Automatic first-breaks picking: New strategies and algorithms

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. V67-V76 ◽  
Author(s):  
Juan I. Sabbione ◽  
Danilo Velis
Keyword(s):  
Fractal Dimension ◽  
Correlated Noise ◽  
Computationally Efficient ◽  
Precise Location ◽  
Edge Preserving ◽  
Automatic Picking ◽  
Rapid Changes ◽  
First Arrival ◽  
Noisy Conditions ◽  
New Strategies

We have developed three methods for the automatic picking of first breaks that can be used for marine, dynamite, or vibroseis shot records: a modified Coppens’s method, an entropy-based method, and a variogram fractal-dimension method. The techniques are based on the fact that the transition between noise and noise plus signal can be automatically identified by detecting rapid changes in a certain attribute (energy ratio, entropy, or fractal dimension), which we calculate within moving windows along the seismic trace. The application of appropriate edge-preserving smoothing operators to enhance these transitions allowed us to develop an automated strategy that can be used to easily signal the precise location of the first-arrival onset. Furthermore, we propose a mispick-correcting technique to exploit the benefits of the data present in the entire shot record, which allows us to adjust the trace-by-trace picks and to discard picks associated with bad or dead traces. As a result, the consistency of the first-break picks is significantly improved. The methods are robust under noisy conditions, computationally efficient, and easy to apply. Results using dynamite and vibroseis field data show that accurate and consistent picks can be obtained in an automated manner even under the presence of correlated noise, bad traces, pulse changes, and indistinct first breaks.

A fractal‐based algorithm for detecting first arrivals on seismic traces

Geophysics ◽  
1996 ◽  
Vol 61 (4) ◽  
pp. 1095-1102 ◽  
Author(s):  
Fabio Boschetti ◽  
Mike D. Dentith ◽  
Ron D. List
Keyword(s):  
Fractal Dimension ◽  
Real Data ◽  
Signal Amplitude ◽  
The Other ◽  
Data Sets ◽  
Detectable Change ◽  
Fractal Method ◽  
Divider Method ◽  
Automatic Picking ◽  
First Arrival

A new algorithm is proposed for the automatic picking of seismic first arrivals that detects the presence of a signal by analyzing the variation in fractal dimension along the trace. The “divider‐method” is found to be the most suitable method for calculating the fractal dimension. A change in dimension is found to occur close to the transition from noise to signal plus noise, that is the first arrival. The nature of this change varies from trace to trace, but a detectable change is always found to occur. The algorithm has been tested on real data sets with varying S/N ratios and the results compared to those obtained using previously published algorithms. With an appropriate tuning of its parameters, the fractal‐based algorithm proved more accurate than all these other algorithms, especially in the presence of significant noise. The fractal method proved able to tolerate noise up to 80% of the average signal amplitude. However, the fractal‐based algorithm is considerably slower than the other methods and hence is intended for use only on data sets with low S/N ratios.

scholarly journals Fusion of Multi-Baseline and Multi-Orbit InSAR DEMs with Terrain Feature-Guided Filter

2018 ◽  
Vol 10 (10) ◽  
pp. 1511 ◽  
Author(s):  
Yuting Dong ◽  
Baobao Liu ◽  
Lu Zhang ◽  
Mingsheng Liao ◽  
Ji Zhao
Keyword(s):  
Linear Time ◽  
Weighted Average ◽  
Guided Filter ◽  
Baseline Length ◽  
Fusion Method ◽  
Computationally Efficient ◽  
Measurement Sensitivity ◽  
Height Measurement ◽  
Edge Preserving ◽  
Local Edge

Interferometric synthetic aperture radar (InSAR) is an effective technology for generating high-precision digital elevation models (DEMs). However, the vertical accuracy of InSAR DEMs is limited by the contradiction between height measurement sensitivity and phase unwrapping reliability in terms of normal baseline length as well as data voids caused by layover or shadow effects. In order to alleviate these two unfavorable factors, in this study, a novel InSAR DEM fusion method with guided filter is developed and assessed with multiple bistatic TanDEM-X InSAR data pairs of different normal baselines acquired from different orbits. Unlike the widely used fusion method based on pixel-by-pixel weighted average, the guided-filter-based method incorporates local spatial context information into the fusion and can thus effectively alleviate the noise effect and automatically fill in data voids. As a result of the local edge-preserving capability of the guided filter, the proposed fusion method can preserve terrain details by maintaining gradient consistency and introducing terrain features as guidance image. Furthermore, the proposed fusion method is computationally efficient owing to the linear time complexity of guided filter. The experimental results show that the fused DEM with guided filter can depict terrain details well and smooth the “salt-and-pepper” noise and fill in almost all of the data voids. The root mean square error (RMSE) of the fused InSAR DEM with guided filter is lower than those of the weighted average fused InSAR DEM and the TanDEM-X DEM released by the German Aerospace Center (DLR), thus validating the effectiveness of the fusion method proposed in this study.

