Wavefield decomposition based on acoustic reciprocity: Theory and applications to marine acquisition

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. WA41-WA54 ◽  
Author(s):  
Roald Gunnar van Borselen ◽  
Jacob Fokkema ◽  
Peter van den Berg
Keyword(s):  
Free Surface ◽  
Seismic Source ◽  
Sensor Data ◽  
Constant Depth ◽  
Seismic Acquisition ◽  
Single Sensor ◽  
Recorded Data ◽  
Marine Seismic ◽  
Wavefield Decomposition ◽  
Multisensor Data

In marine seismic acquisition, the free surface generates seismic events in our recorded data that are often categorized as noise because these events do not contain independent information about the subsurface geology. Ghost events are considered as such noise because these events are generated when the energy generated by the seismic source, as well as any upgoing wavefield propagating upward from the subsurface, is reflected downward by the free surface. As a result, complex interference patterns between up- and downgoing wavefields are present in the recorded data, affecting the spectral bandwidth of the recorded data negatively. The interpretability of the data is then compromised, and hence it is desirable to remove the ghost events from the data. Rayleigh’s reciprocity theorem is used to derive the relevant equations for wavefield decomposition for multisensor and single-sensor data, for depth-varying and depth-independent recordings from marine seismic experiments using a single-source or dual-source configuration. A comparison is made between the results obtained for a 2D synthetic example designed to highlight the strengths and weaknesses of the various acquisition configurations. It is demonstrated that, using the proposed wavefield decomposition method, multisensor data (measurements of pressure and particle velocity components, or multidepth pressure measurements) allow for optimal wavefield decomposition as independent measurements are used to eliminate the interference patterns caused by the free surface. Single-sensor data using constant-depth recordings are found to be incapable of producing satisfactory results in the presence of noise. Single-sensor data using a configuration with depth-varying measurements are able to deliver better results than when constant-depth recordings are used, but the results obtained are not of the same quality when multisensor data are used.

scholarly journals On low frequencies emitted by air guns at very shallow depths — An experimental study

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. P61-P71 ◽  
Author(s):  
Daniel Wehner ◽  
Martin Landrø ◽  
Lasse Amundsen
Keyword(s):  
Large Volume ◽  
Field Experiments ◽  
Field Measurements ◽  
Seismic Source ◽  
Sea Surface ◽  
Frequency Signal ◽  
Low Frequencies ◽  
Air Gun ◽  
Seismic Acquisition ◽  
Marine Seismic

In marine seismic acquisition, the enhancement of frequency amplitudes below 5 Hz is of special interest because it improves imaging of the subsurface. The frequency content of the air gun, the most commonly used marine seismic source, is mainly controlled by its depth and the volume. Although the depth dependency on frequencies greater than 5 Hz has been thoroughly investigated, for frequencies less than 5 Hz it is less understood. However, recent results suggest that sources fired very close to the sea surface might enhance these very low frequencies. Therefore, we conduct dedicated tank experiments to investigate the changes of the source signal for very shallow sources in more detail. A small-volume air gun is fired at different distances from the water-air interface, including depths for which the air bubble bursts directly into the surrounding air. The variations of the oscillating bubble and surface disturbances, which can cause changes of the reflected signal from the sea surface, are explored to determine whether an increased frequency signal below 5 Hz can be achieved from very shallow air guns. The results are compared with field measurements of a large-volume air gun fired close to the sea surface. The results reveal an increased signal for frequencies below 5 Hz of up to 10 and 20 dB for the tank and field experiments, respectively, for the source depth at which the air gun bubble bursts directly into the surrounding air. For large-volume air guns, an increased low-frequency signal might also be achieved for sources that are slightly deeper than this bursting depth. From these observations, new design considerations in the geometry of air-gun arrays in marine seismic acquisition are suggested.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Examining the contribution of near real-time data for rapid seismic loss assessment of structures

