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.

The diminishing residual matrices method for surface-consistent statics — A review

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V257-V274
Author(s):  
Necati Gülünay
Keyword(s):  
Synthetic Data ◽  
Reconstruction Technique ◽  
Data Set ◽  
Arrival Times ◽  
Long Wavelength ◽  
Iterative Reconstruction Technique ◽  
Land Survey ◽  
First Arrival ◽  
Waveform Distortions ◽  
The Individual

The diminishing residual matrices (DRM) method can be used to surface-consistently decompose individual trace statics into source and receiver components. The statics to be decomposed may either be first-arrival times after the application of linear moveout associated with a consistent refractor as used in refraction statics or residual statics obtained by crosscorrelating individual traces with corresponding model traces (known as pilot traces) at the same common-midpoint (CMP) location. The DRM method is an iterative process like the well-known Gauss-Seidel (GS) method, but it uses only source and receiver terms. The DRM method differs from the GS method in that half of the average common shot and receiver terms are subtracted simultaneously from the observations at each iteration. DRM makes the under-constrained statics problem a constrained one by implicitly adding a new constraint, the equality of the contribution of shots and receivers to the solution. The average of the shot statics and the average of the receiver statics are equal in the DRM solution. The solution has the smallest difference between shot and receiver statics profiles when the number of shots and the number of receivers in the data are equal. In this case, it is also the smallest norm solution. The DRM method can be derived from the well-known simultaneous iterative reconstruction technique. Simple numerical tests as well as results obtained with a synthetic data set containing only the field statics verify that the DRM solution is the same as the linear inverse theory solution. Both algorithms can solve for the long-wavelength component of the statics if the individual picks contain them. Yet DRM method is much faster. Application of the method to the normal moveout-corrected CMP gathers on a 3D land survey for residual statics calculation found that pick-decompose-apply-stack stages of the DRM method need to be iterated. These iterations are needed because of time and waveform distortions of the pilot traces due to the individual trace statics. The distortions lessen at every external DRM iteration.

First-arrival picking with a U-net convolutional network

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. U45-U57 ◽  
Author(s):  
Lianlian Hu ◽  
Xiaodong Zheng ◽  
Yanting Duan ◽  
Xinfei Yan ◽  
Ying Hu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Synthetic Data ◽  
Training Data ◽  
Convolutional Network ◽  
Data Set ◽  
Waveform Data ◽  
Near Surface ◽  
Arrival Times ◽  
Seismic Waveform ◽  
First Arrival

In exploration geophysics, the first arrivals on data acquired under complicated near-surface conditions are often characterized by significant static corrections, weak energy, low signal-to-noise ratio, and dramatic phase change, and they are difficult to pick accurately with traditional automatic procedures. We have approached this problem by using a U-shaped fully convolutional network (U-net) to first-arrival picking, which is formulated as a binary segmentation problem. U-net has the ability to recognize inherent patterns of the first arrivals by combining attributes of arrivals in space and time on data of varying quality. An effective workflow based on U-net is presented for fast and accurate picking. A set of seismic waveform data and their corresponding first-arrival times are used to train the network in a supervised learning approach, then the trained model is used to detect the first arrivals for other seismic data. Our method is applied on one synthetic data set and three field data sets of low quality to identify the first arrivals. Results indicate that U-net only needs a few annotated samples for learning and is able to efficiently detect first-arrival times with high precision on complicated seismic data from a large survey. With the increasing training data of various first arrivals, a trained U-net has the potential to directly identify the first arrivals on new seismic data.

Effect of antennas on velocity estimates obtained from crosshole GPR data

Geophysics ◽  
2005 ◽  
Vol 70 (5) ◽  
pp. K39-K42 ◽  
Author(s):  
James D. Irving ◽  
Rosemary J. Knight
Keyword(s):  
Numerical Modeling ◽  
Ground Penetrating Radar ◽  
Significant Fraction ◽  
High Angle ◽  
Standard Assumption ◽  
Tomographic Images ◽  
Wide Range ◽  
Antenna Length ◽  
Ground Penetrating

