scholarly journals Wiener estimation of the Green’s function

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. W31-W44 ◽  
Author(s):  
Anton Ziolkowski
Keyword(s):  
Green’S Function ◽  
Impulse Response ◽  
Green's Function ◽  
Seismic Data ◽  
Wiener Filter ◽  
Electromagnetic Data ◽  
Transient Electromagnetic ◽  
Marine Seismic ◽  
Measured Response

I consider the problem of finding the impulse response, or Green’s function, from a measured response including noise, given an estimate of the source time function. This process is usually known as signature deconvolution. Classical signature deconvolution provides no measure of the quality of the result and does not separate signal from noise. Recovery of the earth impulse response is here formulated as the calculation of a Wiener filter in which the estimated source signature is the input and the measured response is the desired output. Convolution of this filter with the estimated source signature is the part of the measured response that is correlated with the estimated signature. Subtraction of the correlated part from the measured response yields the estimated noise, or the uncorrelated part. The fraction of energy not contained in this uncorrelated component is defined as the quality of the filter. If the estimated source signature contains errors, the estimated earth impulse response is incomplete, and the estimated noise contains signal, recognizable as trace-to-trace correlation. The method can be applied to many types of geophysical data, including earthquake seismic data, exploration seismic data, and controlled source electromagnetic data; it is illustrated here with examples of marine seismic and marine transient electromagnetic data.

Measurement of interstation phase and group velocities and Q using Wiener filtering

1982 ◽  
Vol 72 (1) ◽  
pp. 73-91
Author(s):  
Steven R. Taylor ◽  
M. Nafi Toksöz
Keyword(s):  
Surface Wave ◽  
Green’S Function ◽  
Green's Function ◽  
Amplitude Spectrum ◽  
Wiener Filter ◽  
Wave Path ◽  
Phase And Group Velocities ◽  
Filtering Technique ◽  
Surface Wave Data ◽  
Group Velocities

abstract A method for calculating interstation phase and group velocities and attenuation coefficients using a Wiener (least-squares) filtering technique is presented. The interstation Green's (or transfer) function is estimated from surface wave data from two stations laying along the same great circle path. The estimate is obtained from a Wiener filter which is constructed to estimate the signal recorded at the station further from the source from the signal recorded at the nearer station. The interstation group velocity is obtained by applying the multiple-filtering technique to the Green's function, and the interstation phase velocity from the phase spectrum of the Green's function. The amplitude spectrum of the Green's function is used to calculate average attenuation between the two stations. Using synthetic seismograms contaminated by noise, it is shown that the Q values calculated from the Green's function are significantly more stable and accurate than those obtained by taking spectral ratios. The method is particularly useful for paths involving short station separations and is applied to a surface wave path crossing the Iranian Plateau.

On estimating the impulse response between receivers in a controlled ultrasonic experiment

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. SI79-SI84 ◽  
Author(s):  
K. van Wijk
Keyword(s):  
Data Processing ◽  
Laboratory Experiment ◽  
Green’S Function ◽  
Detailed Analysis ◽  
Elastic Medium ◽  
Impulse Response ◽  
Green's Function ◽  
Geophysical Data ◽  
Seismic Interferometry ◽  
Band Limited

A controlled ultrasonic laboratory experiment provides a detailed analysis of retrieving a band-limited estimate of the Green's function between receivers in an elastic medium. Instead of producing a formal derivation, this paper appeals to a series of intuitive operations, common to geophysical data processing, to understand the practicality of seismic interferometry. Whereas the retrieval of the full Green's function is based on the crosscorrelation of receivers in the presence of equipartitioned signal, an estimate of the impulse response is recovered successfully with 40 sources in a line covering six wavelengths at the surface.

