Seismic Attributes and Acoustic Inversion for Shallow Marine Slope Stratigraphy Analysis

2021 ◽  
Author(s):  
Jungrak Son ◽  
Rebecca Boon ◽  
Julien Kuhn de Chizelle
Keyword(s):  
Signal Processing ◽  
Site Characterization ◽  
Seismic Attributes ◽  
Offshore Structures ◽  
Seismic Inversion ◽  
Shallow Marine ◽  
Time Domain Data ◽  
Geological Features ◽  
Marine Site ◽  
The Hilbert Transform

Abstract Geophysical seismic surveys have been used in marine site characterization for subsea engineering and the design of offshore structures. Signal processing plays a key role in obtaining seismic attributes from observed seismic data to identify subsurface geological features within complex shallow sediments. Instantaneous amplitude, phase, and frequency are the most widely used seismic attributes to indicate geological features, but those time-domain data are too limited to define an accurate subsurface model in depth. Therefore, seismic inversion is also required to generate additional geospatial subsurface model information to aid in shallow stratigraphy interpretation. In this paper, we applied both geophysical signal processing and stochastic seismic inversion to a high-resolution multichannel seismic dataset from the Eastern North American Margin (ENAM). Seismic attributes from the Hilbert transform and inversion modeling results (acoustic impedance and modeling uncertainty) were integrated to define better geological horizons and discontinuities. The results show the integrated geophysical subsurface models can support seismic interpretation and improve shallow marine site characterization.

scholarly journals Phase and the Hilbert transform

2014 ◽  
Vol 33 (10) ◽  
pp. 1164-1166 ◽  
Author(s):  
Steve Purves
Keyword(s):  
Signal Processing ◽  
Data Processing ◽  
Hilbert Transform ◽  
Seismic Data ◽  
Seismic Attributes ◽  
Seismic Data Processing ◽  
Data Set ◽  
The Hilbert Transform

The concept of phase permeates seismic data processing and signal processing in general, but it can be awkward to understand, and manipulating it directly can lead to surprising results. It doesn't help that the word phase is used to mean a variety of things, depending on whether we refer to the propagating wavelet, the observed wavelet, poststack seismic attributes, or an entire seismic data set. Several publications have discussed the concepts and ambiguities (e.g., Roden and Sepúlveda, 1999 ; Liner, 2002 ; Simm and White, 2002 ).

scholarly journals The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3487 ◽  
Author(s):  
Mohammed Sayed ◽  
Markus Nemitz ◽  
Simona Aracri ◽  
Alistair McConnell ◽  
Ross McKenzie ◽  
...  
Keyword(s):  
Signal Processing ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Communication Systems ◽  
Offshore Structures ◽  
Classification Approach ◽  
Communication Techniques ◽  
Sensing Platform ◽  
Online Classification ◽  
Monitoring Process

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair, and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new multi-sensing platform, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (Underwater Autonomous Vehicles, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal-processing techniques. We show an example of this approach for monitoring offshore wind turbines, by designing an experimental setup to mimic a wind turbine using a stepper motor and custom-designed acrylic fan blades. We use the distance sensor, which is a Time-of-Flight sensor, to achieve the monitoring process. We use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and Wi-Fi. We performed the online classification approach using two different communication techniques: LoRa and optical. We train our classifier offline and transfer its parameters to the Limpet for online classification. We simulated and classified four different faults in the operation of wind turbines. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among each of the functioning states. The results show successful classification using the online approach, where the processing and analysis of the data is done on-board by the microcontroller. By using online classification, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. This work shines light on how robots can perform on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.

