3D wavefront attribute determination and conflicting dip processing

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. V325-V343 ◽  
Author(s):  
Yujiang Xie ◽  
Dirk Gajewski
Keyword(s):  
Decomposition Method ◽  
Data Driven ◽  
Superior Performance ◽  
Pragmatic Approach ◽  
Swarm Optimization ◽  
Model Driven ◽  
Common Reflection Surface ◽  
The Common ◽  
Dip Angle

The knowledge of 3D wavefront attributes allows many important applications, such as stacking, 5D interpolation, 3D diffraction separation and imaging, and 3D wavefront tomography, just to name a few. For the determination of wavefront attributes, we use the common-reflection-surface (CRS) operator. We adopt a simultaneous search for the determination of wavefront attributes and combine it with conflicting dip processing. For the simultaneous search, we compare three heuristic global optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE). For conflicting dip processing, a dip angle decomposition method for the probed sample is introduced and the simultaneous search is independently performed in specified dip ranges to individually obtain attributes and semblance for each range. Results for the laterally heterogeneous 3D SEG C3WA data indicate that DE has superior performance to determine the 3D wavefront attributes when compared with PSO, GA, and the conventional pragmatic approach because a higher semblance and an improved set of wavefront attributes are achieved. A comparison of the data-driven wavefront attributes obtained from the DE with the model-driven wavefront attributes computed by kinematic and dynamic ray tracing reveals the validity of the data-driven wavefront attributes. Combining the simultaneous search with conflicting dip processing for the 3D CRS stack further improved reflected energy and diffraction details when compared with results without simultaneous search and/or conflicting dip processing.

scholarly journals Removal of internal multiples with the common-focus-point (CFP) approach: Part 1 — Explanation of the theory

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. V45-V60 ◽  
Author(s):  
A. J. Berkhout ◽  
D. J. Verschuur
Keyword(s):  
Wave Theory ◽  
Data Driven ◽  
Numerical Examples ◽  
Model Driven ◽  
The Common ◽  
Internal Multiples ◽  
Unified Description ◽  
Focus Point

Removal of surface and internal multiples can be formulated by removing the influence of downward-scattering boundaries and downward-scattering layers. The involved algorithms can be applied in a model-driven or a data-driven way. A unified description is proposed that relates both types of algorithms based on wave theory. The algorithm for the removal of surface multiples shows that muted shot records play the role of multichannel prediction filters. The algorithm for the removal of internal multiples shows that muted CFP gathers play the role of multichannel prediction filters. The internal multiple removal algorithm is illustrated with numerical examples. The conclusion is that the layer-related version of the algorithm has significant practical advantages.

Reliability of data-driven wavefront attributes in laterally heterogeneous media

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. O49-O62
Author(s):  
Yujiang Xie ◽  
Dirk Gajewski
Keyword(s):  
Heterogeneous Media ◽  
Data Driven ◽  
Velocity Variation ◽  
Good Match ◽  
Lateral Velocity ◽  
Model Driven ◽  
Automatic Picking ◽  
Velocity Variations ◽  
Velocity Changes

3D wavefront attributes play a major role in many processing steps, such as prestack data enhancement, diffraction separation, and wavefront tomography. For the determination of the 3D wavefront attributes, various stacking operators can be used by adopting semblance optimization. These operators are derived for laterally homogeneous media. In praxis, however, they are applied in real geologic environments with even strong lateral velocity variations such as salt structures. This leads to the question of the quality of the 3D wavefront attributes using these operators when determined in the presence of strong lateral velocity changes. We compared the 3D wavefront attributes determined by 3D common-reflection-surface (CRS) operator (called data-driven wavefront attributes) with the 3D wavefront attributes computed by 3D kinematic and dynamic ray tracing (called model-driven wavefront attributes). For the determination of the 3D CRS wavefront attributes, we have developed a global optimization scheme based on differential evolution. Reflection seismic data of the laterally heterogeneous 3D SEG C3WA salt model are considered, and the model-driven wavefront attributes are computed for a smoothed version of the 3D SEG salt model. The comparison reveals that the wavefront attributes for the normal-incidence-point ray indicate a very good match not only in areas of mild lateral velocity variation but even in regions with strong lateral velocity variations. Approximately 80%–90% of the total picks indicate the good match with a relative error of less than 10% when a semblance threshold of 0.1 is considered in the automatic picking process. This confirms the validity of the determined wavefront attributes even in the presence of strong lateral velocity changes. Using a higher semblance threshold in the automatic picking leads to fewer picks but with an even better match between model- and data-driven wavefront attributes.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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