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We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach. 相似文献
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In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories. 相似文献
66.
本文基于时间分布参数设置,利用伴随同化方法,反演了Ekman模型中随时间变化的风应力拖曳系数,并在孪生实验和实际实验中对该方法进行了验证。在孪生实验中,研究了参数反演结果对不同影响因素的响应,包括:风速分布、风应力系数分布、风应力系数初始猜测值、风应力系数独立变量个数、观测数据误差和观测的深度。孪生实验结果验证了伴随同化方法反演Ekman模型中时变风应力系数的有效性,具体包括如下五个方面结论:1)不同风速分布下均能成功反演出不同风应力拖曳系数分布; 2)反演结果对初始猜测值较为敏感,风应力系数初始猜测值越接近给定值,反演结果越好;3)风应力系数独立点个数的选取会显著影响反演结果,合理的选择有利于提高反演效率及减小观测数据误差;4)观测误差能够影响反演结果,观测数据误差在20%以下时能取得合理的反演结果; 5)反演结果对观测数据的表层和次表层流速更为敏感,这是由Ekman流的物理性质决定的。实际实验,利用百慕大锚系试验平台的风速和流速数据,去除周期性潮流和地转流成分后得到Ekman流成分,并作为观测输入到该同化模型,反演出了适用于该区域和该时段的随时间变化的风应力系数。通过比较模拟流速和观测流速,证明利用伴随同化方法能从实测数据中反演出合理的时变风应力系数,对于海洋模型风应力系数的确定是一项有益的尝试。 相似文献
67.
Tomohide Ishiyama Mohammed Y. Ali Satoshi Ishikawa Gerrit Blacquiere Shotaro Nakayama 《Geophysical Prospecting》2019,67(7):1852-1866
We introduce a concept of generalized blending and deblending, develop its models and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized blending and deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We applied our method to existing real data acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data. We discuss the complexity of blending properties on the deblending performance. In addition, we discuss the applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Conclusively, we should acquire blended data and reconstruct deblended data without serious problems but with the benefit of blended acquisition. 相似文献
68.
Application of Hilbert‐like transforms for enhanced processing of full tensor magnetic gradient data 下载免费PDF全文
M. Schiffler M. Queitsch R. Stolz H.‐G. Meyer N. Kukowski 《Geophysical Prospecting》2017,65(Z1):68-81
Commonly, geomagnetic prospection is performed via scalar magnetometers that measure values of the total magnetic intensity. Recent developments of superconducting quantum interference devices have led to their integration in full tensor magnetic gradiometry systems consisting of planar‐type first‐order gradiometers and magnetometers fabricated in thin‐film technology. With these systems measuring directly the magnetic gradient tensor and field vector, a significantly higher magnetic and spatial resolution of the magnetic maps is yield than those produced via conventional magnetometers. In order to preserve the high data quality in this work, we develop a workflow containing all the necessary steps for generating the gradient tensor and field vector quantities from the raw measurement data up to their integration into highresolution, lownoise, and artefactless two‐dimensional maps of the magnetic field vector. The gradient tensor components are processed by superposition of the balanced gradiometer signals and rotation into an Earth‐centred Earth‐fixed coordinate frame. As the magnetometers have sensitivity lower than that of gradiometers and the total magnetic intensity is not directly recorded, we employ Hilbert‐like transforms, e.g., integration of the gradient tensor components or the conversion of the total magnetic intensity derived by calibrated magnetometer readings to obtain these values. This can lead to a better interpretation of the measured magnetic anomalies of the Earth's magnetic field that is possible from scalar total magnetic intensity measurements. Our conclusions are drawn from the application of these algorithms on a survey acquired in South Africa containing full tensor magnetic gradiometry data. 相似文献
69.
Definitive superparamagnetic source identification through spatial,temporal, and amplitude analysis of airborne electromagnetic data 下载免费PDF全文
James Macnae 《Geophysical Prospecting》2017,65(4):1071-1084
The aim of this paper is to add confidence to existing methods using decay shape analysis to detect superparamagnetic responses in airborne electromagnetic data. While expensive to acquire, vertical spatial gradient measurements of the electromagnetic signals can discriminate near‐surface superparamagnetic sources. This research investigated the use of horizontal spatial gradients and amplitude information as further indicators of superparamagnetic. High horizontal gradients were shown both theoretically and in field data to help discriminate superparamagnetic from deep mineral targets. Further, superparamagnetic responses have characteristically small amplitudes inconsistent with realistic mineral exploration targets at shallow depths. 相似文献
70.
针对当前地震微测井仪器在施工和数据采集方面的难题,从提高数据采集质量及仪器功能性方面提出了几种改进方法.主要利用MEMS加速度传感器结合多路并行处理采集方法设计了数字检波器,并集成姿态检测技术直接实现水平分量数据的校正功能,保证系统采集的质量;提出了Ethernet-RS485分布式总线自动控制技术,实现各级联探管的随机组合,同时采用电动方式控制探管操作,提高系统的集成化程度.文中详细分析了利用姿态检测技术实现水平分量数据校正的方法,解决了级联探管随机组合控制和震源激发方位的判定问题.仪器单节探管支持三分量并行采集,有效分辨率达18位,参数可远程配置,同时具备快速显示探管姿态的功能,且支持远程控制,非常适于复杂环境下的工程勘探,整个系统实现全数字化操作且仅需四芯电缆互连,从而缩小了设备的体积和重量,降低施工布线的复杂性. 相似文献