See the Unseen through Target-Oriented Dip-Steered Seismic Resolution

2021 ◽  
Author(s):  
Muhammad Sajid ◽  
Ahmad Riza Ghazali
Keyword(s):  
Reservoir Characterization ◽  
Resolution Enhancement ◽  
Dominant Frequency ◽  
Seismic Inversion ◽  
Seismic Resolution ◽  
Geological Features ◽  
Spectral Enhancement ◽  
Enhancement Method ◽  
Layer Velocity ◽  
Seismic Volumes

Abstract Seismic resolution plays an important role not only in interpretation and reservoir characterization but also in seismic inversion and seismic attributes analysis. The resolution depends on several factors, including seismic frequency bandwidth, dominant frequency, and layer velocity. This paper presents a spectral resolution enhancement approach that is based on Non-stationary Differential Resolution (NSDR) that honors the local structural dip, better preserves amplitude and improves target-oriented seismic interpretation. The proposed technology is applied to both 2D and 3D seismic volumes and findings are compared with the oil industry common spectral enhancement algorithms. We demonstrate the target-oriented dip steering spectral enhancement method on two 3D field datasets and compare the resulting outcome with those obtained by conventional techniques. It is found that thinly layered subsurface geological features with steeply dipping beds are better defined, with artifacts from the conflicting dips removed.

scholarly journals Detection of cellular micromotion by advanced signal processing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephan Rinner ◽  
Alberto Trentino ◽  
Heike Url ◽  
Florian Burger ◽  
Julian von Lautz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Time Domain ◽  
Neural Activity ◽  
Matched Filtering ◽  
Label Free ◽  
Spectral Filtering ◽  
Video Recordings ◽  
Time Domain Data ◽  
Cellular Signal

AbstractCellular micromotion—a tiny movement of cell membranes on the nm-µm scale—has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity. Here we harness several recent approaches of signal processing to detect such micromotion in video recordings of unlabeled cells. Our survey includes spectral filtering of the video signal, matched filtering, as well as 1D and 3D convolutional neural networks acting on pixel-wise time-domain data and a whole recording respectively.

scholarly journals Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

2019 ◽  
Vol 125 ◽  
pp. 15006
Author(s):  
Taufik Mawardi Sinaga ◽  
M. Syamsu Rosid ◽  
M. Wahdanadi Haidar
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Frequency ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
Neural Network Method ◽  
Time Migration ◽  
The Neural Network ◽  
Network Method

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

scholarly journals Low power laser generated ultrasound: Signal processing for time domain data acquisition

2011 ◽  
Vol 278 ◽  
pp. 012036
Author(s):  
A Cleary ◽  
I Veres ◽  
G Thursby ◽  
C McKee ◽  
I Armstrong ◽  
...  
Keyword(s):  
Signal Processing ◽  
Low Power ◽  
Data Acquisition ◽  
Time Domain ◽  
Power Laser ◽  
Time Domain Data ◽  
Ultrasound Signal

Site characterization with 3D elastic full-waveform tomography

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. R389-R400 ◽  
Author(s):  
Trung Dung Nguyen ◽  
Khiem T. Tran
Keyword(s):  
Field Data ◽  
Wave Equations ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Seismic Inversion ◽  
Waveform Analysis ◽  
Staggered Grid ◽  
Model Parameters ◽  
Optimization Approach ◽  
Full Waveform

We have developed a 3D elastic full-waveform inversion (FWI) method for geotechnical site characterization. The method is based on a solution of 3D elastic-wave equations for forward modeling to simulate wave propagation and a local optimization approach based on the adjoint-state method to update the model parameters. The staggered-grid finite-difference technique is used to solve the wave equations together with implementation of the perfectly matched layer condition for boundary truncation. Seismic wavefields are acquired from geophysical testing using sensors and sources located in uniform 2D grids on the ground surface, and they are then inverted for the extraction of 3D subsurface wave velocity structures. The capability of the presented FWI method is tested on synthetic and field data sets. The inversion results from synthetic data indicate the ability of characterizing laterally variable low- and high-velocity layers. Field experimental data were collected using 96 receivers and a propelled energy generator to induce seismic wave energy. The field data result indicates that the waveform analysis was able to delineate variable subsurface soil layers. The seismic inversion results are generally consistent with invasive standard penetration test [Formula: see text]-values, including identification of a low-velocity zone.

Sign in / Sign up

Export Citation Format

Share Document