Volterra series analysis and synthesis of a neural network for velocity estimation

1999 ◽  
Vol 29 (2) ◽  
pp. 190-197 ◽  
Author(s):  
W.S. Gray ◽  
B. Nabet
Keyword(s):  
Neural Network ◽  
Volterra Series ◽  
Velocity Estimation ◽  
Series Analysis ◽  
Analysis And Synthesis

Pre-treating GNSS time series using a recurrent neural network to improve the automated detection of jump discontinuities

2021 ◽  
Author(s):  
Luca Tavasci ◽  
Pasquale Cascarano ◽  
Stefano Gandolfi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Ad Hoc ◽  
Short Term Memory ◽  
Learning Approaches ◽  
Jump Detection ◽  
Series Analysis ◽  
Jump Discontinuities ◽  
Gnss Time Series

<p>Ground motion monitoring is one of the main goals in the geoscientist community and at the time it is mainly performed by analyzing time series of data. Our capability of describing the most significant features characterizing the time evolution of a point-position is affected by the presence of undetected discontinuities in the time series. One of the most critical aspects in the automated time series analysis, which is quite necessary since the amount of data is increasing more and more, is still the detection of discontinuities and in particular the definition of their epoch. A number of algorithms have already been developed and proposed to the community in the last years, following different statistical approaches and different hypotheses on the coordinates behavior. In this work, we have chosen to analyze GNSS time series and to use an already published algorithm (STARS) for jump detection as a benchmark to test our approach, consisting of pre-treating the time series to be analyzed using a neural network. In particular, we chose a Long Short Term Memory (LSTM) neural network belonging to the class of the Recurrent Neural Networks (RNNs), ad hoc modified for the GNSS time series analysis. We focused both on the training algorithm and the testing one. The latter has been the object of a parametric test to find out the number of predicted data that mostly emphasize our capability of detecting jump discontinuities. Results will be presented considering several GNSS time series of daily positions. Finally, a discussion on the possible integration of machine learning approaches and classical deterministic approaches will be done.</p>

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

scholarly journals Interface-targeted seismic velocity estimation using machine learning

2019 ◽  
Vol 218 (1) ◽  
pp. 45-56 ◽  
Author(s):  
C Nur Schuba ◽  
Jonathan P Schuba ◽  
Gary G Gray ◽  
Richard G Davy
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Dimensional Space ◽  
Seismic Profile ◽  
Velocity Estimation ◽  
Seismic Survey ◽  
Wide Angle ◽  
Seismic Velocities ◽  
Lower Dimensional Space ◽  
Lower Dimensional

SUMMARY We present a new approach to estimate 3-D seismic velocities along a target interface. This approach uses an artificial neural network trained with user-supplied geological and geophysical input features derived from both a 3-D seismic reflection volume and a 2-D wide-angle seismic profile that were acquired from the Galicia margin, offshore Spain. The S-reflector detachment fault was selected as the interface of interest. The neural network in the form of a multilayer perceptron was employed with an autoencoder and a regression layer. The autoencoder was trained using a set of input features from the 3-D reflection volume. This set of features included the reflection amplitude and instantaneous frequency at the interface of interest, time-thicknesses of overlying major layers and ratios of major layer time-thicknesses to the total time-depth of the interface. The regression model was trained to estimate the seismic velocities of the crystalline basement and mantle from these features. The ‘true’ velocities were obtained from an independent full-waveform inversion along a 2-D wide-angle seismic profile, contained within the 3-D data set. The autoencoder compressed the vector of inputs into a lower dimensional space, then the regression layer was trained in the lower dimensional space to estimate velocities above and below the targeted interface. This model was trained on 50 networks with different initializations. A total of 37 networks reached minimum achievable error of 2 per cent. The low standard deviation (<300  m s−1) between different networks and low errors on velocity estimations demonstrate that the input features were sufficient to capture variations in the velocity above and below the targeted S-reflector. This regression model was then applied to the 3-D reflection volume where velocities were predicted over an area of ∼400 km2. This approach provides an alternative way to obtain velocities across a 3-D seismic survey from a deep non-reflective lithology (e.g. upper mantle) , where conventional reflection velocity estimations can be unreliable.

Sign in / Sign up

Export Citation Format

Share Document