New objective functions for waveform inversion

Geophysics ◽  
1998 ◽  
Vol 63 (1) ◽  
pp. 213-222 ◽  
Author(s):  
L. Neil Frazer ◽  
Xinhua Sun
Keyword(s):  
Green’S Function ◽  
Objective Function ◽  
Impulse Response ◽  
Green's Function ◽  
Waveform Inversion ◽  
Data Sets ◽  
Minimum Phase ◽  
Objective Functions ◽  
Data Set ◽  
Parameter Values

Inversion is an organized search for parameter values that maximize or minimize an objective function, referred to here as a processor. This note derives three new seismic processors that require neither prior deconvolution nor knowledge of the source‐receiver wavelet. The most powerful of these is the fourwise processor, as it is applicable to data sets from multiple shots and receivers even when each shot has a different unknown signature and each receiver has a different unknown impulse response. Somewhat less powerful than the fourwise processor is the pairwise processor, which is applicable to a data set consisting of two or more traces with the same unknown wavelet but possibly different gains. When only one seismogram exists the partition processor can be used. The partition processor is also applicable when there is only one shot (receiver) and each receiver (shot) has a different signature. In fourwise and pairwise inversions the unknown wavelets may be arbitrarily long in time and need not be minimum phase. In partition inversion the wavelet is assumed to be shorter in time than the data trace itself but is not otherwise restricted. None of the methods requires assumptions about the Green’s function.

PC‐based acquisition and processing of high‐resolution marine seismic data

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1804-1812 ◽  
Author(s):  
Ho‐Young Lee ◽  
Byung‐Koo Hyun ◽  
Young‐Sae Kong
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Spectral Characteristics ◽  
Processing System ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Optical Disk Drive ◽  
Air Gun ◽  
Marine Seismic

We have improved the quality of high‐resolution marine seismic data using a simple PC‐based acquisition and processing system. The system consists of a PC, an A/D converter, and a magneto‐optical disk drive. The system has been designed to acquire single‐channel data at up to 60,000 samples per second and to perform data processing of seismic data by a simple procedure. Test surveys have been carried out off Pohang, southern East Sea of Korea. The seismic systems used for the test were an air gun and a 3.5 kHz sub‐bottom profiling system. Spectral characteristics of the sources were analyzed. Simple digital signal processes which include gain recovery, deconvolution, band‐pass filter, and swell filter were performed. The quality of seismic sections produced by the system is greatly enhanced in comparison to analog sections. The PC‐based system for acquisition and processing of high‐resolution marine seismic data is economical and versatile.

Migration using sea surface-related multiples: Challenges and opportunities

Geophysics ◽  
2021 ◽  
pp. 1-42
Author(s):  
Shaoping Lu
Keyword(s):  
Data Acquisition ◽  
Least Squares ◽  
Seismic Data ◽  
Sea Surface ◽  
Seismic Exploration ◽  
Algorithm Development ◽  
Challenges And Opportunities ◽  
Seismic Data Acquisition ◽  
Marine Seismic

In marine seismic exploration, it has been well known that sea surface-related multiples can be treated as signals to image the subsurface and provide extended illumination. Previous studies on imaging of multiples have been mainly focusing on its algorithm development and implementation. This paper serves as a tutorial where we systematically investigate the fundamental challenges in the process of imaging of multiples. We first examine the impacts of marine seismic data acquisition parameters: such as offset, trace spacing and streamer towing direction, which are all key elements that control the quality of the images of multiples, and illustrate that 3D towed streamer and OBS surveys are preferable acquisition geometries to apply imaging of multiples. In addition, we investigate the challenges in jointly imaging primaries and multiples and the crosstalk problem in the process, and demonstrate that a Least-Squares inversion based algorithm is effective to address these issues. With the proper handling of all those challenges, imaging of multiples can help to mitigate shallow acquisition footprints, improve salt boundary illumination and enhance the imaging resolution, which allow the identification of drilling hazards and reduction in drilling risks. To apply imaging of multiples in practice, the objective is not to replace but to augment imaging of primaries by providing extra illumination.

Airborne electromagnetic bathymetry and estimation of bedrock topography in Broken Bay, Australia

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB3-WB17 ◽  
Author(s):  
Julian Vrbancich
Keyword(s):  
Water Depth ◽  
Seismic Data ◽  
Sea Water ◽  
Unconsolidated Sediments ◽  
Sediment Thickness ◽  
Bedrock Topography ◽  
Transient Electromagnetic ◽  
Airborne Electromagnetic ◽  
Marine Seismic ◽  
Water Depths

