Calculating Multiple Scattering from a Time-Varying, Undulating Sea Surface Using a Full-Wavefield Multistage Algorithm

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Xiangyu Meng ◽  
Fuxing Han ◽  
Jianguo Sun ◽  
Mingchen Liu ◽  
Zeshuang Xu ◽  
...  
Keyword(s):  
Processing Technique ◽  
Sea Surface ◽  
Time Varying ◽  
Parabolic Approximation ◽  
Seismic Responses ◽  
Irregular Boundary ◽  
Numerical Tests ◽  
Marine Seismic ◽  
Changes Over Time ◽  
Incident Waves

The sea surface interface between ocean and air is time varying and can be spatially rough as a result of wind, tides and currents; the shape of this interface changes over time considering the influence of wind, tides, etc. As a result, waves impinging on the sea surface are continuously scattered. In the case of marine seismic, the multiple scattered waves propagate downward into the underwater formation and result in complex seismic responses. To understand the structure of the responses, we propose a multistage algorithm for computing the scattered waves at the sea surface. Specifically, we first extrapolate the upgoing incident waves stepwise using the thin-slab approximation from the scattering theory based on the De Wolf approximation of the Lippmann–Schwinger equation. Then, we implement the air-water boundary condition at the sea surface. Finally, we use the irregular boundary processing technique to compute the time-varying undulating sea-surface scattered waves from different scattering stages. To overcome the angular limitation of the original parabolic approximation, we introduce a multi-directional parabolic approximation based on computational electromagnetics. Numerical tests show that the multistage algorithm presented here can accurately calculate the sea surface scattered waves and should be useful in investigating the structure of marine seismic responses.

Modeling scattering effects from time-varying sea surface based on acoustic reciprocity

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. T49-T68 ◽  
Author(s):  
Elsa Cecconello ◽  
Endrias G. Asgedom ◽  
Okwudili C. Orji ◽  
Morten W. Pedersen ◽  
Walter Söllner
Keyword(s):  
Weather Conditions ◽  
Sea Surface ◽  
Time Varying ◽  
Flat Mirror ◽  
Sea Surface Effects ◽  
Marine Seismic ◽  
Rough Sea Surface ◽  
Frequency Domain Approach ◽  
The Impact ◽  
Rough Sea

In marine seismic processing, the sea surface is often considered a flat mirror; hence, the effects of different weather conditions during the acquisition are largely ignored. However, studies have shown that rough sea-surface ghosts can severely damage the 4D signal, if not handled properly in data processing. To account for realistic sea-surface effects in processing, the impact of time-varying rough sea surfaces needs to be studied. We derive a method for modeling source and receiver ghosts from the time-varying rough sea surface and their interaction with subsurface reflections. This method is based on acoustic reciprocity and leads to integral equations of nonstationary wavefields. These modeling equations can also serve as a basis for investigating source and receiver deghosting methods for time-varying rough sea surfaces. Our developed modeling algorithm is validated against a frequency-domain approach for a “frozen” rough sea surface. For a moving simple sea surface, the Doppler shift produced by our method is in very good agreement with the analytical solution. Using a Pierson-Moskowitz spectrum, we derive a time-varying rough sea surface and model the receiver ghost, the source ghost, and the source-receiver ghost for the subsurface primary reflections of a heterogeneous geologic model. The results highlight that the source and receiver ghost interactions with a time-varying sea surface differently affect the subsurface reflections, and these effects can significantly impact the seismic repeatability of 4D studies.

Removal of all time-varying sea-surface effects by source deghosting and demultiple

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. T347-T362 ◽  
Author(s):  
Elsa Cecconello ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Seismic Data ◽  
Surface Condition ◽  
Seismic Source ◽  
Sea Surface ◽  
Normal Velocity ◽  
Time Varying ◽  
Data Set ◽  
Marine Seismic ◽  
Rough Sea Surface ◽  
The Impact

