scholarly journals Blind Test of Methods for Obtaining 2-D Near-Surface Seismic Velocity Models from First-Arrival Traveltimes

2013 ◽  
Vol 18 (3) ◽  
pp. 183-194 ◽  
Author(s):  
C. A. Zelt ◽  
S. Haines ◽  
M. H. Powers ◽  
J. Sheehan ◽  
S. Rohdewald ◽  
...  
Keyword(s):  
Seismic Velocity ◽  
Blind Test ◽  
Near Surface ◽  
Velocity Models ◽  
First Arrival

Improved interpretation of SAGEEP 2011 blind refraction data using Frequency-Dependent Traveltime Tomography

2021 ◽  
Author(s):  
Siegfried Rohdewald
Keyword(s):  
Current Velocity ◽  
Seismic Refraction ◽  
Blind Test ◽  
Near Surface ◽  
Traveltime Inversion ◽  
Traveltime Tomography ◽  
Velocity Models ◽  
First Arrival ◽  
Starting Model ◽  
Refraction Data

<p>We demonstrate improved resolution in P-wave velocity tomograms obtained by inversion of the synthetic SAGEEP 2011 refraction traveltime data (Zelt 2010) using Wavepath-Eikonal Traveltime Inversion (WET; Schuster 1993) and Wavelength-Dependent Velocity Smoothing (WDVS; Zelt and Chen 2016). We use a multiscale inversion approach and a Conjugate-Gradient based search method. Our default starting model is a 1D-gradient model obtained directly from the traveltime first arrivals assuming diving waves (Sheehan, 2005). As a second approach, we map the first breaks to assumed refractors and obtain a layered starting model using the Plus-Minus refraction method (Hagedoorn, 1959). We compare tomograms obtained using WDVS to smooth the current velocity model grid before forward modeling traveltimes vs. tomograms obtained without WDVS. Results show that WET images velocity layer boundaries more sharply when engaging WDVS. We determine the optimum WDVS frequency iteratively by trial-and-error. We observe that the lower the used WDVS frequency, the stronger the imaged velocity contrast at the top-of-basement. Using a WDVS frequency that is too low makes WDVS based WET inversion unstable exhibiting increasing RMS error, too high modeled velocity contrast and too shallow imaged top-of-basement. To speed up WDVS, we regard each nth node only when scanning the velocity along straight scan lines radiating from the current velocity grid node. Scanned velocities are weighted with a Cosine-Squared function as described by (Zelt and Chen, 2016). We observe that activating WDVS allows decreasing WET regularization (smoothing and damping) to a higher degree than without WDVS.</p><p>References:</p><p><span>Hagedoorn, J.G., 1959, </span><span>The Plus-Minus method of interpreting seismic refraction sections, Geophysical Prospecting</span><span>, Volume 7, 158-182.</span></p><p><span>Rohdewald, S.R.C., 2021, SAGEEP11 data interpretation, https://rayfract.com/tutorials/sageep11_16.pdf.</span></p><p>Schuster, G.T., Quintus-Bosz, A., 1993, <span>Wavepath eikonal traveltime inversion: Theory</span>. Geophysics, Volume 58, 1314-1323.</p><p><span>Sheehan, J.R., Doll, W.E., Mandell, W., 2005, </span><span>An evaluation of methods and available software for seismic refraction tomography analysis</span><span>, JEEG, Volume 10(1), 21-34.</span></p><p>Shewchuk, J.R., 1994, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, <span>http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf</span><span>. </span></p><p>Zelt, C.A., 2010, Seismic refraction shootout: blind test of methods for obtaining velocity models from first-arrival travel times, <span>http://terra.rice.edu/department/faculty/zelt/sageep2011</span>.</p><p><span>Zelt, C.A., Haines, S., Powers, M.H. et al. 2013, </span><span>Blind Test of Methods for Obtaining 2-D Near-Surface Seismic Velocity Models from First-Arrival Traveltimes</span><span>, JEEG, Volume 18(3), 183-194. </span></p><p><span>Zelt, C.A., Chen, J., 2016, </span><span>Frequency-dependent traveltime tomography for near-surface seismic refraction data</span><span>, Geophys. J. Int., Volume 207, 72-88. </span></p>

scholarly journals Machine Learning Enabled Traveltime Inversion Based on the Horizontal Source Location Perturbation

Geophysics ◽  
2021 ◽  
pp. 1-40
Author(s):  
Isa Eren Yildirim ◽  
Tariq Alkhalifah ◽  
Ertugrul Umut Yildirim
Keyword(s):  
Linear Optimization ◽  
Deep Convolutional Neural Networks ◽  
Lateral Velocity ◽  
Suggested Approach ◽  
Near Surface ◽  
Arrival Times ◽  
Velocity Models ◽  
Uniqueness Of The Solution ◽  
First Arrival ◽  
High Level

