scholarly journals Tutorial: Spectral bandwidth extension — Invention versus harmonic extrapolation

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. W1-W16 ◽  
Author(s):  
Chen Liang ◽  
John Castagna ◽  
Ricardo Zavala Torres
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Low Frequency ◽  
Original Data ◽  
Spectral Bandwidth ◽  
Bandwidth Extension ◽  
Seismic Resolution ◽  
Earth Structures ◽  
Seismic Sections ◽  
Phase Acceleration

Various postprocessing methods can be applied to seismic data to extend the spectral bandwidth and potentially increase the seismic resolution. Frequency invention techniques, including phase acceleration and loop reconvolution, produce spectrally broadened seismic sections but arbitrarily create high frequencies without a physical basis. Tests in extending the bandwidth of low-frequency synthetics using these methods indicate that the invented frequencies do not tie high-frequency synthetics generated from the same reflectivity series. Furthermore, synthetic wedge models indicate that the invented high-frequency seismic traces do not improve thin-layer resolution. Frequency invention outputs may serve as useful attributes, but they should not be used for quantitative work and do not improve actual resolution. On the other hand, under appropriate circumstances, layer frequency responses can be extrapolated to frequencies outside the band of the original data using spectral periodicities determined from within the original seismic bandwidth. This can be accomplished by harmonic extrapolation. For blocky earth structures, synthetic tests show that such spectral extrapolation can readily double the bandwidth, even in the presence of noise. Wedge models illustrate the resulting resolution improvement. Synthetic tests suggest that the more complicated the earth structure, the less valid the bandwidth extension that harmonic extrapolation can achieve. Tests of the frequency invention methods and harmonic extrapolation on field seismic data demonstrate that (1) the frequency invention methods modify the original seismic band such that the original data cannot be recovered by simple band-pass filtering, whereas harmonic extrapolation can be filtered back to the original band with good fidelity and (2) harmonic extrapolation exhibits acceptable ties between real and synthetic seismic data outside the original seismic band, whereas frequency invention methods have unfavorable well ties in the cases studied.

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

Broadband seismic over/under sources and their designature-deghosting

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. P61-P73 ◽  
Author(s):  
Lasse Amundsen ◽  
Ørjan Pedersen ◽  
Are Osen ◽  
Johan O. A. Robertsson ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Low Frequency ◽  
Point Sources ◽  
High Frequency Side ◽  
Frequency Content ◽  
Source Depth ◽  
Deep Sources ◽  
The Cost ◽  
High Frequency Content

The source depth influences the frequency band of seismic data. Due to the source ghost effect, it is advantageous to deploy sources deep to enhance the low-frequency content of seismic data. But, for a given source volume, the bubble period decreases with the source depth, thereby degrading the low-frequency content. At the same time, deep sources reduce the seismic bandwidth. Deploying sources at shallower depths has the opposite effects. A shallow source provides improved high-frequency content at the cost of degraded low-frequency content due to the ghosting effect, whereas the bubble period increases with a lesser source depth, thereby slightly improving the low-frequency content. A solution to the challenge of extending the bandwidth on the low- and high-frequency side is to deploy over/under sources, in which sources are towed at two depths. We have developed a mathematical ghost model for over/under point sources fired in sequential and simultaneous modes, and we have found an inverse model, which on common receiver gathers can jointly perform designature and deghosting of the over/under source measurements. We relate the model for simultaneous mode shooting to recent work on general multidepth level array sources, with previous known solutions. Two numerical examples related to over/under sequential shooting develop the main principles and the viability of the method.

