Joint time/frequency-domain inversion of reflection data for seabed geoacoustic profiles and uncertainties

2008 ◽  
Vol 123 (3) ◽  
pp. 1306-1317 ◽  
Author(s):  
Jan Dettmer ◽  
Stan E. Dosso ◽  
Charles W. Holland
Keyword(s):  
Frequency Domain ◽  
Time Frequency ◽  
Reflection Data ◽  
Domain Inversion

Estimating local slope in the time-frequency domain: Velocity-independent seismic imaging in the near surface

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. U99-U107
Author(s):  
Matthew P. Griffiths ◽  
André J.-M. Pugin ◽  
Dariush Motazedian
Keyword(s):  
Frequency Domain ◽  
Surface Shear ◽  
Near Surface ◽  
Local Slope ◽  
Surface Reflection ◽  
Time Frequency ◽  
Reflection Data ◽  
Dynamic Time ◽  
Geophone Spacing ◽  
Multicomponent Data

Seismic reflection processing for multicomponent data is very time consuming. To automatically streamline and shorten this process, a new approach for estimating the local event slope (local static shift) in the time-frequency domain is proposed and tested. The seismic event slope is determined by comparing the local phase content of Stockwell transformed signals. This calculation allows for noninterfering arrivals to be aligned by iteratively correcting trace by trace. Alternatively, the calculation can be used in a velocity-independent imaging framework with the possibility of exporting the determined time and velocities for each common midpoint gather, which leads to a more robust moveout correction. Synthetic models are used to test the robustness of the calculation and compare it directly to an existing method of local slope estimation. Compared to dynamic time warping, our method is more robust to noise but less robust to large time shifts, which limits our method to shorter geophone spacing. We apply the calculation to near-surface shear-wave data and compare it directly to semblance/normal-moveout processing. Examples demonstrate that the calculation yields an accurate local slope estimate and can produce sections of better or equal quality to sections processed using the conventional approach with much less user time input. It also serves as a first example of velocity-independent processing applied to near-surface reflection data.

scholarly journals Fracture detection from Azimuth-dependent seismic inversion in joint time–frequency domain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinpeng Pan ◽  
Dazhou Zhang ◽  
Pengfei Zhang
Keyword(s):  
Frequency Domain ◽  
Fractured Reservoirs ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Frequency Bandwidth ◽  
Fracture Detection ◽  
Time Frequency ◽  
Free Data ◽  
Domain Inversion

AbstractDetection of fracture properties can be implemented using azimuth-dependent seismic inversion for optimal model parameters in time or frequency domain. Considering the respective potentials for sensitivities of inversion resolution and anti-noise performance in time and frequency domain, we propose a more robust azimuth-dependent seismic inversion method to achieve fracture detection by combining the Bayesian inference and joint time–frequency-domain inversion theory. Both Cauchy Sparse and low-frequency constraint regularizations are introduced to reduce multi-solvability of model space and improve inversion reliability of model parameters. Synthetic data examples demonstrate that the frequency bandwidth of inversion result is almost the same for time, frequency and joint time–frequency domain inversion in seismic dominant frequency band using the noise-free data, but the frequency bandwidth in joint time–frequency domain is larger than that in time and frequency domains using low- signal-to-noise-ratio (SNR) data. The results of cross-correlation coefficients validate that the joint time–frequency-domain inversion retains both the excellent characteristics of high resolution in frequency-domain inversion and the advantage of strong anti-noise ability in time-domain inversion. A field data example further demonstrates that our proposed inversion approach in joint time–frequency domain may provide a more stable technique for fracture detection in fractured reservoirs.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

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