scholarly journals ATTENUATION OF MULTIPLE REFLECTIONS ASSOCIATED WITH DIABASE SILLS FROM SOLIMÕES BASIN THROUGH THE PARABOLIC RADON TRANSFORM AND MULTICHANNEL PREDICTIVE DECONVOLUTION

2019 ◽  
Vol 37 (2) ◽  
Author(s):  
Misael Possidonio de Souza ◽  
Michelangelo Gomes da Silva ◽  
Milton J. Porsani
Keyword(s):  
Radon Transform ◽  
Oil And Gas ◽  
Synthetic Data ◽  
Low Frequency ◽  
Real Data ◽  
Multiple Reflections ◽  
Predictive Deconvolution ◽  
Fortran 90 ◽  
Parabolic Radon Transform

ABSTRACT. The Solimões Basin Brazil will still be the subject of many discussions in the future due to the success of oil exploration in the 1970s with the discovery of oil and gas fields. The geology of this basin is characterized by significant thick igneous rocks layers, the diabase sills, which can be seen in any stacked section as reflectors with strong amplitude but low frequency. The high contrast of seismic impedance between the sedimentary rock layers and the diabase sills generate multiple reflection and reverberations that can lead to wrong seismic interpretation of stacked sections. In this work, to improve the quality of the stacked sections, we propose a seismic process flow that includes multiple filtering steps in land data, throughout the Multichannel Predictive Deconvolution and the Parabolic Radon Transform. This study was first performed on synthetic data to test the methodology, and then in real data provided by Agência Nacional de Petróleo, Gás Natural e Biocombustíveis (ANP). The conventional processing flowchart was applied using commercial processing software such as SeisSpace/ProMAX, and Fortran 90 codes available in the Centro de Pesquisa em Geofísica e Geologia, Universidade Federal da Bahia (CPGG/UFBA). The results obtained were satisfactory with the methodology used, besides visible improvements in the quality of the stacked seismic sections after attenuation of unwanted noises. Keywords: multiple attenuation, seismic processing, seismic reflection.RESUMO. A Bacia do Solimões será ainda tema de muitas discussões no futuro, devido ao sucesso da exploração de petróleo nas décadas de 1970 com a descoberta de campos de oléo e gás. A geologia desta bacia é caracterizada por espessas camadas de rochas ígneas, as soleiras de diabásio, que podem ser vistas em toda seção empilhada como refletores com forte amplitude e baixa frequência. O alto contraste de impedância sísmica entre as rochas sedimentares e as soleiras de diabásio gera reflexões múltiplas e reverberações que podem levar a uma interpretação sísmica errada das seções empilhadas. Neste trabalho, para melhorar a qualidade das seções empilhadas, propomos um fluxograma de processamento que adicione etapas de filtragem de múltiplas, através da Deconvolução Preditiva Multicanal e da Transformada Radon parabólica. Este estudo foi realizado primeiramente em dados sintéticos para testar a metodologia, e depois em dados reais cedidos pela Agência Nacional de Petróleo, Gás Natural e Biocombustíveis (ANP). O fluxograma de processamento convencional foi aplicado utilizando software comercial de processamento, como o SeisSpace/ProMAX, códigos implementados em Fortran 90 disponíveis no Centro de Pesquisa em Geofísica e Geologia, Universidade Federal da Bahia (CPGG/UFBA). Os resultados obtidos foram satisfatórios com a metodologia utilizada, além de visíveis melhorias na qualidade das seções sísmicas empilhadas após atenuação dos ruídos indesejados.Palavras-chave: atenuação de múltiplas, processamento sísmico, sísmica de reflexão.

Stationarity transformation of multiples to improve the performance of predictive deconvolution

Geophysics ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 723-737 ◽  
Author(s):  
Michael Schoenberger ◽  
Louis M. Houston
Keyword(s):  
Low Frequency ◽  
Real Data ◽  
Time Shift ◽  
Model Data ◽  
Multiple Reflections ◽  
Data Set ◽  
First Order ◽  
Layer Cake ◽  
Predictive Deconvolution ◽  
Dominant Period

