Revisiting holistic migration

2021 ◽  
Vol 40 (10) ◽  
pp. 768-777
Author(s):  
Vemund S. Thorkildsen ◽  
Leiv-J. Gelius ◽  
Enders A. Robinson
Keyword(s):  
Field Data ◽  
Barents Sea ◽  
Sampling Strategy ◽  
Processing Technique ◽  
Sampling Theorem ◽  
Virtual Object ◽  
Coherent Noise ◽  
Signal Processing Technique ◽  
Data Set ◽  
Image Domain

If an optical hologram is broken into pieces, a virtual object can still be reconstructed from each of the fragments. This reconstruction is possible because each diffraction point emits waves that reach every point of the hologram. Thus, the entire object is encoded into each subset of the hologram. Analogous to the broken hologram, the use of undersampled seismic data violating the Nyquist-Shannon sampling theorem may still give a well-resolved image of the subsurface. A theoretical framework of this idea has already been introduced in the literature and denoted as holistic migration. However, the general lack of seismic field data demonstrations has inspired the study presented here. Since the optical hologram is diffraction-driven, we propose to employ diffraction-separated data and not conventional reflection data as input for holistic migration. We follow the original idea and regularly undersample the data spatially. Such a sampling strategy will result in coherent noise in the image domain. We therefore introduce a novel signal processing technique to remove such noise. The feasibility of the proposed approach is demonstrated employing the Sigsbee2a controlled data set and field data from the Barents Sea.

scholarly journals Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Thomas André Larsen Greiner ◽  
Jan Erik Lie ◽  
Odd Kolbjørnsen ◽  
Andreas Kjelsrud Evensen ◽  
Espen Harris Nilsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Variation ◽  
Field Data ◽  
Barents Sea ◽  
Broad Band ◽  
Total Variation Regularization ◽  
Data Set ◽  
L2 Norm ◽  
Unsupervised Deep Learning ◽  
Seismic Wavefield

In 3D marine seismic acquisition, the seismic wavefield is not sampled uniformly in the spatial directions. This leads to a seismic wavefield consisting of irregularly and sparsely populated traces with large gaps between consecutive sail-lines especially in the near-offsets. The problem of reconstructing the complete seismic wavefield from a subsampled and incomplete wavefield, is formulated as an underdetermined inverse problem. We investigate unsupervised deep learning based on a convolutional neural network (CNN) for multidimensional wavefield reconstruction of irregularly populated traces defined on a regular grid. The proposed network is based on an encoder-decoder architecture with an overcomplete latent representation, including appropriate regularization penalties to stabilize the solution. We proposed a combination of penalties, which consists of the L2-norm penalty on the network parameters, and a first- and second-order total-variation (TV) penalty on the model. We demonstrate the performance of the proposed method on broad-band synthetic data, and field data represented by constant-offset gathers from a source-over-cable data set from the Barents Sea. In the field data example we compare the results to a full production flow from a contractor company, which is based on a 5D Fourier interpolation approach. In this example, our approach displays improved reconstruction of the wavefield with less noise in the sparse near-offsets compared to the industry approach, which leads to improved structural definition of the near offsets in the migrated sections.

scholarly journals Image domain least-squares migration with a Hessian matrix estimated by non-stationary matching filters

2019 ◽  
Vol 17 (1) ◽  
pp. 148-159 ◽  
Author(s):  
Song Guo ◽  
Huazhong Wang
Keyword(s):  
Least Squares ◽  
Numerical Experiments ◽  
Field Data ◽  
Hessian Matrix ◽  
Computational Cost ◽  
Image Deblurring ◽  
Data Set ◽  
Image Domain ◽  
Slow Convergence ◽  
Least Squares Migration

Abstract Assuming that an accurate background velocity is obtained, least-squares migration (LSM) can be used to estimate underground reflectivity. LSM can be implemented in either the data domain or image domain. The data domain LSM (DDLSM) is not very practical because of its huge computational cost and slow convergence rate. The image domain LSM (IDLSM) might be a flexible alternative if estimating the Hessian matrix using a cheap and accurate approach. It has practical potential to analyse convenient Hessian approximation methods because the Hessian matrix is too huge to compute and save. In this paper, the Hessian matrix is approximated with non-stationary matching filters. The filters are calculated to match the conventional migration image to the demigration/remigration image. The two images are linked by the Hessian matrix. An image deblurring problem is solved with the estimated filters for the IDLSM result. The combined sparse and total variation regularisations are used to produce accurate and reasonable inversion results. The numerical experiments based on part of Sigsbee model, Marmousi model and a 2D field data set illustrate that the non-stationary matching filters can give a good approximation for the Hessian matrix, and the results of the image deblurring problem with combined regularisations can provide high-resolution and true-amplitude reflectivity estimations.