2021 ◽  
pp. 147592172199621
Author(s):  
Enrico Tubaldi ◽  
Ekin Ozer ◽  
John Douglas ◽  
Pierre Gehl
Keyword(s):  
Real Time ◽  
Damage Assessment ◽  
Free Field ◽  
Seismic Source ◽  
Seismic Damage ◽  
Added Value ◽  
Sensor Data ◽  
Sources Of Information ◽  
Bridge Model ◽  
Seismic Damage Assessment

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.

scholarly journals Categorizing Bicycling Environment Quality Based on Mobile Sensor Data and Bicycle Flow Data

2021 ◽  
Vol 13 (8) ◽  
pp. 4085
Author(s):  
Yang Bian ◽  
Ling Li ◽  
Huan Zhang ◽  
Dandan Xu ◽  
Jian Rong ◽  
...  
Keyword(s):  
Theoretical Method ◽  
Traffic Monitoring ◽  
Assessment Model ◽  
Sensor Data ◽  
The Sustainable Development ◽  
Environment Quality ◽  
Perceived Satisfaction ◽  
Level Data ◽  
Recorded Data ◽  
Bicycle Sharing System

The bicycle is a healthy and sustainable transport mode due to its emission-free characteristics. To increase bicycle use, it is fundamental to provide bicycle-friendly environments. To better monitor bicycle environments, this study proposed the concept of bicycling environment quality (BEQ), which was defined by perceived satisfaction and conflict level. Data collection was conducted at 19 road segments in five sites located in Beijing, China. Then, speed-related and acceleration-related bicycling behavior indicators (BBIs) were extracted from data collected using sensors on mobile phones, while bicycling environment indicators (BEIs), such as bicycle flow, were extracted from recorded data. Taking the BBIs and BEIs as input attributes, a two-level BEQ classification assessment model based on a random forest (RF) algorithm was constructed. The proposed RF-based classification assessment model was able to produce approximately 77.35% overall correct classification. The results demonstrate the feasibility of using GPS data in evaluating BEQ. In addition, a novel dockless bicycle-sharing system (DBS)-based framework for bicycle traffic monitoring is discussed, which is of great significance in the sustainable development of bicycles. This study provides a theoretical method for objective BEQ assessment. It can further be used by planners and road administrators to monitor and improve BEQ and by individual cyclists for optimal route choice.

Coherent noise in marine seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Source ◽  
Coherent Noise ◽  
Critical Data ◽  
Predictive Deconvolution ◽  
Scattered Energy ◽  
Marine Seismic ◽  
The Many ◽  
Water Bottom

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.

High-resolution 3D marine seismic acquisition and imaging in shallow waters: Application to shallow fault detection, Beppu-Bay, Japan

2021 ◽  
Author(s):  
Yosuke Teranishi ◽  
Fumitoshi Murakami ◽  
Shinji Kawasaki ◽  
Motonori Higashinaka ◽  
Kei Konno ◽  
...  
Keyword(s):  
High Resolution ◽  
Fault Detection ◽  
Shallow Waters ◽  
Seismic Acquisition ◽  
Marine Seismic

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

scholarly journals Surface and internal waves in a liquid of variable depth

1969 ◽  
Vol 1 (1) ◽  
pp. 29-46 ◽  
Author(s):  
D. G. Hurley ◽  
J. Imberger
Keyword(s):  
Free Surface ◽  
Gravity Wave ◽  
Internal Waves ◽  
Infinite Number ◽  
Variable Depth ◽  
Constant Depth ◽  
Stratified Liquid

Consider a stably stratified liquid, whose density varies exponentially with the vertical co-ordinate, that is bounded above by a free surface and below by a bed whose height depends on only one of the horizontal co-ordinates. Suppose that a gravity wave, that may be either a surface or an internal one, is travelling in a direction normal to the lines of constant depth. It is shown that if the frequency is below a certain value an infinite number of waves, all of the same frequency but having differing wave lengths, are generated and expressions for their amplitude are given in terms of the changes in depth which are assumed to be small.

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