WATER REVERBERATIONS—THEIR NATURE AND ELIMINATION

Geophysics ◽  
1959 ◽  
Vol 24 (2) ◽  
pp. 233-261 ◽  
Author(s):  
Milo M. Backus
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Water Layer ◽  
Original Data ◽  
Present Form ◽  
Linear Filtering ◽  
Inverse Filtering ◽  
Multiple Reflections ◽  
Filtering Techniques

In offshore shooting the validity of previously recorded seismic data has been severely limited by multiple reflections within the water layer. The magnitude of this problem is dependent on the thickness and the nature of the boundaries of the water layer. The effect of the water layer is treated as a linear filtering mechanism, and it is suggested that most apparent water reverberation records probably contain some approximate subsurface structural information, even in their present form. The use of inverse filtering techniques for the removal or attenuation of the water reverberation effect is discussed. Examples show the application of the technique to conventional magnetically recorded offshore data. It has been found that the effectiveness of the method is strongly dependent on the instrumental parameters used in the recording of the original data.

Deep‐water peg legs and multiples: Emulation and suppression

Geophysics ◽  
1986 ◽  
Vol 51 (12) ◽  
pp. 2177-2184 ◽  
Author(s):  
J. R. Berryhill ◽  
Y. C. Kim
Keyword(s):  
Wave Equation ◽  
Seismic Data ◽  
Water Layer ◽  
Original Data ◽  
Equation Method ◽  
Multiple Reflections ◽  
Step Method ◽  
Arrival Times ◽  
Sea Floor ◽  
Water Bottom

This paper discusses a two‐step method for predicting and attenuating multiple and peg‐leg reflections in unstacked seismic data. In the first step, an (observed) seismic record is extrapolated through a round‐trip traversal of the water layer, thus creating an accurate prediction of all possible multiples. In the second step, the record containing the predicted multiples is compared with and subtracted from the original. The wave‐equation method employed to predict the multiples takes accurate account of sea‐floor topography and so requires a precise water‐bottom profile as part of the input. Information about the subsurface below the sea floor is not required. The arrival times of multiple reflections are reproduced precisely, although the amplitudes are not accurate, and the sea floor is treated as a perfect reflector. The comparison step detects the similarities between the computed multiples and the original data, and estimates a transfer function to equalize the amplitudes and account for any change in waveform caused by the sea‐floor reflector. This two‐step wave‐equation method is effective even for dipping sea floors and dipping subsurface reflectors. It does not depend upon any assumed periodicity in the data or upon any difference in stacking velocity between primaries and multiples. Thus it is complementary to the less specialized methods of multiple suppression.

SOME CHARACTERISTICS OF MARINE SPARKER SEISMIC DATA

Geophysics ◽  
1972 ◽  
Vol 37 (3) ◽  
pp. 462-470 ◽  
Author(s):  
F. T. Allen
Keyword(s):  
Seismic Data ◽  
Water Layer ◽  
Seismic Survey ◽  
Multiple Reflections ◽  
Time Profiles ◽  
Survey Technique ◽  
Recording Conditions ◽  
Single Detector ◽  
Marine Seismic ◽  
The Relationship

The marine seismic survey technique of frequent recordings with a single detector group can provide intricate details valuable to relatively shallow investigations. Velocities may be computed from time anomalies, under certain circumstances. The extent of multiple energy response is an indication that the 100,000-joule source is strong enough for the purpose. Recognizable minor details in primary reflections are important clues in identifying related multiple reflections. Bounces off the underside of the water layer are rarely found. The pattern and character of reflections are influenced by recording conditions; thus, the relationship between recorded events and sedimentary beds is not simple. Seismic time profiles frequently give wrong impressions of structural attitudes because of the horizontal‐to‐vertical exaggeration, time anomalies, and multiple reflections, as well as the usual effects of velocity differences. The interpreted cross‐section gives a reasonably correct (even if velocities are assumed) impression of structure; the profile often does not.

Ray‐tracing‐based prediction and subtraction of water‐layer multiples

Geophysics ◽  
1990 ◽  
Vol 55 (4) ◽  
pp. 443-451 ◽  
Author(s):  
Andrew J. Calvert
Keyword(s):  
Ray Tracing ◽  
Seismic Data ◽  
Computational Cost ◽  
Water Layer ◽  
Computational Effort ◽  
Lateral Variation ◽  
Multiple Reflections ◽  
Seafloor Topography ◽  
Arrival Times ◽  
Data Matching

Many methods of multiple suppression break down when the structure that produces the reverberation possesses significant lateral variation; a common example of this situation occurs in marine data with the multiple reflections that are generated by seafloor topography. Such multiples may be suppressed by techniques based upon wave‐equation extrapolation; the recorded seismic data are mathematically propagated through a simulated water layer to generate a set of multiple arrivals which may, after data matching, be subtracted. However, the computational effort required to propagate prestack data to a laterally varying datum is very large. In this paper, a method of suppressing selected multiples with arbitrary moveout is presented. In order to reduce the computational cost, prediction of the multiple arrival times is performed by ray tracing through a model of the laterally varying water layer and, possibly, the subsurface. An estimate of the multiple waveform on each trace is obtained by stacking a window of data about the calculated arrival times. The multiple arrival can then be attenuated by subtracting this wavelet from each trace in the prestack gather from which the estimate is derived. In practice, calculations of the variation in multiple amplitude and of any errors in the moveout correction require the multiple reflections to be of comparable, or higher, amplitude than contemporary primary events, a situation that is often the case where multiple contamination is a problem.