To obtain tomographic images with the highest possible resolution from crosshole ground-penetrating radar (GPR) data, raypaths covering a wide range of angles between the boreholes are required. In practice, however, the inclusion of high-angle ray data in crosshole GPR inversions often leads to tomograms so dominated by inversion artifacts that they contain little reliable subsurface information. Here, we investigate the problems that arise from the standard assumption that all first-arriving energy travels directly between the centers of the antennas. Through numerical modeling, we show that this assumption is often incorrect at high transmitter-receiver angles and can lead to significant errors in tomographic velocity estimates when the antenna length is a significant fraction of the borehole spacing.

scholarly journals Crustal velocity structure of the Apennines (Italy) from P-wave travel time tomography

1996 ◽  
Vol 39 (6) ◽  
Author(s):  
C. Chiarabba ◽  
A. Amato
Keyword(s):  
Velocity Structure ◽  
P Wave ◽  
Depth Interval ◽  
Low Velocity ◽  
Data Set ◽  
Arrival Times ◽  
Velocity Anomaly ◽  
Minimum Number ◽  
Velocity Images ◽  
Lower Crustal

In this paper we provide P-wave velocity images of the crust underneath the Apennines (Italy), focusing on the lower crustal structure and the Moho topography. We inverted P-wave arrival times of earthquakes which occurred from 1986 to 1993 within the Apenninic area. To overcome inversion instabilities due to noisy data (we used bulletin data) we decided to resolve a minimum number of velocity parameters, inverting for only two layers in the crust and one in the uppermost mantle underneath the Moho. A partial inversion of only 55% of the overall dataset yields velocity images similar to those obtained with the whole data set, indicating that the depicted tomograms are stable and fairly insensitive to the number of data used. We find a low-velocity anomaly in the lower crust extending underneath the whole Apenninic belt. This feature is segmented by a relative high-velocity zone in correspondence with the Ortona-Roccamonfina line, that separates the northern from the southern Apenninic arcs. The Moho has a variable depth in the study area, and is deeper (more than 37 km) in the Adriatic side of the Northern Apennines with respect to the Tyrrhenian side, where it is found in the depth interval 22-34 km.

scholarly journals Modeling across-trial variability in the Wald drift rate parameter

2020 ◽  
Author(s):  
Helen Steingroever ◽  
Dominik Wabersich ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Drift Rate ◽  
Model Parameters ◽  
Analysis Tool ◽  
Rate Parameter ◽  
Data Set ◽  
Arrival Times ◽  
First Arrival ◽  
Truncated Normal ◽  
Experimental Trials ◽  
Variance Parameter

Abstract The shifted-Wald model is a popular analysis tool for one-choice reaction-time tasks. In its simplest version, the shifted-Wald model assumes a constant trial-independent drift rate parameter. However, the presence of endogenous processes—fluctuation in attention and motivation, fatigue and boredom—suggest that drift rate might vary across experimental trials. Here we show how across-trial variability in drift rate can be accounted for by assuming a trial-specific drift rate parameter that is governed by a positive-valued distribution. We consider two candidate distributions: the truncated normal distribution and the gamma distribution. For the resulting distributions of first-arrival times, we derive analytical and sampling-based solutions, and implement the models in a Bayesian framework. Recovery studies and an application to a data set comprised of 1469 participants suggest that (1) both mixture distributions yield similar results; (2) all model parameters can be recovered accurately except for the drift variance parameter; (3) despite poor recovery, the presence of the drift variance parameter facilitates accurate recovery of the remaining parameters; (4) shift, threshold, and drift mean parameters are correlated.

Automatic Picking of Teleseismic P- and S-Phases using an Autoregressive Prediction Approach

2020 ◽  
Author(s):  
Johannes Stampa ◽  
Máté Timkó ◽  
Marcel Tesch ◽  
Thomas Meier
Keyword(s):  
Characteristic Function ◽  
Time Window ◽  
Large Data ◽  
Information Criterion ◽  
Recent Decade ◽  
Moving Window ◽  
Data Set ◽  
Arrival Times ◽  
Automatic Picking ◽  
Prediction Approach