Seismic low-frequency shadows and their application to detect CO2 anomalies on time-lapse seismic data: a case study from Sleipner field, North sea

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Emmanuel Anthony ◽  
Nimisha Vedanti
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
High Frequency ◽  
Seismic Waves ◽  
Decomposition Analysis ◽  
Low Frequency ◽  
Time Lapse ◽  
Underlying Mechanism ◽  
Real Field ◽  
Saturated Medium

The detection and underlying mechanism of prospect-scale seismic low-frequency shadows (LFS) has been an issue of debate. Even though the concept of LFS is widely accepted, the practical applicability of the method remains limited due to few real field case studies and little understanding of the underlying attenuation mechanism. To characterize the attenuation phenomenon responsible for the occurrence of LFS in CO2 saturated formations, we use the diffusivity and viscosity of the fluid saturated medium to derive a complex velocity function that characterizes a high-frequency attenuation phenomenon responsible for the occurrence of LFS in a CO2 saturated formation. Synthetic seismic data sets representing pre- and post- CO2 injection scenarios were generated using 2D diffusive viscous equations to model the LFS and understand its occurrence mechanism. Furthermore, to demonstrate the applicability of LFS in a real field, a spectral decomposition analysis of time-lapse 3D seismic data of the Sleipner field, North Sea, was carried out using the continuous wavelet transform. LFSs were clearly detected below the reservoir base at frequencies lower than 30 Hz in the post- CO2 injection surveys. It is shown that the seismic low-frequency shadows are not artefacts but occur due to attenuation of the high frequency components of the propagating seismic waves in the CO2 saturated Utsira Formation. The attenuation of these frequencies is a result of the diffusivity and viscosity of the fluid saturated medium. The low-frequency shadows are localized anomalies at the base of the formation; hence with the present approach, these anomalies cannot be related to the migration of the CO2 plume in the Utsira Formation.

Blind Inversion of Multidimensional Seismic Data Using Sequential Tikhonov and Total Variation Regularizations (STTVR)

Geophysics ◽  
2021 ◽  
pp. 1-56
Author(s):  
Saber jahanjooy ◽  
Mohammad Ali Riahi ◽  
Hamed Ghanbarnejad Moghanloo
Keyword(s):  
Total Variation ◽  
Tikhonov Regularization ◽  
Seismic Data ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Earth Model ◽  
Rapid Changes ◽  
Nonlinear Relation ◽  
Ill Posed ◽  
Seismic Sections

The acoustic impedance (AI) model is key data for seismic interpretation, usually obtained from its nonlinear relation with seismic reflectivity. Common approaches use initial geological and seismic information to constraint the AI model estimation. When no accurate prior information is available, these approaches may dictate false results at some parts of the model. The regularization of ill-posed underdetermined problems requires some constraints to restrict the possible results. Available seismic inversion methods mostly use Tikhonov or total variation (TV) regularizations with some adjustments. Tikhonov regularization assumes smooth variation in the AI model, and it is incurious about the rapid changes in the model. TV allows rapid changes, and it is more stable in presence of noisy data. In a detailed realistic earth model that AI changes gradually, TV creates a stair-casing effect, which could lead to misinterpretation. This could be avoided by using TV and Tikhonov regularization sequentially in the alternating direction method of multipliers (ADMM) and creating the AI model. The result of implementing the proposed algorithm (STTVR) on 2D synthetic and real seismic sections shows that the smaller details in the lithological variations are accounted for as well as the general trend. STTVR can calculate major AI variations without any additional low-frequency constraints. The temporal and spatial transition of the calculated AI in real seismic data is gradual and close to a real geological setting.

The influence of the planted geophone on seismic land data

Geophysics ◽  
1980 ◽  
Vol 45 (8) ◽  
pp. 1239-1253 ◽  
Author(s):  
G. M. Hoover ◽  
J. T. O’Brien
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Mineral Exploration ◽  
Low Frequency ◽  
Soil Consolidation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Strong Damping ◽  
Local Soil ◽  
Oscillatory Coupling