Seismic source deghosting and sea-surface-related demultiple have been long-standing problems in marine seismic data processing. Although the receiver ghost problem may be considered as solved by using collocated measurement of pressure and normal velocity wavefields, the source deghosting and demultiple algorithms are still limited by assumptions related to the sea-surface condition. We have investigated the impact of a time-varying rough sea surface on source deghosting and demultiple. Starting from Rayleigh’s reciprocity theorem for time-varying sea surfaces, we uncover a fundamental limitation for source deghosting of time-dependent wavefields, such as marine seismic data that contain a receiver ghost or sea-surface-related multiples. We use simple synthetic examples to study the impact of source deghosting on sea-surface-related multiples. To resolve this limitation, we derive a method for simultaneous source deghosting and sea-surface-related demultiple for time-variant wavefields. Finally, we use the complex geologic model Sigsbee 2B first to illustrate that the source deghosting operation brings significant errors when applied to a data set containing sea-surface multiples. Second, we find that this problem can be resolved by simultaneously performing source deghosting and demultiple operations even in the presence of time-varying sea surfaces.

Effects of time-varying sea surface in marine seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. P33-P43 ◽  
Author(s):  
Okwudili C. Orji ◽  
Walter Söllner ◽  
Leiv-J. Gelius
Keyword(s):  
Imaging Technique ◽  
Sea Surface ◽  
Time Varying ◽  
Surface Imaging ◽  
The North ◽  
Dual Sensor ◽  
The North Sea ◽  
Marine Seismic ◽  
Seismic Measurements ◽  
Rough Sea

A method of imaging sea surfaces based on marine seismic measurements has recently been developed. The imaging technique is based on extrapolating decomposed wavefields obtained from dual-sensor streamers to the sea surface where an adequate imaging condition is applied. Earlier feasibility tests of the method involved only controlled data associated with frozen sea surfaces. Here, the issue of time-varying effects will be in focus. We introduced a modeling approach based on the Kirchhoff-Helmholtz integral and computed the scattered wavefield from time-varying rough sea surfaces (e.g., Pierson-Moskowitz sea surfaces). We generated data for a realistic wind speed and verify the robustness of the proposed sea surface imaging technique by taking into account possible effects of moving receivers as well as streamers with variable shape. We investigate the feasibility of estimating the surface wave velocity from the spectra of the imaged sea surfaces and finally present a successful application of the sea surface imaging technique to data from the North Sea.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

scholarly journals Doppler spectrum of microwave SAR signals from two-dimensional time-varying sea surface

2016 ◽  
Vol 30 (10) ◽  
pp. 1265-1276 ◽  
Author(s):  
Yunhua Wang ◽  
Yanmin Zhang ◽  
Huimin Li ◽  
Ge Chen
Keyword(s):  
Sea Surface ◽  
Doppler Spectrum ◽  
Time Varying ◽  
Two Dimensional

Estimation of Sea Surface Temperature (SST) Using Marine Seismic Data

2015 ◽  
Vol 173 (4) ◽  
pp. 1305-1316 ◽  
Author(s):  
Satish Kumar Sinha ◽  
Pawan Dewangan ◽  
Kalachand Sain
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seismic Data ◽  
Sea Surface ◽  
Marine Seismic

scholarly journals Body weight at 10 years of age and change in body composition between 8 and 10 years of age were related to survival in a longitudinal study of 39 Labrador retriever dogs

2019 ◽  
Vol 61 (1) ◽  
Author(s):  
Johanna Christina Penell ◽  
David Mark Morgan ◽  
Penny Watson ◽  
Stuart Carmichael ◽  
Vicki Jean Adams
Keyword(s):  
Body Composition ◽  
Body Weight ◽  
End Of Life ◽  
Fat Mass ◽  
Lean Mass ◽  
Coat Colour ◽  
Overweight And Obesity ◽  
Time Varying ◽  
Cohort Study Design ◽  
Changes Over Time