Predictive deconvolution is a very effective multiple attenuator for zero‐offset data and for nonzero offset data acquired in water depths less than 100 m. However, predictive deconvolution’s efficacy degrades rapidly with offset, a degradation that correlates highly with nonstationarity of the primary‐to‐multiple traveltime separation. For model data, predictive deconvolution’s performance degrades by a factor of two when the multiple period changes by only 5 ms (20% of the seismic wavelet’s dominant period) within the deconvolution gate. For two‐thirds of the model‐data offsets, the change in primary‐multiple separation on each trace exceeds 40% of the dominant period, and deconvolution is completely ineffective at removing multiples. We develop a stationarity transform, which is a moveout operation or a time‐variable time shift that can be applied separately to each trace. The stationarity transform stabilizes the traveltime separation between primary and first‐order multiple, based upon the assumptions of hyperbolic moveout, layer‐cake geology, and Dix multiple velocities. After applying the stationarity transform to a model data set consisting of primaries and first‐order multiples only, predictive deconvolution suppresses multiples at the theoretical suppression limit for all offsets. Furthermore, predictive deconvolution is equally effective for low‐frequency and high‐frequency wavelets. When the data set is made more realistic by including higher‐order multiples, predictive deconvolution’s ability to suppress multiple reflections degrades only slightly with offset. Stationarity transformation also improves predictive deconvolution’s multiple suppression on a real data set. Because the real data set is from a region where the water depth is shallower than 100 m, predictive deconvolution suppresses multiples effectively on the near‐ and middle‐offset traces, even without stationarity transformation. However, on the farthest offsets, stationarity transformation improves the efficacy of predictive deconvolution significantly.

scholarly journals Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Georg Steinbuss ◽  
Klemens Böhm
Keyword(s):  
Outlier Detection ◽  
Synthetic Data ◽  
Real Data ◽  
Detection Methods ◽  
Quality Of Data ◽  
Benchmark Data ◽  
Core Idea ◽  
Generic Process ◽  
Unsupervised Outlier Detection

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.

scholarly journals Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator

2019 ◽  
Vol 33 ◽  
pp. 978-985 ◽  
Author(s):  
Hoon Kim ◽  
Kangwook Lee ◽  
Gyeongjo Hwang ◽  
Changho Suh
Keyword(s):  
Driving Simulator ◽  
Synthetic Data ◽  
Real Data ◽  
Classification Performance ◽  
Motion Model ◽  
Time To Collision ◽  
Data Generator ◽  
Internal States ◽  
Accident Data

Developing a computer vision-based algorithm for identifying dangerous vehicles requires a large amount of labeled accident data, which is difficult to collect in the real world. To tackle this challenge, we first develop a synthetic data generator built on top of a driving simulator. We then observe that the synthetic labels that are generated based on simulation results are very noisy, resulting in poor classification performance. In order to improve the quality of synthetic labels, we propose a new label adaptation technique that first extracts internal states of vehicles from the underlying driving simulator, and then refines labels by predicting future paths of vehicles based on a well-studied motion model. Via real-data experiments, we show that our dangerous vehicle classifier can reduce the missed detection rate by at least 18.5% compared with those trained with real data when time-to-collision is between 1.6s and 1.8s.

Multiple attenuation with 3D high-order high-resolution parabolic Radon transform using lower frequency constraints

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V317-V328
Author(s):  
Jitao Ma ◽  
Guoyang Xu ◽  
Xiaohong Chen ◽  
Xiaoliu Wang ◽  
Zhenjiang Hao
Keyword(s):  
High Resolution ◽  
Radon Transform ◽  
Seismic Data ◽  
Computational Cost ◽  
Real Data ◽  
Multiple Model ◽  
Multiple Attenuation ◽  
Common Depth Point ◽  
Frequency Constraint ◽  
Parabolic Radon Transform

The parabolic Radon transform is one of the most commonly used multiple attenuation methods in seismic data processing. The 2D Radon transform cannot consider the azimuth effect on seismic data when processing 3D common-depth point gathers; hence, the result of applying this transform is unreliable. Therefore, the 3D Radon transform should be applied. The theory of the 3D Radon transform is first introduced. To address sparse sampling in the crossline direction, a lower frequency constraint is introduced to reduce spatial aliasing and improve the resolution of the Radon transform. An orthogonal polynomial transform, which can fit the amplitude variations in different parabolic directions, is combined with the dealiased 3D high-resolution Radon transform to account for the amplitude variations with offset of seismic data. A multiple model can be estimated with superior accuracy, and improved results can be achieved. Synthetic and real data examples indicate that even though our method comes at a higher computational cost than existing techniques, the developed approach provides better attenuation of multiples for 3D seismic data with amplitude variations.