Characteristics of the seismic data acquisition system that previously have been ignored become important as more sophisticated interpretive methods based on broader frequency bandwidths are developed to extract stratigraphic information from land data in hydrocarbon and mineral exploration. Theoretical and experimental results indicate that the geophone plant can be approximated by a damped oscillatory coupling, properties dependent upon the geophone mass, dimension of earth contact, and local soil consolidation. A massive geophone with minimal earth contact exhibits a low‐frequency plant resonance with weak damping and acts as a low‐pass filter to eliminate the high‐frequency components of a recorded signal. A lightweight geophone with large earth contact exhibits a high‐frequency plant resonance with strong damping and, consequently, filtering effects are minimal if the plant resonance is well above the signal bandwidth. Although signal filtering influences are weak for strong damping, phase delays can introduce errors of several milliseconds which resemble static errors. Additional complications arise since these time shifts are frequency dependent and, consequently, not identical for all reflection events in a seismic trace. The resonant frequency of the geophone plant increases with increased soil consolidation; however, damping demonstrates only a weak dependence upon consolidation. All of these factors can limit the effectiveness of common‐depth‐point (CDP) stacking methods if the proper technique is not practiced in the acquisition of broad‐bandwidth seismic data.

5D seismic data regularization by a damped least-norm Fourier inversion

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB103-WB111 ◽  
Author(s):  
Side Jin
Keyword(s):  
Seismic Data ◽  
Fourier Coefficients ◽  
Fourier Transforms ◽  
Low Frequency ◽  
Original Data ◽  
Spatial Spectrum ◽  
Small Data ◽  
Inversion Algorithm ◽  
Fourier Inversion ◽  
Discrete Fourier Transforms

Regularizing inadequate and irregularly sampled seismic data is one of important problems in seismic data processing. An improvement to existing methods to solve this problem is proposed by applying a 5D regularization/interpolation scheme with a damped least-norm Fourier inversion. Under the assumption of planar seismic events within small data windows, the spatial spectrum of regularized data for a fixed frequency should be sparse and have minimum damped norm. The inversion scheme consists in finding a set of regularly spaced spatial Fourier coefficients by minimizing its damped norm for each frequency, subject to the condition that the resulting spatial Fourier coefficients also faithfully reconstruct the original data. The damping factors are automatically derived from the amplitude spectra of the regularized low-frequency data. With the guidance of the damping factors and automatic adjustment of wavenumber ranges according to the Nyquist sampling theory, the proposed inversion algorithm naturally yields a one-step solution for both stabilization and antialiasing of the interpolation problem. A distinctive feature of the method is that it uses high-dimensional nonuniform fast Fourier transforms to evaluate expensive discrete Fourier transforms involved in conjugate gradient iterations. This improves the computational efficiency. The results of applying this algorithm to synthetic and field data demonstrate that it performs well when applied to highly irregular data and outperforms lower dimensional interpolation schemes.

Decoupling of seismic reflectors and stratigraphic timelines: A modeling study of Tertiary strata from Svalbard

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. SM273-SM280 ◽  
Author(s):  
Ståle Johansen ◽  
Espen Granberg ◽  
Donatella Mellere ◽  
Børge Arntsen ◽  
Torben Olsen
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Ground Control ◽  
Seismic Section ◽  
Sequence Stratigraphic ◽  
Fundamental Assumption ◽  
Geologic Model ◽  
Seismic Sections ◽  
High Level ◽  
Seismic Reflectors

In sequence stratigraphic interpretations, the key premise is that stratal surfaces effectively represent geologic timelines. When applied to seismic sections, the fundamental assumption is that primary reflections generally mimic stratigraphic timelines. The main objective of this study was to test how well key reflectors in a seismic section couple to timelines. To achieve the high level of ground control needed for such testing, we combined photogrammetry and traditional sedimentologic fieldwork to optimize the geologic model. We relied further on petrophysical analysis to derive a numerical model suitable for the simulation of seismic data. In spite of laterally discontinuous vertical-impedance contrasts (VICs), false seismic continuity was created, and we observed frequent decoupling of seismic reflectors and stratigraphic timelines. These observations demonstrate how the low-frequency seismic method fails to image normal complexity in a stratigraphic unit. A seismic correlation test showed that the interpreters made numerous mistakes and that such mistakes are very difficult to avoid. The failure of a fundamental assumption, as illustrated here, creates serious problems for the sequence stratigraphic concept when applied to detailed correlation analysis on seismic sections.