A helicopter transient electromagnetic (TEM) survey was flown over shallow coastal waters in Broken Bay, Australia, overlying several palaeovalleys and exposed reef sections. The infilled palaeovalleys contain unconsolidated sediments with variable thicknesses exceeding 100 m. Previous marine seismic reflection and vibrocore studies provide an estimate of sediment thickness and surficial sediment conductivity, respectively, which, combined with known bathymetry (sonar soundings) and measured seawater conductivity, can be used to generate a crude 1D geoelectric ground-truth model consisting of two layers (seawater/sediment) overlying a relatively resistive basement. The primary focus was to examine the accuracy of interpreted water depths obtained by 1D inversion of airborne TEM data, assuming a two-layer over resistive basement model, by comparing these depths with known water depths. The secondary focus was to compare the interpreted sediment thickness (i.e., the thickness of the second geoelectric layer, which combined with bathymetry, gives the bedrock depth) with thicknesses estimated from marine seismic data to test the potential of using airborne electromagnetic systems for remote sensing of the coarse features of the bedrock topography. Interpreted water depths obtained from TEM data resulted in absolute water depth accuracies of 1–2 m for depths between 10 and 30 m, and 0.3–0.5 m in water shallower than [Formula: see text]. More importantly, similar water depth accuracies were obtained using raw TEM data (with birdswing removal) and TEM data obtained by postprocessing using time-consuming empirical corrections based on the TEM half-space response over deep sea water. The interpreted sediment depths derived from TEM and marine seismic data showed good agreement, generally, for example, inversion of TEM data delineated a distinct palaeovalley that transects a beach, with a maximum depth of 60–70 m below the seafloor, in agreement with depths estimated from marine seismic data to within [Formula: see text].

Attenuation of seismic swell noise using convolutional neural networks in frequency domain and transfer learning

2020 ◽  
Vol 8 (4) ◽  
pp. T941-T952
Author(s):  
Jiachun You ◽  
Yajuan Xue ◽  
Junxing Cao ◽  
Canping Li
Keyword(s):  
Frequency Domain ◽  
Transfer Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Fine Tuning ◽  
Network Parameter ◽  
Marine Seismic ◽  
The Time Domain

Because swell noises are very common in marine seismic data, it is extremely important to attenuate them to improve the signal-to-noise ratio (S/N). Compared to process noises in the time domain, we have built a frequency-domain convolutional neural network (CNN) based on the short-time Fourier transform to address swell noises. In the numerical experiments, we quantitatively evaluate the denoising performances of the time- and frequency-domain CNNs, compare the impacts of network structures on attenuating swell noises, and study how network parameter choices impact the quality of the denoised signal based on peak S/N, structural similarity, and root-mean-square-error indices. These results help us to build an optimal CNN model. Furthermore, to illustrate the superiority of our proposed method, we compare the conventional and proposed CNN methods. To address the generalization capability of CNN, we adopt transfer learning by using fine tuning to adjust the weights of the pretrained model with a small amount of target data. The application of transfer learning improves the quality of the denoised images, which further proves that our proposed method with transfer learning has the potential to be deployed in actual seismic data acquisition.

Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data

2020 ◽  
Vol 223 (3) ◽  
pp. 1888-1898
Author(s):  
Kirill Gadylshin ◽  
Ilya Silvestrov ◽  
Andrey Bakulin
Keyword(s):  
Artificial Intelligence ◽  
Seismic Data ◽  
Cost Effective ◽  
Computational Effort ◽  
Full Density ◽  
Ocean Bottom ◽  
Data Set ◽  
Marine Seismic ◽  
Missing Attributes

SUMMARY We propose an advanced version of non-linear beamforming assisted by artificial intelligence (NLBF-AI) that includes additional steps of encoding and interpolating of wavefront attributes using inpainting with deep neural network (DNN). Inpainting can efficiently and accurately fill the holes in waveform attributes caused by acquisition geometry gaps and data quality issues. Inpainting with DNN delivers excellent quality of interpolation with the negligible computational effort and performs particularly well for a challenging case of irregular holes where other interpolation methods struggle. Since conventional brute-force attribute estimation is very costly, we can further intentionally create additional holes or masks to restrict expensive conventional estimation to a smaller subvolume and obtain missing attributes with cost-effective inpainting. Using a marine seismic data set with ocean bottom nodes, we show that inpainting can reliably recover wavefront attributes even with masked areas reaching 50–75 per cent. We validate the quality of the results by comparing attributes and enhanced data from NLBF-AI and conventional NLBF using full-density data without decimation.

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