Subsalt imaging by target-oriented inversion-based imaging:A 3D field-data example

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. S47-S58 ◽  
Author(s):  
Yaxun Tang ◽  
Biondo Biondi
Keyword(s):  
Field Data ◽  
Imaging System ◽  
Normal Operator ◽  
Data Set ◽  
Image Domain ◽  
Depth Migration ◽  
Regularized Inversion ◽  
Computationally Intensive ◽  
Subsalt Imaging ◽  
Cost Issue

Reflectivity images obtained by prestack depth migration are often distorted by uneven subsurface illumination, especially in areas with complex geology, such as subsalt regions. We address the problem of uneven illumination in subsalt imaging by posing the reflectivity-imaging problem as a linear inverse problem and solving it in the image domain in a target-oriented fashion. The most computationally intensive part of the image-domain inversion is the explicit computation of the so-called Hessian operator. The Hessian is defined to be the normal operator of the associated modeling/imaging operator, which is a direct measure of the illumination deficiency of the imaging system. We can overcome the cost issue by using the phase-encoding technique in the 3D conical-wave domain for marine streamer acquisitions. We apply the inversion-based imaging methodology to a 3D field data set acquired from the Gulf of Mexico, and we precondition the inversion with nonstationary dip filters, which naturally incorporate interpreted geologic information. Numerical examples demonstrate that imaging by regularized inversion successfully recovers the reflectivities from the effects of uneven illumination, yielding images with more balanced amplitudes and higher spatial resolution.

ENHANCING WELLBORE LEAK LOCALIZATION WITH CONTINUOUS LOGGING DATA FROM A SONIC SENSOR ARRAY

2021 ◽  
Author(s):  
Yao Ge ◽  
◽  
Yadong Wang ◽  
Xiang Wu ◽  
Ruijia Wang ◽  
...  
Keyword(s):  
Field Data ◽  
Array Signal Processing ◽  
Noise Spectrum ◽  
Processing Technique ◽  
Guided Wave ◽  
Acquisition Time ◽  
Flow Profile ◽  
Road Noise ◽  
Signal Processing Technique ◽  
Leak Location

Early detection and localization of downhole leaks are essential to maintain well integrity, reduce cost, and minimize downtime. New technology has been developed to detect leak locations in a well quickly and to characterize the flow profile of the leak by using an array of hydrophones. The technology uses advanced modeling and beamforming algorithm to map out the flow pattern in a 2D image within the well’s completion structure. However, during continuous logging, the leak signal may be contaminated by guided wave noises such as the road noise from the tool string, and the logging results will be compromised. This paper demonstrates a method to estimate and remove guided-wave noise to enhance the leak detection answer products. The data from continuous logging may be contaminated with significant road noise due to equipment contacting the casing or borehole which produces Stoneley or tube waves. For single and dual hydrophone tools, additional runs may be needed to stop these tools at selected locations to record data without this contamination, but this approach prolongs the acquisition time and limits the vertical resolution. In order to obtain depth-continuous and high-resolution leak information, an advanced array signal-processing technique has been developed to enhance the signal quality. Extensive studies on field data were conducted to extract the features and characteristics of the leak noise, even when those features overlap in time or frequency with contamination noise. The processing method employs multiple steps that analyze the hydrophone array to remove the contamination noise in the time or frequency domain, leaving the leak noise for flow and leak location analysis. The proposed method has successfully identified high noise activity at certain depths as road noise in continuous logging data. Road noise may increase in amplitude within a limited depth due to a momentary change in logging activity. The elevated noise generated can be identified as guided-wave noise instead of a potential leak. The method can be implemented in realtime and the results will save additional rig time conducting further stationary logging at the non-leak depths. Field data results also suggest that the proposed method improves the signal-to-noise ratio of the continuous logging data significantly and delivers quality noise spectrum and leak location logs for the industry. The proposed method has been proven to be effective in identifying and enhancing leak signals and removing contaminating signals due to guided wave noises. It has greatly enhanced the quality of the detection, resolution, and location of leaks in wellbore tubulars produced from continuous logging data. These high-quality continuous logging results will help field engineers to make more accurate decisions quickly during logging operations and could avoid costly and time-lengthy stationary logging programs.

scholarly journals Convolutional sparse coding for noise attenuation in seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. V23-V30
Author(s):  
Zhaolun Liu ◽  
Kai Lu
Keyword(s):  
Sparse Coding ◽  
Seismic Data ◽  
Field Data ◽  
Synthetic Data ◽  
Basis Functions ◽  
High Signal ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Denoising Method ◽  
Data Set

We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training with the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in complex field data. By excluding filters with coherent noise features, our method can further attenuate coherent noise and separate ground roll.