Coherence attribute applications on seismic data in various guises — Part 2

2018 ◽  
Vol 6 (3) ◽  
pp. T531-T541 ◽  
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Bandwidth Extension ◽  
Second Derivatives ◽  
Spectral Components ◽  
Alternative Means ◽  
Seismic Amplitudes ◽  
Reflectivity Inversion ◽  
Derivatives Of ◽  
High Frequency Content

We have previously discussed some alternative means of modifying the frequency spectrum of the input seismic data to modify the resulting coherence image. The simplest method was to increase the high-frequency content by computing the first and second derivatives of the original seismic amplitudes. We also evaluated more sophisticated techniques, including the application of structure-oriented filtering to different spectral components before spectral balancing, thin-bed reflectivity inversion, bandwidth extension, and the amplitude volume technique. We further examine the value of coherence computed from individual spectral voice components, and alternative means of combining three or more such coherence images, providing a single volume for interpretation.

Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. A37-A43
Author(s):  
Jinwei Fang ◽  
Hui Zhou ◽  
Yunyue Elita Li ◽  
Qingchen Zhang ◽  
Lingqian Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
High Frequency ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Data Driven ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Recovery Method ◽  
Full Waveform

The lack of low-frequency signals in seismic data makes the full-waveform inversion (FWI) procedure easily fall into local minima leading to unreliable results. To reconstruct the missing low-frequency signals more accurately and effectively, we have developed a data-driven low-frequency recovery method based on deep learning from high-frequency signals. In our method, we develop the idea of using a basic data patch of seismic data to build a local data-driven mapping in low-frequency recovery. Energy balancing and data patches are used to prepare high- and low-frequency data for training a convolutional neural network (CNN) to establish the relationship between the high- and low-frequency data pairs. The trained CNN then can be used to predict low-frequency data from high-frequency data. Our CNN was trained on the Marmousi model and tested on the overthrust model, as well as field data. The synthetic experimental results reveal that the predicted low-frequency data match the true low-frequency data very well in the time and frequency domains, and the field results show the successfully extended low-frequency spectra. Furthermore, two FWI tests using the predicted data demonstrate that our approach can reliably recover the low-frequency data.

Interpretation of depositional facies from seismic data

Geophysics ◽  
1979 ◽  
Vol 44 (2) ◽  
pp. 131-160 ◽  
Author(s):  
J. B. Sangree ◽  
J. M. Widmier
Keyword(s):  
Seismic Data ◽  
Depositional Environments ◽  
Depositional Facies ◽  
Interval Velocity ◽  
Seismic Resolution ◽  
Depositional Processes ◽  
Basin Floor ◽  
Seismic Sections ◽  
Seismic Reflections ◽  
Seismic Lines

Depositional environments can be predicted from seismic data through an orderly approach to the interpretation of seismic reflections. One keystone to this approach is an understanding of the effects of lithology and bed spacing on reflection parameters. Amplitude, frequency, and continuity are some of the parameters most useful for interpreting environments. Reflection amplitude contains information on the velocity and density contrasts at individual interfaces and on the extent of interbedding. Frequency is primarily a characteristic of the nature of the seismic pulse, but it is also related to such geologic factors as the spacing of reflectors or lateral changes in interval velocity. Continuity of reflections is closely associated with continuity of bedding (e.g., continuous reflections suggest widespread, layered deposits). A second keystone to this interpretive approach is the parallelism of reflection cycles to gross bedding and, therefore, to physical surfaces that separate older from younger sediments. Exceptions to this concept include (1) fluid contact reflections, (2) limitations imposed by seismic resolution, and (3) various non‐geologic coherent events. In spite of these exceptions, this concept provides a powerful tool for the analysis of reflection patterns. Reflection cycle patterns include the configuration of reflections (i.e., layered, chaotic, and reflection‐free) and the nature of cycle terminations at the depositional unit boundaries. The external form of the depositional unit can be analyzed from a grid of seismic lines and is valuable in interpreting the depositional processes responsible for the unit. Sheet, sheet drape, wedge, lens, fan, and other forms are described. The areal associations of these forms are often critical to environmental interpretation. Examples of facies interpretation from seismic sections are shown for depositional environments ranging from shelf to basin floor.

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