERING USING BINOMIAL FILTERS APPLIED TO THE GROUND-ROLL ATTENUATION

2016 ◽  
Vol 34 (3) ◽  
Author(s):  
Cristian D. Ariza A. ◽  
Milton J. Porsani
Keyword(s):  
Seismic Data ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Noise Signal ◽  
Signal Decomposition ◽  
Signal To Noise ◽  
Ground Roll ◽  
Noise Ratio ◽  
Adaptive Signal

ABSTRACT. The ground-roll is a type of noise normally present in land seismic data. It strongly harms the signal-to-noise ratio, and interferes in several stages of the seismic data processing, strongly affecting the final quality of the obtained seismic images...Keywords: seismic noise, signal-to-noise ratio, adaptive filters, Burg algorithm, seismic signal decomposition.  RESUMO. O ground-roll é um tipo de ruído normalmente presente nos dados sísmicos terrestres. Ele prejudica muito a razão sinal-ruído e interfere em vários est´ágios do processamento de dados sísmicos, afetando fortemente a qualidade final das imagens sísmicas obtidas...Palavras-chave: ruídos sísmicos, relação sinal-ruído, filtragem adaptativo, algoritmo de Burg, decomposição do sinal sísmico.

scholarly journals First images and orientation of internal waves from a 3-D seismic oceanography data set

2009 ◽  
Vol 6 (3) ◽  
pp. 2341-2356 ◽  
Author(s):  
T. M. Blacic ◽  
W. S. Holbrook
Keyword(s):  
Internal Wave ◽  
Internal Waves ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Quantitative Information ◽  
Data Set ◽  
Seismic Oceanography ◽  
Reflector Surface ◽  
Seismic Images ◽  
Wide Swath

Abstract. We present 3-D images of ocean finestructure from a unique industry-collected 3-D multichannel seismic dataset from the Gulf of Mexico that includes expendable bathythermograpgh casts for both swaths. 2-D processing reveals strong laterally continuous reflectors throughout the upper ~800 m as well as a few weaker but still distinct reflectors as deep as ~1100 m. Two bright reflections are traced across the 225-m-wide swath to produce reflector surface images that show the 3-D structure of internal waves. We show that the orientation of internal wave crests can be obtained by calculating the orientations of contours of reflector relief. Preliminary 3-D processing further illustrates the potential of 3-D seismic data in interpreting images of oceanic features such as internal wave strains. This work demonstrates the viability of imaging oceanic finestructure in 3-D and shows that, beyond simply providing a way to see what oceanic finestructure looks like, quantitative information such as the spatial orientation of features like internal waves and solitons can be obtained from 3-D seismic images. We expect complete, optimized 3-D processing to improve both the signal to noise ratio and spatial resolution of our images resulting in increased options for analysis and interpretation.

scholarly journals Study on the Velocity Analysis Method of Low SNR Seismic Data

2021 ◽  
Vol 11 (1) ◽  
pp. 78
Author(s):  
Jianbo He ◽  
Zhenyu Wang ◽  
Mingdong Zhang
Keyword(s):  
Seismic Data ◽  
Fresnel Zone ◽  
Signal To Noise Ratio ◽  
Normal Wave ◽  
Curvature Radius ◽  
Velocity Spectrum ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Zero Offset

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

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