Gradient based traveltime tomography, which aims to minimize the difference between modeled and observed first arrival times, is a highly non-linear optimization problem. Stabilization of this inverse problem often requires employing regularization. While regularization helps avoid local minima solutions, it might cause low resolution tomograms because of its inherent smoothing property. On the other hand, although conventional ray-based tomography can be robust in terms of the uniqueness of the solution, it suffers from the limitations inherent in ray tracing, which limits its use in complex media. To mitigate the aforementioned drawbacks of gradient and ray-based tomography, we approach the problem in a completely novel way leveraging data-driven inversion techniques based on training deep convolutional neural networks (DCNN). Since DCNN often face challenges in detecting high level features from the relatively smooth traveltime data, we use this type of network to map horizontal changes in observed first arrival traveltimes caused by a source shift to lateral velocity variations. The relationship between them is explained by a linearized eikonal equation. Construction of the velocity models from this predicted lateral variation requires information from, for example, a vertical well-log in the area. This vertical profile is then used to build a tomogram from the output of the network. Both synthetic and field data results verify that the suggested approach estimates the velocity models reliably. Because of the limited depth penetration of first arrival traveltimes, the method is particularly favorable for near-surface applications.

Distribution of Paleogene and Cretaceous rocks around the Nazko River belt of central British Columbia from 3-D long-offset first-arrival seismic tomography

2014 ◽  
Vol 51 (4) ◽  
pp. 358-372 ◽  
Author(s):  
Draga Talinga ◽  
Andrew J. Calvert
Keyword(s):  
British Columbia ◽  
Seismic Velocity ◽  
Volcanic Rocks ◽  
Sedimentary Rocks ◽  
Three Dimensional ◽  
Gravity Anomalies ◽  
Hydrocarbon Exploration ◽  
P Wave ◽  
Velocity Models ◽  
First Arrival

Across the Nechako–Chilcotin plateau of British Columbia, the distribution of Cretaceous sedimentary rocks, which are considered prospective for hydrocarbon exploration, is poorly known due to the surface cover of glacial deposits and Tertiary volcanic rocks. To constrain the subsurface distribution of these Cretaceous rocks, in 2008 Geoscience BC acquired seven long, up to 14.4 km, offset vibroseis seismic reflection lines across a north-northwest-trending belt of exhumed sedimentary rocks inferred to be part of the Taylor Creek Group. P-wave velocity models, which are consistent with sonic logs from nearby wells, have been estimated using three-dimensional first-arrival tomography to depths ranging from 1 to 4 km. Igneous basement can be identified on most lines using the 5.5 km/s isovelocity contour, which locates the top of the basement to an accuracy of ∼400 m where its depth is known in exploration wells. There is no general distinction on the basis of seismic velocity between Cretaceous sedimentary and Paleocene–Eocene volcanic–volcaniclastic rocks, both of which appear to be characterized in the tomographic models by velocities of 3.0–5.0 km/s. The geometry of the igneous basement inferred from the velocity models identifies north-trending basins and ridges, which correlate with exposed rocks of the Jurassic Hazelton Group. Identified Cretaceous sedimentary rocks occur beneath less negative Bouguer gravity anomalies, but the original distribution of these rocks has been disrupted by later Tertiary extension that created north-trending basins associated with the most negative gravity anomalies. We suggest that Cretaceous sedimentary rocks, if deposited, could be preserved within these basins if the rocks had not been eroded prior to Tertiary extension.

Tomographic velocity model building of the near surface with velocity-inversion interfaces: A test using the Yilmaz model

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. U39-U47 ◽  
Author(s):  
Hui Liu ◽  
Hua-wei Zhou ◽  
Wenge Liu ◽  
Peiming Li ◽  
Zhihui Zou
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Low Velocity ◽  
Near Surface ◽  
Velocity Inversion ◽  
Velocity Models ◽  
Velocity Model Building ◽  
First Arrival ◽  
The Impact

First-arrival traveltime tomography is a popular approach to building the near-surface velocity models for oil and gas exploration, mining, geoengineering, and environmental studies. However, the presence of velocity-inversion interfaces (VIIs), across which the overlying velocity is higher than the underlying velocity, might corrupt the tomographic solutions. This is because most first-arrival raypaths will not traverse along any VII, such as the top of a low-velocity zone. We have examined the impact of VIIs on first-arrival tomographic velocity model building of the near surface using a synthetic near-surface velocity model. This examination confirms the severe impact of VIIs on first-arrival tomography. When the source-to-receiver offset is greater than the lateral extent of the VIIs, good near-surface velocity models can still be established using a multiscale deformable-layer tomography (DLT), which uses a layer-based model parameterization and a multiscale scheme as regularization. Compared with the results from a commercial grid-based tomography, the DLT delivers much better near-surface statics solutions and less error in the images of deep reflectors.

