Bayesian environmental inversion of airgun modal dispersion using a single hydrophone in the Chukchi Sea

Warner, Graham A.; Dosso, Stan E.; Dettmer, Jan; Hannay, David

doi:10.1121/1.4921284

Cited by 30 publications

(8 citation statements)

References 34 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data d is a set of modal dispersion curves. The replicas d i (m) are given by (9). They are simulated using the modal code ORCA [41].…”

Section: Summary and Application To Sbcex Data Setmentioning

confidence: 99%

“…Each frequency component of each mode travels with its own group speed, so that modal arrivals have frequency dependent travel times. Provided that these travel times can be estimated from the data, they can be used as input for inversion algorithms, to estimate source range [4]- [7], water sound-speed profile (SSP) [8], [9], and/or seabed geoacoustic properties [5], [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…In such case, it is necessary to resort to more advanced signal processing. In the past ten years, a nonlinear processing method called warping has emerged as an effective way to resolve modes with a single receiver, even when they are not separated on a conventional spectrogram [5]- [9], [11], [13]- [18].…”

Section: Introductionmentioning

confidence: 99%

“…It also illustrates how data information content varies with source type, and thus with number of modes and bandwidth. Note that trans-D geoacoustic inversion has been successfully applied in the past to modal TF dispersion curves for a hard seabed [9], but this paper is the first in a fine grained sediment context. More importantly, this paper provides new information about inversion consistency and data information content, by using a common method to compare two independent data sets, nearly collocated in space and under similar ocean environment conditions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Trans-Dimensional Inversion of Modal Dispersion Data on the New England Mud Patch

Bonnel¹,

Dosso²,

Eleftherakis³

et al. 2020

IEEE J. Oceanic Eng.

Self Cite

View full text Add to dashboard Cite

This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content. Index Terms-Geoacoustic inversion, New England mud patch, seabed characterization experiment (SBCEX), underwater acoustics, warping.

show abstract

“…The data d is a set of modal dispersion curves. The replicas d i (m) are given by (9). They are simulated using the modal code ORCA [41].…”

Section: Summary and Application To Sbcex Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Trans-Dimensional Inversion of Modal Dispersion Data on the New England Mud Patch

Bonnel¹,

Dosso²,

Eleftherakis³

et al. 2020

IEEE J. Oceanic Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The working principle of the air cleaner is that the compressed air in the container, due to the reversing effect of the solenoid valve, the airflow is ejected instantly and at high speed, which directly acts on the material to cause it to spread or collapse. 1,2 This operation can be manual control or automatic control, which is quite easy to use. There are many types of shakers, the most widely used of which are electric shakers.…”

Section: Introductionmentioning

confidence: 99%

The automation control system of intelligent flexible clearing robot

Fan

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

The cleaning of cement warehouse is a labor-intensive and high-risk operation. The applicability and cleaning effect of traditional work tools are poor. Therefore, most of the methods are manual cleaning, which is inefficient and prone to accidents. It is of great social significance and practical value to develop an intelligent flexible warehouse cleaning robot that replaces the manual cleaning of cement. When the top of the cement warehouse is small and the volume of the cement warehouse is very large, how to ensure that the working range of the cleaning system can cover the entire area, and effectively and quickly complete the cleaning operation, and the automatic control of the cleaning robot has become an automatic control. Key issues to be addressed. Although the human–machine interface configuration technology has been widely used in automated monitoring systems, its graphical and componentized interface construction methods and the flexibility and scalability of the configuration interface have been widely recognized, but the human–machine interface, the coding method is still used in the construction, the development efficiency is low, and the interface is not open and flexible enough. At the same time, there are few research attempts on interface configuration. Interface configuration mostly only stays in the graphical phase of interface construction. Data and interactive control cannot meet the configuration requirements, and there is no complete implementation plan for configuration. Therefore, it has become an urgent need to change the traditional interface development mode and realize the configuration of man–machine interface. Based on the above background, the research content of this article is the research on the automatic control system of intelligent flexible clearing robot. Based on the understanding of the relevant properties of cement powder, this article discusses its flow state in the cement storehouse theoretically and provides a basis for subsequent structural design and stability research. A modularized and component-based configuration method for the numerical control interface is proposed. The interface is divided into three modules: data, graphics, and interactive control. The functional modules are assembled and the components are combined to implement the interface configuration. The experimental simulation results in this article show that compared to the traditional method, the amount of code editing of the configured human–machine interface is reduced by 46.34%, the development cycle is shortened by 43.72%, and the development efficiency of the configured human–machine interface has been greatly improved. It is confirmed that the configuration technology studied in this article can meet the requirements of automatic control systems.

show abstract

Sequential Inversion for Geoacoustic Parameters in the South China Sea Using Modal Dispersion Curves

et al. 2017

View full text Add to dashboard Cite

Bayesian environmental inversion of airgun modal dispersion using a single hydrophone in the Chukchi Sea

Cited by 30 publications

References 34 publications

Trans-Dimensional Inversion of Modal Dispersion Data on the New England Mud Patch

Trans-Dimensional Inversion of Modal Dispersion Data on the New England Mud Patch

The automation control system of intelligent flexible clearing robot

Sequential Inversion for Geoacoustic Parameters in the South China Sea Using Modal Dispersion Curves

Contact Info

Product

Resources

About