<div> <div> <div> <p>In the recent decade, the amount of available seismological broadband data has increased steeply. Picking later arriving phases such as S-phases is difficult, and there are few manual picks available for these phases. Data sets of manual picks can also be problematic, since phase arrival picks are sensitive to the parameters of the filtering, which are often unknown, and the individual picking behavior of the analysts. This neccesitates the adoption of automatic techniques for determining teleseismic phase arrival times consistently over a large data set.</p> <p>In this work, a robust automatic picking algorithm based on autoregressive prediction in a moving window is explained. In this algorithm, a characteristic function is calculated as the autoregressive prediction error in a moving window. This characteristic function is then transformed with the Akaike-Information Criterion to obtain the phase arrival time estimate. This estimate is further improved in a second iteration of a similiar scheme in a smaller time window.</p> <p>The algorithm is applied to a global data set including AlpArray stations, covering a time period from 1995 to present, to obtain arrival times for teleseis- mic P- and S-phases. Residuals to theoretical travel times and to local averages are shown. Different methods for automatically evaluating the quality of indi- vidual picks are used, based on signal to noise ratio of the seismic trace and impulsiveness of the arrival. The picking errors are estimated by comparision with manual picks and neighboring stations as well as statistical methods. The quality evaluations suggest potential of using these automatically determined phase arrival times for a travel time tomography.</p> </div> </div> </div>

Continuous and simultaneous measurement of reflector depth and average soil-water content with multichannel ground-penetrating radar

Geophysics ◽  
2008 ◽  
Vol 73 (4) ◽  
pp. J15-J23 ◽  
Author(s):  
Holger Gerhards ◽  
Ute Wollschläger ◽  
Qihao Yu ◽  
Philip Schiwek ◽  
Xicai Pan ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ground Penetrating Radar ◽  
Synthetic Data ◽  
Evaluation Procedure ◽  
Noninvasive Technique ◽  
Data Set ◽  
Large Variability ◽  
Ground Penetrating

Ground-penetrating radar is a fast noninvasive technique that can monitor subsurface structure and water-content distribution. To interpret traveltime information from single common-offset measurements, additional assumptions, such as constant permittivity, usually are required. We present a fast ground-penetrating-radar measurement technique using a multiple transmitter-and-receiver setup to measure simultaneously the reflector depth and average soil-water content. It can be considered a moving minicommon-midpoint measurement. For a simple analysis, we use a straightforward evaluation procedure that includes two traveltimes to the same reflector, obtained from different antenna separations. For a more accurate approach, an inverse evaluation procedure is added, using traveltimes obtained from all antenna separations at one position and its neighboring measurement locations. The evaluation of a synthetic data set with a lateral variability in reflector depth and an experimental example with a large variability in soil-water content are introduced to demonstrate the applicability for field-scale measurements. The crucial point for this application is the access to absolute traveltimes, which are difficult to determine accurately from common-offset measurements.

First-arrival traveltime tomography based on the adjoint-state method

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCB1-WCB10 ◽  
Author(s):  
Cédric Taillandier ◽  
Mark Noble ◽  
Hervé Chauris ◽  
Henri Calandra
Keyword(s):  
Synthetic Data ◽  
Point Of View ◽  
Data Set ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Adjoint State ◽  
Gradient Based ◽  
First Arrival ◽  
State Method ◽  
Adjoint State Method

Classical algorithms used for traveltime tomography are not necessarily well suited for handling very large seismic data sets or for taking advantage of current supercomputers. The classical approach of first-arrival traveltime tomography was revisited with the proposal of a simple gradient-based approach that avoids ray tracing and estimation of the Fréchet derivative matrix. The key point becomes the derivation of the gradient of the misfit function obtained by the adjoint-state technique. The adjoint-state method is very attractive from a numerical point of view because the associated cost is equivalent to the solution of the forward-modeling problem, whatever the size of the input data and the number of unknown velocity parameters. An application on a 2D synthetic data set demonstrated the ability of the algorithm to image near-surface velocities with strong vertical and lateral variations and revealed the potential of the method.