Decomposition of electromagnetic fields into upgoing and downgoing components

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. G211-G223 ◽  
Author(s):  
Lasse Amundsen ◽  
Lars Løseth ◽  
Rune Mittet ◽  
Svein Ellingsrud ◽  
Bjørn Ursin
Keyword(s):  
Synthetic Data ◽  
Low Frequency ◽  
Real Data ◽  
Sea Surface ◽  
Linear Phase ◽  
Electric Permittivity ◽  
The Real ◽  
Electromagnetic Data ◽  
Electromagnetic Measurements ◽  
Em Field

This paper gives a unified treatment of electromagnetic (EM) field decomposition into upgoing and downgoing components for conductive and nonconductive media, where the electromagnetic data are measured on a plane in which the electric permittivity, magnetic permeability, and electrical conductivity are known constants with respect to space and time. Above and below the plane of measurement, the medium can be arbitrarily inhomogeneous and anisotropic. In particular, the proposed decomposition theory applies to marine EM, low-frequency data acquired for hydrocarbon mapping where the upgoing components of the recorded field guided and refracted from the reservoir, that are of interest for the interpretation. The direct-source field, the refracted airwave induced by the source, the reflected field from the sea surface, and mostmagnetotelluric noise traveling downward just below the seabed are field components that are considered to be noise in electromagnetic measurements. The viability and validity of the decomposition method is demonstrated using modeled and real marine EM data, also termed seabed logging (SBL) data. The synthetic data are simulated in a model that is fairly representative of the geologic area where the real SBL were collected. The results from the synthetic data study therefore are used to assist in the interpretation of the real data from an area with [Formula: see text] water depth above a known gas province offshore Norway. The effect of the airwave is seen clearly in measured data. After field decomposition just below the seabed, the upgoing component of the recorded electric field has almost linear phase, indicating that most of the effect of the airwave component has been removed.

Parabolic Radon transform, sampling and efficiency

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 667-678 ◽  
Author(s):  
M. A. Schonewille ◽  
A. J. W. Duijndam
Keyword(s):  
Radon Transform ◽  
Temporal Frequency ◽  
Computation Time ◽  
Sampling Interval ◽  
Good Choice ◽  
Irregular Sampling ◽  
Fourier Theory ◽  
Curvature Parameter ◽  
Parabolic Radon Transform

A good choice of the sampling in the transform domain is essential for a successful application of the parabolic Radon transform. The parabolic Radon transform is computed for each temporal frequency and is essentially equivalent to the nonuniform Fourier transform. This leads to new and useful insights in the parabolic Radon transform. Using nonuniform Fourier theory, we derive a minimum sampling interval for the curvature parameter and a maximum curvature range for which stability is guaranteed for general (irregular) sampling. A significantly smaller sampling interval requires stabilization. If diagonal stabilization is used, no gain in resolution is obtained. In contrast to conventional implementations, the curvature sampling interval is proposed to be inversely proportional to the temporal frequency. This results in improved quality of the transform and yields significant savings in computation time.

Offset-extended sparse Radon transform: application to multiple suppression in the presence of AVO

Geophysics ◽  
2021 ◽  
pp. 1-41
Author(s):  
Nasser Kazemi ◽  
Mauricio D. Sacchi
Keyword(s):  
Radon Transform ◽  
Synthetic Data ◽  
Stable Solution ◽  
Model Space ◽  
Real Data ◽  
Basis Functions ◽  
Model Domain ◽  
Mississippi Canyon ◽  
Ill Posed ◽  
Ray Parameter