scholarly journals A field data example of Marchenko multiple elimination

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. S65-S70 ◽  
Author(s):  
Lele Zhang ◽  
Evert Slob
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Field Data ◽  
Multiple Reflections ◽  
Coherent Noise ◽  
Data Set ◽  
Model Independent ◽  
Multiple Elimination

Internal multiple reflections have been widely considered as coherent noise in measured seismic data, and many approaches have been developed for their attenuation. The Marchenko multiple elimination (MME) scheme eliminates internal multiple reflections without model information or adaptive subtraction. This scheme was originally derived from coupled Marchenko equations, but it was modified to make it model independent. It filters primary reflections with their two-way traveltimes and physical amplitudes from measured seismic data. The MME scheme is applied to a deepwater field data set from the Norwegian North Sea to evaluate its success in removing internal multiple reflections. The result indicates that most internal multiple reflections are successfully removed and primary reflections masked by overlapping internal multiple reflections are recovered.

scholarly journals A method to identify aperiodic disturbances in the ionosphere

2014 ◽  
Vol 32 (5) ◽  
pp. 563-569 ◽  
Author(s):  
J.-S. Wang ◽  
Z. Chen ◽  
C.-M. Huang
Keyword(s):  
Statistical Estimation ◽  
Critical Frequency ◽  
Random Noise ◽  
Synthetic Data ◽  
Processing Technique ◽  
Data Series ◽  
Signal Processing Technique ◽  
Data Set ◽  
The Fourier Transform ◽  
Periodic Components

Abstract. In this paper, variations in the ionospheric F2 layer's critical frequency are decomposed into their periodic and aperiodic components. The latter include disturbances caused both by geophysical impacts on the ionosphere and random noise. The spectral whitening method (SWM), a signal-processing technique used in statistical estimation and/or detection, was used to identify aperiodic components in the ionosphere. The whitening algorithm adopted herein is used to divide the Fourier transform of the observed data series by a real envelope function. As a result, periodic components are suppressed and aperiodic components emerge as the dominant contributors. Application to a synthetic data set based on significant simulated periodic features of ionospheric observations containing artificial (and, hence, controllable) disturbances was used to validate the SWM for identification of aperiodic components. Although the random noise was somewhat enhanced by post-processing, the artificial disturbances could still be clearly identified. The SWM was then applied to real ionospheric observations. It was found to be more sensitive than the often-used monthly median method to identify geomagnetic effects. In addition, disturbances detected by the SWM were characterized by a Gaussian-type probability density function over all timescales, which further simplifies statistical analysis and suggests that the disturbances thus identified can be compared regardless of timescale.

scholarly journals A Remote Sensor System Based on TDLAS Technique for Ammonia Leakage Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2448
Author(s):  
Hongbin Lu ◽  
Chuantao Zheng ◽  
Lei Zhang ◽  
Zhiwei Liu ◽  
Fang Song ◽  
...  
Keyword(s):  
Signal Processing ◽  
Limit Of Detection ◽  
Processing Technique ◽  
Tunable Diode Laser ◽  
Sensor System ◽  
Signal Processing Technique ◽  
Suppression Effect ◽  
Output Wavelength ◽  
Deviation Analysis ◽  
Remote Sensor

The development of an efficient, portable, real-time, and high-precision ammonia (NH3) remote sensor system is of great significance for environmental protection and citizens’ health. We developed a NH3 remote sensor system based on tunable diode laser absorption spectroscopy (TDLAS) technique to measure the NH3 leakage. In order to eliminate the interference of water vapor on NH3 detection, the wavelength-locked wavelength modulation spectroscopy technique was adopted to stabilize the output wavelength of the laser at 6612.7 cm−1, which significantly increased the sampling frequency of the sensor system. To solve the problem in that the light intensity received by the detector keeps changing, the 2f/1f signal processing technique was adopted. The practical application results proved that the 2f/1f signal processing technique had a satisfactory suppression effect on the signal fluctuation caused by distance changing. Using Allan deviation analysis, we determined the stability and limit of detection (LoD). The system could reach a LoD of 16.6 ppm·m at an average time of 2.8 s, and a LoD of 0.5 ppm·m at an optimum averaging time of 778.4 s. Finally, the measurement result of simulated ammonia leakage verified that the ammonia remote sensor system could meet the need for ammonia leakage detection in the industrial production process.

scholarly journals Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3725
Author(s):  
Paweł Zimroz ◽  
Paweł Trybała ◽  
Adam Wróblewski ◽  
Mateusz Góralczyk ◽  
Jarosław Szrek ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Processing Technique ◽  
Underground Mine ◽  
Search And Rescue ◽  
Reference Spectrum ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Communication Device ◽  
Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

Sign in / Sign up

Export Citation Format

Share Document