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 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.

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.

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.

Suppression of high‐energy noise using an alternative stacking procedure

Geophysics ◽  
1989 ◽  
Vol 54 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Jakob B. U. Haldorsen ◽  
Paul A. Farmer
Keyword(s):  
Seismic Data ◽  
Traditional Approach ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
High Amplitude ◽  
High Energy ◽  
Data Set ◽  
Trimmed Mean ◽  
Fold Reduction ◽  
Processing Techniques

Occasionally, seismic data contain transient noise that can range from being a nuisance to becoming intolerable when several seismic vessels try simultaneously to collect data in an area. The traditional approach to solving this problem has been to allocate time slots to the different acquisition crews; the procedure, although effective, is very expensive. In this paper a statistical method called “trimmed mean stack” is evaluated as a tool for reducing the detrimental effects of noise from interfering seismic crews. Synthetic data, as well as field data, are used to illustrate the efficacy of the technique. Although a conventional stack gives a marginally better signal‐to‐noise ratio (S/N) for data without interference noise, typical usage of the trimmed mean stack gives a reduced S/N equivalent to a fold reduction of about 1 or 2 percent. On the other hand, for a data set containing high‐energy transient noise, trimming produces stacked sections without visible high‐amplitude contaminating energy. Equivalent sections produced with conventional processing techniques would be totally unacceptable. The application of a trimming procedure could mean a significant reduction in the costs of data acquisition by allowing several seismic crews to work simultaneously.

2.5D multifocusing imaging of crooked-line seismic surveys

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Hossein Jodeiri Akbari Fam ◽  
Mostafa Naghizadeh ◽  
Oz Yilmaz
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Line Geometry ◽  
Data Set ◽  
Seismic Surveys ◽  
Seismic Image ◽  
New Formulation ◽  
Optimization Search

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.

An application of the Marchenko internal multiple elimination scheme formulated as a least-squares problem

Geophysics ◽  
2021 ◽  
pp. 1-70
Author(s):  
Rodrigo S. Santos ◽  
Daniel E. Revelo ◽  
Reynam C. Pestana ◽  
Victor Koehne ◽  
Diego F. Barrera ◽  
...  
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Neumann Series ◽  
Convergence Criterion ◽  
Coherent Noise ◽  
Data Set ◽  
Least Squares Problem ◽  
Time Migration ◽  
Multiple Elimination ◽  
Seismic Images

Seismic images produced by migration of seismic data related to complex geologies, suchas pre-salt environments, are often contaminated by artifacts due to the presence of multipleinternal reflections. These reflections are created when the seismic wave is reflected morethan once in a source-receiver path and can be interpreted as the main coherent noise inseismic data. Several schemes have been developed to predict and subtract internal multiplereflections from measured data, such as the Marchenko multiple elimination (MME) scheme,which eliminates the referred events without requiring a subsurface model or an adaptivesubtraction approach. The MME scheme is data-driven, can remove or attenuate mostof these internal multiples, and was originally based on the Neumann series solution ofMarchenko’s projected equations. However, the Neumann series approximate solution isconditioned to a convergence criterion. In this work, we propose to formulate the MMEas a least-squares problem (LSMME) in such a way that it can provide an alternative thatavoids a convergence condition as required in the Neumann series approach. To demonstratethe LSMME scheme performance, we apply it to 2D numerical examples and compare theresults with those obtained by the conventional MME scheme. Additionally, we evaluatethe successful application of our method through the generation of in-depth seismic images,by applying the reverse-time migration (RTM) algorithm on the original data set and tothose obtained through MME and LSMME schemes. From the RTM results, we show thatthe application of both schemes on seismic data allows the construction of seismic imageswithout artifacts related to internal multiple events.

scholarly journals A convolutional neural network approach to deblending seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA13-WA26 ◽  
Author(s):  
Jing Sun ◽  
Sigmund Slang ◽  
Thomas Elboth ◽  
Thomas Larsen Greiner ◽  
Steven McDonald ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Common Source ◽  
Neural Network Approach ◽  
Data Set ◽  
The Common ◽  
Training Examples ◽  
Blended Data