The conventional Radon transform suffers from a lack of resolution when data kinematics and amplitudes differ from those of the Radon basis functions. Also, a limited aperture of data, missing traces, aliasing, a finite number of scanned ray parameters, noise, residual statics, and amplitude variations with offset (AVO) reduce the de-correlation power of the Radon basis functions. Posing Radon transform estimation as an inverse problem by searching for a sparse model that fits the data improves the performance of the algorithm. However, due to averaging along the offset axis, the conventional Radon transform cannot preserve AVO. Accordingly, we modify the Radon basis functions by extending the model domain along the offset direction. Extending the model space helps in fitting data; however, computing the offset-extended Radon transform is an under-determined and ill-posed problem. To alleviate this shortcoming, we add model domain sparsity and smoothing constraints to yield a stable solution. We develop an algorithm using offset-extended Radon basis functions with sparsity promoting in offset-stacked Radon images in conjunction with a smoothing restriction along the offset axis. As the inverted model is sparse and fits the data, muting common-offset Radon panels based on ray-parameter/curvature is sufficient for separating primaries from multiples. We successfully apply the algorithm to suppress multiples in the presence of strong AVO on synthetic data and a real data example from the Gulf of Mexico, Mississippi Canyon. The results show that extending the Radon model space is necessary for improving the separation and suppression of the multiples in the presence of strong AVO.

Multiple attenuation in the parabolic τ-p domain using wavefront characteristics of multiple generating primaries

Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1806-1815 ◽  
Author(s):  
Evgeny Landa ◽  
Igor Belfer ◽  
Shemer Keydar
Keyword(s):  
Radon Transform ◽  
Time Windows ◽  
Real Data ◽  
Multiple Model ◽  
Transform Method ◽  
Multiple Attenuation ◽  
The Common ◽  
P Domain ◽  
Multiple Prediction ◽  
Parabolic Radon Transform

The problem of multiple attenuation has been solved only partially. One of the most common methods of attenuating multiples is an approach based on the Radon transform. It is commonly accepted that the parabolic Radon transform method is only able to attenuate multiples with significant moveouts. We propose a new 2-D method for attenuation of both surface‐related and interbed multiples in the parabolic τ-p domain. The method is based on the prediction of a multiple model from the wavefront characteristics of the primary events. Multiple prediction comprises the following steps: 1) For a given multiple code, the angles of emergence and the radii of wavefront curvatures are estimated for primary reflections for each receiver in the common‐shotpoint gather. 2) The intermediate points which compose a specified multiple event are determined for each shot‐receiver pair. 3) Traveltimes of the multiples are calculated. Wavefields within time windows around the predicted traveltime curves may be considered as multiple model traces which we use for multiple attenuation process. Using the predicted multiple traveltimes, we can define the area in the τ-p domain which contains the main energy of the multiple event. Resolution improvement of the parabolic Radon operator can be achieved through a simple multiplication of each sample in the τ-p space by a nonlinear semblance function. In this work, we follow the idea of defining the multiple reject areas automatically by comparing the energy of the multiple model and the original input data in the τ-p space. We illustrate the usefulness of this algorithm for the attenuation of multiples on both synthetic and real data.

A fast, modified parabolic Radon transform

Geophysics ◽  
2011 ◽  
Vol 76 (1) ◽  
pp. V11-V24 ◽  
Author(s):  
Brahim Abbad ◽  
Bjørn Ursin ◽  
Milton J. Porsani
Keyword(s):  
Frequency Domain ◽  
Radon Transform ◽  
Low Frequency ◽  
Small Time ◽  
Time Interval ◽  
Seismic Line ◽  
Frequency Domain Methods ◽  
Multiple Paths ◽  
Value Decomposition ◽  
Parabolic Radon Transform

We propose a fast and efficient frequency-domain implementation of a modified parabolic Radon transform (modified PRT) based on a singular value decomposition (SVD) with applications to multiple removal. The problem is transformed into a complex linear system involving a single operator after merging the curvature-frequency parameters into a new variable. A complex SVD is applied to this operator and the forward transform is computed by means of a complex back-substitution that is frequency independent. The new transform offers a wider curvature range at signal frequencies than the other PRT implementations, allowing the mapping in the transform domain of low-frequency events with important residual moveouts (long period multiples). The method is capable of resolving multiple energy from primaries when they interfere in a small time interval, a situation where most frequency-domain methods fail to discriminate the different wave types. Additionally, the method resists better to amplitude variations with offset (AVO) effects in the data than does the iteratively reweighted least-squares (IRLS) method. The proposed method was successfully applied to a deep-water seismic line in the Gulf of Mexico to attenuate water-bottom multiples and subsequent peg-legs originating from multiple paths in the water column. Combining the suggested method with the surface-related multiple elimination (SRME) has led to the best attenuation results in removing residual multiple energy in the stack.

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