The role of visual saliency in the automation of seismic interpretation          下载免费PDF全文

Muhammad Amir Shafiq  Tariq Alshawi  Zhiling Long  Ghassan AlRegib 《Geophysical Prospecting》2018,66(Z1):132-143

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modelling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using this attribute we cannot only highlight the neighbouring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post‐processing. The proposed delineation workflow detects the salt‐dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt‐dome delineation algorithms, we have used a reference salt‐dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared with other salt‐dome delineation algorithms and shows a promising potential in seismic interpretation.  相似文献   

长短时记忆神经网络在地电场数据处理中的应用          下载免费PDF全文

汪凯翔  黄清华  吴思弘 《地球物理学报》2020,63(8):3015-3024

作为深度学习方法的一种,长短时记忆神经网络(LSTM)是一种信号处理的重要方法.本文基于实际观测地电场数据来合成训练集,对特定结构的长短时记忆神经网络进行训练,将训练所得网络对测试集数据进行测试后,将网络应用至实际观测数据.结果显示,经过训练的网络很好地学到了训练集样本的特征,对测试集数据的信噪比压制了约20 dB,并过滤了人为添加的特定频率的干扰成分,对实际观测数据处理后得到明显的日变、半日变以及半月变、月变、半年变、年变等潮汐响应,表明长短时记忆神经网络可以有效应用于地电场数据处理研究.  相似文献   

Local outlier factor as part of a workflow for detecting and attenuating blending noise in simultaneously acquired data     

Woodon Jeong  Constantinos Tsingas  Mohammed S. Almubarak 《Geophysical Prospecting》2020,68(5):1523-1539

A number of deblending methods and workflows have been reported in the past decades to eliminate the source interference noise recorded during a simultaneous shooting acquisition. It is common that denoising algorithms focusing on optimizing coherency and weighting down/ignoring outliers can be considered as deblending tools. Such algorithms are not only enforcing coherency but also handling outliers either explicitly or implicitly. In this paper, we present a novel approach based on detecting amplitude outliers and its application on deblending based on a local outlier factor that assigns an outlier-ness (i.e. a degree of being an outlier) to each sample of the data. A local outlier factor algorithm quantifies outlier-ness for an object in a data set based on the degree of isolation compared with its locally neighbouring objects. Assuming that the seismic pre-stack data acquired by simultaneous shooting are composed of a set of non-outliers and outliers, the local outlier factor algorithm evaluates the outlier-ness of each object. Therefore, we can separate the data set into blending noise (i.e. outlier) and signal (i.e. non-outlier) components. By applying a proper threshold, objects having high local outlier factors are labelled as outlier/blending noise, and the corresponding data sample could be replaced by zero or a statistically adequate value. Beginning with an explanation of parameter definitions and properties of local outlier factor, we investigate the feasibility of a local outlier factor application on seismic deblending by analysing the parameters of local outlier factor and suggesting specific deblending strategies. Field data examples recorded during simultaneous shooting acquisition show that the local outlier factor algorithm combined with a thresholding can detect and attenuate blending noise. Although the local outlier factor application on deblending shows a few shortcomings, it is consequently noted that the local outlier factor application in this paper obviously achieves benefits in terms of detecting and attenuating blending noise and paves the way for further geophysical applications.  相似文献   

Attenuation of marine seismic interference noise employing a customized U-Net     

Jing Sun  Sigmund Slang  Thomas Elboth  Thomas Larsen Greiner  Steven McDonald  Leiv-J. Gelius 《Geophysical Prospecting》2020,68(3):845-871

Marine seismic interference noise occurs when energy from nearby marine seismic source vessels is recorded during a seismic survey. Such noise tends to be well preserved over large distances and causes coherent artefacts in the recorded data. Over the years, the industry has developed various denoising techniques for seismic interference removal, but although well performing, they are still time-consuming in use. Machine-learning-based processing represents an alternative approach, which may significantly improve the computational efficiency. In the case of conventional images, autoencoders are frequently employed for denoising purposes. However, due to the special characteristics of seismic data as well as the noise, autoencoders failed in the case of marine seismic interference noise. We, therefore, propose the use of a customized U-Net design with element-wise summation as part of the skip-connection blocks to handle the vanishing gradient problem and to ensure information fusion between high- and low-level features. To secure a realistic study, only seismic field data were employed, including 25,000 training examples. The customized U-Net was found to perform well, leaving only minor residuals, except for the case when seismic interference noise comes from the side. We further demonstrate that such noise can be treated by slightly increasing the depth of our network. Although our customized U-Net does not outperform a standard commercial algorithm in quality, it can (after proper training) read and process one single shot gather in approximately 0.02 s. This is significantly faster than any existing industry denoising algorithm. In addition, the proposed network processes shot gathers in a sequential order, which is an advantage compared with industry algorithms that typically require a multi-shot input to break the coherency of the noise.  相似文献   

Low-rank seismic denoising with optimal rank selection for hankel matrices   总被引：1，自引：0，他引：1  