First-arrival traveltime inversion of seismic diving waves observed on undulant surface

2021 ◽  
Vol 225 (2) ◽  
pp. 1020-1031
Author(s):  
Huachen Yang ◽  
Jianzhong Zhang ◽  
Kai Ren ◽  
Changbo Wang
Keyword(s):  
Turning Points ◽  
Analytical Formula ◽  
Velocity Model ◽  
Western China ◽  
Inversion Method ◽  
Mountain Area ◽  
Traveltime Inversion ◽  
Static Correction ◽  
Velocity Models ◽  
First Arrival

SUMMARY A non-iterative first-arrival traveltime inversion method (NFTI) is proposed for building smooth velocity models using seismic diving waves observed on irregular surface. The new ray and traveltime equations of diving waves propagating in smooth media with undulant observation surface are deduced. According to the proposed ray and traveltime equations, an analytical formula for determining the location of the diving-wave turning points is then derived. Taking the influence of rough topography on first-arrival traveltimes into account, the new equations for calculating the velocities at turning points are established. Based on these equations, a method is proposed to construct subsurface velocity models from the observation surface downward to the bottom using the first-arrival traveltimes in common offset gathers. Tests on smooth velocity models with rugged topography verify the validity of the established equations, and the superiority of the proposed NFTI. The limitation of the proposed method is shown by an abruptly-varying velocity model example. Finally, the NFTI is applied to solve the static correction problem of the field seismic data acquired in a mountain area in the western China. The results confirm the effectivity of the proposed NFTI.

scholarly journals Physics-Based Relationship for Pore Pressure and Vertical Stress Monitoring Using Seismic Velocity Variations

2021 ◽  
Vol 13 (14) ◽  
pp. 2684
Author(s):  
Eldert Fokker ◽  
Elmer Ruigrok ◽  
Rhys Hawkins ◽  
Jeannot Trampert
Keyword(s):  
Pore Pressure ◽  
Seismic Velocity ◽  
Pressure Head ◽  
Groundwater Table ◽  
Surface Velocity ◽  
Seismic Velocities ◽  
Stress Monitoring ◽  
Near Surface ◽  
Velocity Variations ◽  
Velocity Changes

Previous studies examining the relationship between the groundwater table and seismic velocities have been guided by empirical relationships only. Here, we develop a physics-based model relating fluctuations in groundwater table and pore pressure with seismic velocity variations through changes in effective stress. This model justifies the use of seismic velocity variations for monitoring of the pore pressure. Using a subset of the Groningen seismic network, near-surface velocity changes are estimated over a four-year period, using passive image interferometry. The same velocity changes are predicted by applying the newly derived theory to pressure-head recordings. It is demonstrated that the theory provides a close match of the observed seismic velocity changes.

The relationship between Vs, Vp, density and depth based on PS-logging data at K-NET and KiK-net sites

2021 ◽  
Author(s):  
Fumiaki Nagashima ◽  
Hiroshi Kawase
Keyword(s):  
Standard Deviation ◽  
Seismic Velocity ◽  
Saturated Soil ◽  
P Wave ◽  
Soil Types ◽  
Depth Range ◽  
Velocity Inversion ◽  
Velocity Models ◽  
Regression Curves ◽  
Ps Logging

Summary P-wave velocity (Vp) is an important parameter for constructing seismic velocity models of the subsurface structures by using microtremors and earthquake ground motions or any other geophysical exploration data. In order to reflect the ground survey information in Japan to the Vp structure, we investigated the relationships among Vs, Vp, and depth by using PS-logging data at all K-NET and KiK-net sites. Vp values are concentrated at around 500 m/s and 1,500 m/s when Vs is lower than 1,000 m/s, where these concentrated areas show two distinctive characteristics of unsaturated and saturated soil, respectively. Many Vp values in the layer shallower than 4 m are around 500 m/s, which suggests the dominance of unsaturated soil, while many Vp values in the layer deeper than 4 m are larger than 1,500 m/s, which suggests the dominance of saturated soil there. We also investigated those relationships for different soil types at K-NET sites. Although each soil type has its own depth range, all soil types show similar relationships among Vs, Vp, and depth. Then, considering the depth profile of Vp, we divided the dataset into two by the depth, which is shallower or deeper than 4 m, and calculated the geometrical mean of Vp and the geometrical standard deviation in every Vs bins of 200 m/s. Finally, we obtained the regression curves for the average and standard deviation of Vp estimated from Vs to get the Vp conversion functions from Vs, which can be applied to a wide Vs range. We also obtained the regression curves for two datasets with Vp lower and higher than 1,200 m/s. These regression curves can be applied when the groundwater level is known. In addition, we obtained the regression curves for density from Vs or Vp. An example of the application for those relationships in the velocity inversion is shown.

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