scholarly journals Seismic data interpolation based on U-net with texture loss

Geophysics ◽  
2020 ◽  
pp. 1-107
Author(s):  
Wenqian Fang ◽  
Lihua Fu ◽  
Meng Zhang ◽  
Zhiming Li
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Deep Neural Network ◽  
Structural Information ◽  
Original Data ◽  
Reconstruction Error ◽  
Low Rank ◽  
Sampled Data ◽  
Data Interpolation ◽  
The Difference

Seismic data interpolation is an effective way of recovering missing traces and obtains enough information for subsequent processing. Unlike traditional methods, deep neural network (DNN)-based methods do not need to make assumptions, as they can self-learn the relationship between sampled data and complete data using large training datasets and complete the interpolation with a small computational burden. However, current DNN-based approaches only focus on reducing the difference between the recovered and original data during training, which helps to improve the quality of reconstructed seismic data as a whole, while ignoring the characteristics of the local structure. We propose a novel Seismic U-net InterpolaTor (SUIT) algorithm based on the framework of the U-net deep neural network in combination with a texture loss, rather than only optimizing for reconstruction loss. Texture loss is proposed to ensure the accuracy of local structural information, which is calculated by a pre-trained texture extraction neural network. Furthermore, we use a trade-off parameter to balance the reconstruction error and texture loss, and a practical technique for selecting the associated weighting parameter. The feasibility of the provide method is assessed via synthetic and field data examples. Numerical tests show that SUIT is robust in noisy environments, and the trained network can reconstruct irregularly or regularly sampled seismic data. Our proposed algorithm performed better than DNN-based approaches that only use reconstruction loss and the traditional low-rank matrix fitting method.

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

Capturing, Preserving, and Digitizing Legacy Seismic Data from the Montserrat Volcano Observatory Analog Seismic Network, July 1995–December 2004

2020 ◽  
Vol 91 (4) ◽  
pp. 2127-2140 ◽  
Author(s):  
Glenn Thompson ◽  
John A. Power ◽  
Jochen Braunmiller ◽  
Andrew B. Lockhart ◽  
Lloyd Lynch ◽  
...  
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Original Data ◽  
South Florida ◽  
Seismic Network ◽  
Quality Data ◽  
Spectral Amplitude ◽  
Assistance Program ◽  
Waveform Data ◽  
Amplitude Measurement

Abstract An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four-station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real-time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ∼10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.

LONG-PERIOD SURFACE-RELATED MULTIPLE SUPPRESSION IN 2D MARINE SEISMIC DATA USING PREDICTIVE DECONVOLUTION AND COMBINATION OF SURFACE-RELATED MULTIPLE ELIMINATION AND PARABOLIC RADON FILTERING

2021 ◽  
Author(s):  
Pimpawee Sittipan ◽  
Pisanu Wongpornchai
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Petroleum Reservoirs ◽  
The United States ◽  
Multiple Reflections ◽  
Long Period ◽  
Predictive Deconvolution ◽  
Short Period ◽  
Marine Seismic ◽  
Multiple Elimination

Some of the important petroleum reservoirs accumulate beneath the seas and oceans. Marine seismic reflection method is the most efficient method and is widely used in the petroleum industry to map and interpret the potential of petroleum reservoirs. Multiple reflections are a particular problem in marine seismic reflection investigation, as they often obscure the target reflectors in seismic profiles. Multiple reflections can be categorized by considering the shallowest interface on which the bounces take place into two types: internal multiples and surface-related multiples. Besides, the multiples can be categorized on the interfaces where the bounces take place, a difference between long-period and short-period multiples can be considered. The long-period surface-related multiples on 2D marine seismic data of the East Coast of the United States-Southern Atlantic Margin were focused on this research. The seismic profile demonstrates the effectiveness of the results from predictive deconvolution and the combination of surface-related multiple eliminations (SRME) and parabolic Radon filtering. First, predictive deconvolution applied on conventional processing is the method of multiple suppression. The other, SRME is a model-based and data-driven surface-related multiple elimination method which does not need any assumptions. And the last, parabolic Radon filtering is a moveout-based method for residual multiple reflections based on velocity discrimination between primary and multiple reflections, thus velocity model and normal-moveout correction are required for this method. The predictive deconvolution is ineffective for long-period surface-related multiple removals. However, the combination of SRME and parabolic Radon filtering can attenuate almost long-period surface-related multiple reflections and provide a high-quality seismic images of marine seismic data.

A dictionary learning method with atom splitting for seismic footprint suppression

Geophysics ◽  
2021 ◽  
pp. 1-97
Author(s):  
Dawei Liu ◽  
Lei Gao ◽  
Xiaokai Wang ◽  
wenchao Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Original Data ◽  
Morphological Features ◽  
Time Slice ◽  
Learning Method ◽  
Seismic Attribute ◽  
Morphological Component Analysis ◽  
Value Decomposition

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises two components, i.e., useful signals and acquisition footprint. Useful signals describe the spatial distributions of geological structures with local piecewise smooth morphological features. However, acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in the marine seismic acquisition appear as stripes. As useful signals and acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two sub-dictionaries to reconstruct these two components. We propose an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D post-stack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D post-stack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that the proposed method can effectively suppress the acquisition footprint with fidelity to the original data.

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