Direct P-Wave Travel Time Tomography of the Middle East

2021 ◽  
Author(s):  
Susini Desilva ◽  
Ebru Bozdag ◽  
Guust Nolet ◽  
Rengin Gok ◽  
Ahmed Ali ◽  
...  
Keyword(s):  
Middle East ◽  
Reliable Data ◽  
P Wave ◽  
Good Recovery ◽  
Data Set ◽  
Iranian Plateau ◽  
Arrival Times ◽  
Caucasus Region ◽  
First Arrival ◽  
Tomographic Inversions

<p>High-resolution seismic images of the crust and mantle beneath regions of complex surface geological structures are necessary to gain insights on the underlying geodynamical processes. One such region embodying various plate boundary motions and intraplate deformations is the Middle East, and consequently the region is prone to significant seismic activity. Hence a tomographic investigation using a more recent and reliable data set is vital in understanding the ongoing complicated deformation process driven by the African, Arabian and Eurasian plates. The purpose of our study is to retrieve a detailed  model of the crust and mantle beneath the Middle Eastern region using teleseismic P arrival times from the ISC-EHB bulletin (Engdahl et al., 1998).</p><p>Starting with AK135 as the reference model we invert for tomographic models of compressional wavespeed perturbations down to lower mantle depths in an area bounded by longitudes 22E–66E and latitudes 8N–48N.  The data set used in this study consists of regionally observed P-phase arrival times from over 1000 global events from 1996–2016 culminating in a larger dataset than other similar studies. Selection of a reliable data, ray tracing, preconditioning and inversion steps are carried out using the BD-soft software suite (https://www.geoazur.fr/GLOBALSEIS/Soft.html).</p><p>Preliminary inversion results are consistent with the previous regional tomographic studies. In checkerboard tests, cell sizes as low as ∼ 2.8° × 2.8° ( ∼ 240 × 240 km at surface) are generally well recovered down to a 1000 km depth beneath the Anatolian plateau where we currently have the densest coverage. Additionally the Caucasus region and northern parts of the Iranian plateau shows good recovery of ±4% Vp perturbation amplitudes at depths ∼ 70 – 135 km. There is fair recovery for a minimum cell size of ∼ 2.8° × 2.8° beneath the Iranian Plateau, Zagros mountain region, Persian gulf, and northeast Iraq, along with quite good recovery of cell amplitudes towards the Anatolian-Caucasus region at depth ranges 380 – 430 km, 650 – 700 km, and around 950 km. Tomographic inversions unveil a low P velocity zone stretching from the Afar region to Sinai Peninsula consistent with S wave velocity observations of a similar feature by Chang and van der Lee 2011.</p><p>We are able to further improve coverage especially down to lithospheric depths within the Arabian peninsula using first arrival times measured from waveform data collected from regional networks. Addition of first arrival time delays from waveforms highlights a prominent low velocity in the tomographic inversions beneath the volcanic fields of western Saudi Arabia. Our ultimate goal is to perform full-waveform inversion of the region constrained by the constructed P-wave model.</p>

scholarly journals Interferometric identification of surface-related multiples

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. Q41-Q52 ◽  
Author(s):  
Boris Boullenger ◽  
Deyan Draganov
Keyword(s):  
Synthetic Data ◽  
Original Data ◽  
Data Set ◽  
Interferometric Method ◽  
Feedback Information ◽  
Arrival Times ◽  
Surface Reflection ◽  
Reflection Data ◽  
Multiple Prediction ◽  
Virtual Events

The theory of seismic interferometry predicts that crosscorrelations of recorded seismic responses at two receivers yield an estimate of the interreceiver seismic response. The interferometric process applied to surface-reflection data involves the summation, over sources, of crosscorrelated traces, and it allows retrieval of an estimate of the interreceiver reflection response. In particular, the crosscorrelations of the data with surface-related multiples in the data produce the retrieval of pseudophysical reflections (virtual events with the same kinematics as physical reflections in the original data). Thus, retrieved pseudophysical reflections can provide feedback information about the surface multiples. From this perspective, we have developed a data-driven interferometric method to detect and predict the arrival times of surface-related multiples in recorded reflection data using the retrieval of virtual data as diagnosis. The identification of the surface multiples is based on the estimation of source positions in the stationary-phase regions of the retrieved pseudophysical reflections, thus not necessarily requiring sources and receivers on the same grid. We have evaluated the method of interferometric identification with a two-layer acoustic example and tested it on a more complex synthetic data set. The results determined that we are able to identify the prominent surface multiples in a large range of the reflection data. Although missing near offsets proved to cause major problems in multiple-prediction schemes based on convolutions and inversions, missing near offsets does not impede our method from identifying surface multiples. Such interferometric diagnosis could be used to control the effectiveness of conventional multiple-removal schemes, such as adaptive subtraction of multiples predicted by convolution of the data.

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