For economic and efficiency reasons, blended acquisition of seismic data is becoming increasingly commonplace. Seismic deblending methods are computationally demanding and normally consist of multiple processing steps. Furthermore, the process of selecting parameters is not always trivial. Machine-learning-based processing has the potential to significantly reduce processing time and to change the way seismic deblending is carried out. We have developed a data-driven deep-learning-based method for fast and efficient seismic deblending. The blended data are sorted from the common-source to the common-channel domain to transform the character of the blending noise from coherent events to incoherent contributions. A convolutional neural network is designed according to the special characteristics of seismic data and performs deblending with results comparable to those obtained with conventional industry deblending algorithms. To ensure authenticity, the blending was performed numerically and only field seismic data were used, including more than 20,000 training examples. After training and validating the network, seismic deblending can be performed in near real time. Experiments also indicate that the initial signal-to-noise ratio is the major factor controlling the quality of the final deblended result. The network is also demonstrated to be robust and adaptive by using the trained model to first deblend a new data set from a different geologic area with a slightly different delay time setting and second to deblend shots with blending noise in the top part of the record.

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.

Ground-roll attenuation using generative adversarial networks

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA255-WA267 ◽  
Author(s):  
Yijun Yuan ◽  
Xu Si ◽  
Yue Zheng
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Coherent Noise ◽  
Traditional Methods ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Persistent Problem ◽  
Ground Roll

Ground roll is a persistent problem in land seismic data. This type of coherent noise often contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data. A variety of methods for addressing ground-roll attenuation have been developed. However, existing methods are limited, especially when using real land seismic data. For example, when ground roll and reflections overlap in the time or frequency domains, traditional methods cannot completely separate them and they often distort the signals during the suppression process. We have developed a generative adversarial network (GAN) to attenuate ground roll in seismic data. Unlike traditional methods for ground-roll attenuation dependent on various filters, the GAN method is based on a large training data set that includes pairs of data with and without ground roll. After training the neural network with the training data, the network can identify and filter out any noise in the data. To fulfill this purpose, the proposed method uses a generator and a discriminator. Through network training, the generator learns to create the data that can fool the discriminator, and the discriminator can then distinguish between the data produced by the generator and the training data. As a result of the competition between generators and discriminators, generators produce better images whereas discriminators accurately recognize targets. Tests on synthetic and real land seismic data show that the proposed method effectively reveals reflections masked by the ground roll and obtains better results in the attenuation of ground roll and in the preservation of signals compared to the three other methods.

Pitfall experiences when interpreting complex structure with low-quality seismic images

2015 ◽  
Vol 3 (1) ◽  
pp. SB29-SB37 ◽  
Author(s):  
Bob A. Hardage
Keyword(s):  
Seismic Data ◽  
Structural Model ◽  
Signal To Noise Ratio ◽  
Complex Structure ◽  
Lessons Learned ◽  
Structural Deformation ◽  
Structural Interpretation ◽  
Seismic Image ◽  
Data Volume ◽  
Seismic Images

Structural interpretation of seismic data presents numerous opportunities for encountering interpretational pitfalls, particularly when a seismic image does not have an appropriate signal-to-noise ratio (S/N), or when a subsurface structure is unexpectedly complex. When both conditions exist — low S/N data and severe structural deformation — interpretation pitfalls are almost guaranteed. We analyzed an interpretation done 20 years ago that had to deal with poor seismic data quality and extreme distortion of strata. The lessons learned still apply today. Two things helped the interpretation team develop a viable structural model of the prospect. First, existing industry-accepted formation tops assigned to regional wells were rejected and new log interpretations were done to detect evidence of repeated sections and overturned strata. Second, the frequency content of the 3D seismic data volume was restricted to only the first octave of its seismic spectrum to create better evidence of fault geometries. A logical and workable structural interpretation resulted when these two action steps were taken. To the knowledge of our interpretation team, neither of these approaches had been attempted in the area at the time of this work (early 1990s). We found two pitfalls that may be encountered by other interpreters. The first pitfall was the hazard of accepting long-standing, industry-accepted definitions of the positions of formation tops on well logs. This nonquestioning acceptance of certain log signatures as indications of targeted formation tops led to a serious misinterpretation in our study. The second pitfall was the prevailing passion by geophysicists to create seismic data volumes that have the widest possible frequency spectrum. This interpretation effort showed that the opposite strategy was better at this site and for our data conditions; i.e., it was better to filter seismic images so that they contained only the lowest octave of frequencies in the seismic spectrum.

Sign in / Sign up

Export Citation Format

Share Document