scholarly journals Combined Reinforcement Learning via Abstract Representations

2019 ◽  
Vol 33 ◽  
pp. 3582-3589 ◽  
Author(s):  
Vincent Francois-Lavet ◽  
Yoshua Bengio ◽  
Doina Precup ◽  
Joelle Pineau
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Transfer Learning ◽  
Computationally Efficient ◽  
Dimensional Representation ◽  
Learning Methods ◽  
Model Free ◽  
Abstract Representations ◽  
Low Dimensional ◽  
New Strategies

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.

Improving crosshole radar velocity tomograms: A new approach to incorporating high-angle traveltime data

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. J31-J41 ◽  
Author(s):  
James D. Irving ◽  
Michael D. Knoll ◽  
Rosemary J. Knight
Keyword(s):  
Synthetic Data ◽  
High Angle ◽  
Data Set ◽  
Tomographic Images ◽  
Arrival Times ◽  
Angle Data ◽  
Automatic Picking ◽  
Velocity Images ◽  
First Arrival ◽  
Ground Penetrating

To obtain the highest-resolution ray-based tomographic images from crosshole ground-penetrating radar (GPR) data, wide angular ray coverage of the region between the two boreholes is required. Unfortunately, at borehole spacings on the order of a few meters, high-angle traveltime data (i.e., traveltime data corresponding to transmitter-receiver angles greater than approximately 50° from the horizontal) are notoriously difficult to incorporate into crosshole GPR inversions. This is because (1) low signal-to-noise ratios make the accurate picking of first-arrival times at high angles extremely difficult, and (2) significant tomographic artifacts commonly appear when high- and low-angle ray data are inverted together. We address and overcome thesetwo issues for a crosshole GPR data example collected at the Boise Hydrogeophysical Research Site (BHRS). To estimate first-arrival times on noisy, high-angle gathers, we develop a robust and automatic picking strategy based on crosscorrelations, where reference waveforms are determined from the data through the stacking of common-ray-angle gathers. To overcome incompatibility issues between high- and low-angle data, we modify the standard tomographic inversion strategy to estimate, in addition to subsurface velocities, parameters that describe a traveltime ‘correction curve’ as a function of angle. Application of our modified inversion strategy, to both synthetic data and the BHRS data set, shows that it allows the successful incorporation of all available traveltime data to obtain significantly improved subsurface velocity images.

3-D interpretation of reflected arrival times by finite‐difference techniques

Geophysics ◽  
1994 ◽  
Vol 59 (5) ◽  
pp. 844-849 ◽  
Author(s):  
M. Ali Riahi ◽  
Christopher Juhlin
Keyword(s):  
Finite Difference ◽  
Finite Difference Methods ◽  
Real Data ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Computationally Efficient ◽  
Reflected Waves ◽  
Arrival Times ◽  
First Arrival ◽  
Difference Methods

Finite‐difference methods have generally been used to solve dynamic wave propagation problems over the last 25 years (Alterman and Karal, 1968; Boore, 1972; Kelly et al., 1976; and Levander, 1988). Recently, finite‐difference methods have been applied to the eikonal equation to calculate the kinematic solution to the wave equation (Vidale, 1988 and 1990; Podvin and Lecomte, 1991; Van Trier and Symes, 1991; Qin et al., 1992). The calculation of the first‐arrival times using this method has proven to be considerably faster than using classical ray tracing, and problems such as shadow zones, multipathing, and barrier penetration are easily handled. Podvin and Lecomte (1991) and Matsuoka and Ezaka (1992) extended and expanded upon Vidale’s (1988) algorithm to calculate traveltimes for reflected waves in two dimensions. Based on finite‐difference calculations for first‐arrival times, Hole et al. (1992) devised a scheme for inverting synthetic and real data to estimate the depth to refractors in the crust in three dimensions. The method of Hole et al. (1992) for inversion is computationally efficient since it avoids the matrix inversion of many of the published schemes for refraction and reflection traveltime data (Gjøystdal and Ursin, 1981).

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