Abstract Background Overweight and obesity have been adversely associated with longevity in dogs but there is scarce knowledge on the relation between body composition and lifespan. We aimed to investigate the effects of body composition, and within-dog changes over time, on survival in adult Labradors using a prospective cohort study design. The dogs had a median age of 6.5 years at study start and were kept in similar housing and management conditions throughout. The effects of the various predictors, including the effect of individual monthly-recorded change in body weight as a time varying covariate, were evaluated using survival analysis. Results All dogs were followed to end-of-life; median age at end-of-life was 14.0 years. Body composition was measured annually with dual-energy x-ray absorptiometer (DEXA) scans between 6.2 and 17.0  years. All 39 dogs had DEXA recorded at 8, 9 and 10 years of age. During the study the mean (± SD) percent of fat (PF) and lean mass (PL) was 32.8 (± 5.6) and 64.2 (± 5.5) %, respectively, with a mean lean:fat ratio (LFR) of 2.1 (± 0.6); body weight (BW) varied from 17.5 to 44.0 kg with a mean BW change of 9.9 kg (± 3.0). There was increased hazard of dying for every kg increase in BW at 10 years of age; for each additional kg of BW at 10 years, dogs had a 19% higher hazard (HR = 1.19, P = 0.004). For the change in both lean mass (LM) and LFR variables, it was protective to have a higher lean and/or lower fat mass (FM) at 10 years of age compared to 8 years of age, although the HR for change in LM was very close to 1.0. For age at study start, older dogs had an increased hazard. There was no observed effect for the potential confounders sex, coat colour and height at shoulders, or of the time-varying covariate. Conclusions These results suggest that even rather late-life control efforts on body weight and the relationship between lean and fat mass may influence survival in dogs. Such “windows of opportunity” can be used to develop healthcare strategies that would help promote an increased healthspan in dogs.

Time-Varying Delay Estimation Applied to the Surface Electromyography Signals Using the Parametric Approach

2018 ◽  
Vol 17 (02) ◽  
pp. 1850015 ◽  
Author(s):  
Gia Thien Luu ◽  
Abdelbassit Boualem ◽  
Tran Trung Duy ◽  
Philippe Ravier ◽  
Olivier Butteli
Keyword(s):  
Parametric Method ◽  
Optimization Technique ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Fiber Direction ◽  
Time Varying ◽  
Time Varying Delay ◽  
Power Spectral ◽  
Changes Over Time ◽  
Varying Delay

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over time, which induces time-varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In this paper, the problem of TVD estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a deterministic optimization technique (Newton) and stochastic optimization technique, called simulated annealing (SA). The performance of the two techniques is also compared. We also derive two appropriate Cramer–Rao Lower Bounds (CRLB) for the estimated TVD model parameters and for the TVD waveforms. Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the derived CRBs. A comparison with non-parametric approaches of the TVD estimation is also presented and shows the superiority of the method proposed.

Least-squares data-to-data migration: An approach for migrating free-surface-related multiples

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. S83-S94 ◽  
Author(s):  
Yikang Zheng ◽  
Yibo Wang ◽  
Xu Chang
Keyword(s):  
Free Surface ◽  
Least Squares ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Data Migration ◽  
Subsurface Imaging ◽  
Full Data ◽  
Recording Time ◽  
Numerical Tests ◽  
Marine Seismic

Free-surface-related multiples can provide extra illumination of the subsurface and thus can be usefully included in migration procedures. However, most multiple migration approaches require separation of primaries and free-surface-related multiples or at least prediction of multiples in advance, which is time consuming and prone to errors. The data-to-data migration (DDM) method migrates free-surface-related multiples by forward and backward propagating the recorded full data (containing primaries and free-surface-related multiples). For DDM, there is no need to predict or separate multiples, but the migration results suffer from the crosstalk generated by crosscorrelations of undesired seismic events, e.g., primaries and second-order free-surface-related multiples. We have developed least-squares DDM (LSDDM) for marine data to eliminate the crosstalk generated by DDM. In each iteration, the forward-propagated primaries and free-surface-related multiples are crosscorrelated with the backward-propagated primary and free-surface-related multiple residuals to form the reflectivity gradient. We use a three-layer model and the Marmousi model for numerical tests. The results validate that LSDDM can provide a migrated image with higher signal-to-noise ratio and more balanced amplitudes than DDM. The LSDDM approach might be valuable for general subsurface imaging for marine seismic data when the migration velocity is accurate, and the acquired data have sufficient recording time.

Sign in / Sign up

Export Citation Format

Share Document