Chong Wang  Zhihui Zhu  Hanming Gu 《Geophysical Prospecting》2020,68(3):892-909

Based on the fact that the Hankel matrix representing clean seismic data is low rank, low-rank approximation methods have been widely utilized for removing noise from seismic data. A common strategy for real seismic data is to perform the low-rank approximations for small local windows where the events can be approximately viewed as linear. This raises a fundamental question of selecting an optimal rank that best captures the number of events for each local window. Gavish and Donoho proposed a method to select the rank when the noise is independent and identically distributed. Gaussian matrix by analysing the statistical performance of the singular values of the Gaussian matrices. However, such statistical performance is not available for noisy Hankel matrices. In this paper, we adopt the same strategy and propose a rule that computes the number of singular values exceed the median singular value by a multiplicative factor. We suggest a multiplicative factor of 3 based on simulations which mimic the theories underlying Gavish and Donoho in the independent and identically distributed Gaussian setting. The proposed optimal rank selection rule can be incorporated into the classical low-rank approximation method and many other recently developed methods such as those by shrinking the singular values. The low-rank approximation methods with optimally selected rank rule can automatically suppress most of the noise while preserving the main features of the seismic data in each window. Experiments on both synthetic and field seismic data demonstrate the superior performance of the proposed rank selection rule for seismic data denoising.  相似文献   

A method for classifying pre‐stack seismic data based on amplitude–frequency attributes and self‐organizing maps          下载免费PDF全文

Erik Molino‐Minero‐Re  Ernesto Rubio‐Acosta  Héctor Benítez‐Pérez  Juan Marcos Brandi‐Purata  Nora Isabel Pérez‐Quezadas  Demetrio Fabián García‐Nocetti 《Geophysical Prospecting》2018,66(4):673-687

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Stretch-free generalized normal moveout correction     

Jorge H. Faccipieri  Tiago A. Coimbra  Rodrigo Bloot 《Geophysical Prospecting》2019,67(1):52-68

The effective application of normal moveout correction processes mainly depends on four factors: the chosen traveltime approximation, the stretching associated with the given traveltime, crossing events and phase changes, the last two being inherent to the seismic data. In this context, we conduct a quantitative analysis on stretching considering a general traveltime expression depending on half-offset and midpoint coordinates. Through this analysis, we propose a mathematically proven procedure to eliminate stretching, which can be applied to any traveltime approximation. The proposed method is applied to synthetic and real data sets, considering different traveltime approximations and achieved complete elimination of stretching.  相似文献   

Identification and classification of transient pulses observed in magnetometer array data by time-domain principal component analysis filtering          下载免费PDF全文

Karl N. Kappler  Daniel D. Schneider  Laura S. MacLean  Thomas E. Bleier 《地震科学（英文版）》2017,30(4):193-207

A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection.Time series of these ‘‘training events' ' are represented in matrix form and transpose-multiplied to generate timedomain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window(approximately the width of the training event) is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigenvectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled(50 Hz) data from six observatories, each equipped with threecomponent induction coil magnetometers. We examine a90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California,together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generatednoise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.  相似文献   

The impact of surface‐wave attenuation on 3‐D seismic survey design          下载免费PDF全文

Tomohide Ishiyama  Gerrit Blacquière  Wim A. Mulder 《Geophysical Prospecting》2017,65(1):86-96

Three‐dimensional seismic survey design should provide an acquisition geometry that enables imaging and amplitude‐versus‐offset applications of target reflectors with sufficient data quality under given economical and operational constraints. However, in land or shallow‐water environments, surface waves are often dominant in the seismic data. The effectiveness of surface‐wave separation or attenuation significantly affects the quality of the final result. Therefore, the need for surface‐wave attenuation imposes additional constraints on the acquisition geometry. Recently, we have proposed a method for surface‐wave attenuation that can better deal with aliased seismic data than classic methods such as slowness/velocity‐based filtering. Here, we investigate how surface‐wave attenuation affects the selection of survey parameters and the resulting data quality. To quantify the latter, we introduce a measure that represents the estimated signal‐to‐noise ratio between the desired subsurface signal and the surface waves that are deemed to be noise. In a case study, we applied surface‐wave attenuation and signal‐to‐noise ratio estimation to several data sets with different survey parameters. The spatial sampling intervals of the basic subset are the survey parameters that affect the performance of surface‐wave attenuation methods the most. Finer spatial sampling will reduce aliasing and make surface‐wave attenuation easier, resulting in better data quality until no further improvement is obtained. We observed this behaviour as a main trend that levels off at increasingly denser sampling. With our method, this trend curve lies at a considerably higher signal‐to‐noise ratio than with a classic filtering method. This means that we can obtain a much better data quality for given survey effort or the same data quality as with a conventional method at a lower cost.  相似文献   

Feasibility of utilizing wavelet phase to map the CO2 plume at the Ketzin pilot site,Germany          下载免费PDF全文

Fei Huang  Christopher Juhlin  Li Han  Daniel Sopher  Monika Ivandic  Ben Norden  Wubing Deng  Fengjiao Zhang  Thomas Kempka  Stefan Lüth 《Geophysical Prospecting》2017,65(2):523-543

Spectral decomposition is a powerful tool that can provide geological details dependent upon discrete frequencies. Complex spectral decomposition using inversion strategies differs from conventional spectral decomposition methods in that it produces not only frequency information but also wavelet phase information. This method was applied to a time‐lapse three‐dimensional seismic dataset in order to test the feasibility of using wavelet phase changes to detect and map injected carbon dioxide within the reservoir at the Ketzin carbon dioxide storage site, Germany. Simplified zero‐offset forward modelling was used to help verify the effectiveness of this technique and to better understand the wavelet phase response from the highly heterogeneous storage reservoir and carbon dioxide plume. Ambient noise and signal‐to‐noise ratios were calculated from the raw data to determine the extracted wavelet phase. Strong noise caused by rainfall and the assumed spatial distribution of sandstone channels in the reservoir could be correlated with phase anomalies. Qualitative and quantitative results indicate that the wavelet phase extracted by the complex spectral decomposition technique has great potential as a practical and feasible tool for carbon dioxide detection at the Ketzin pilot site.  相似文献