Detecting near-surface objects with seismic waveform tomography

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC119-WCC127 ◽  
Author(s):  
Brendan Smithyman ◽  
R. Gerhard Pratt ◽  
John Hayles ◽  
Ralph Wittebolle
Keyword(s):  
Seismic Attenuation ◽  
Blind Test ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Seismic Waveform ◽  
Long Offset ◽  
Velocity Images ◽  
Weight Drop ◽  
Waveform Tomography ◽  
Geophysical Imaging

Three shallow, high-velocity, rubble-filled targets are imaged using waveform tomography in an engineering-scale clay embankment at Seven Sisters Falls, Manitoba, Canada, to locate targets buried at approximately [Formula: see text] as a blind test of geophysical imaging methods. Previous studies use near-offset reflection methods to image the targets; however, this test uses waveform tomography of the long-offset, refracted arrivals to image P-velocity and seismic attenuation. The targets are invisible to standard traveltime tomography. Using weight-drop data, with frequencies of 20–150 Hz, the subwavelength targets are resolved in the velocity images and complementary images of seismic [Formula: see text] are produced. The interpreted target locations are consistent with limited survey information from the embankment construction. Multiple quality-control efforts, paired with a very good fit between model and observed data, indicate the reliability of the results.

Early arrival waveform tomography on near-surface refraction data

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. U47-U57 ◽  
Author(s):  
Jianming Sheng ◽  
Alan Leeds ◽  
Maike Buddensiek ◽  
Gerard T. Schuster
Keyword(s):  
Ground Truth ◽  
Velocity Model ◽  
Frequency Method ◽  
Near Surface ◽  
Good Convergence ◽  
Traveltime Tomography ◽  
Early Arrival ◽  
Synthetic Test ◽  
Waveform Tomography ◽  
Tomography Method

We develop a waveform-tomography method for estimating the velocity distribution that minimizes the waveform misfit between the predicted and observed early arrivals in space-time seismograms. By fitting the waveforms of early arrivals, early arrival waveform tomography (EWT) naturally takes into account more general wave-propagation effects compared to the high-frequency method of traveltime tomography, meaning that EWT can estimate a wider range of slowness wavenumbers. Another benefit of EWT is more reliable convergence compared to full-waveform tomography, because an early-arrival misfit function contains fewer local minima. Synthetic test results verify that the waveform tomogram is much more accurate than the traveltime tomogram and that this algorithm has good convergence properties. For marine data from the Gulf of Mexico, the statics problem caused by shallow, gassy muds was attacked by using EWT to obtain a more accurate velocity model. Using the waveform tomogram to correct for statics, the stacked section was significantly improved compared to using the normal move-out (NMO) velocity, and moderately improved compared to using the traveltime tomogram. Inverting high-resolution land data from Mapleton, Utah, showed an EWT velocity tomogram that was more consistent with the ground truth (trench log) than the traveltime tomogram. Our results suggest that EWT can provide supplemental, shorter-wavelength information compared to the traveltime tomogram for both shallow and moderately deep seismic data.

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>

Fracture effects in seismic attenuation images reconstructed by waveform tomography

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. R25-R34 ◽  
Author(s):  
Ying Rao ◽  
Yanghua Wang
Keyword(s):  
Petroleum Reservoirs ◽  
Seismic Attenuation ◽  
Fracture Density ◽  
Seismic Reflection Data ◽  
Fracture Angle ◽  
Reflection Data ◽  
Seismic Waveform ◽  
Half Wavelength ◽  
Waveform Tomography ◽  
Domain Inversion

We have investigated seismic waveform tomography to characterize fractures in petroleum reservoirs. Seismic reflection data are used in a frequency-domain inversion to reconstruct subsurface attenuation images. The images show fracture distributions, from which fracture density is estimated. Fractures smaller than or equal to a half-wavelength of seismic act as single scatterers, producing images of strong attenuation ellipses and from which fracture density can be estimated. When fracture size approaches one wavelength, fracture orientation affects the attenuation image. Horizontal fractures act as individual reflectors and produce strong tomographic attenuation images from which fracture density can be estimated. The strength of the attenuation image decreases when the fracture angle relative to horizontal increases; vertical fractures produce the weakest attenuation image. Consequently, the accuracy of fracture density measurements decreases with increased fracture angle unless waveform tomography includes different seismic modes acquired from several directions.

Waveform tomography of field vibroseis data using an approximate 2D geometry leads to improved velocity models

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. R33-R43 ◽  
Author(s):  
Brendan R. Smithyman ◽  
Ronald M. Clowes
Keyword(s):  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Time Shift ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Full Waveform ◽  
First Arrival ◽  
Waveform Tomography

Waveform tomography, a combination of traveltime tomography (or inversion) and waveform inversion, is applied to vibroseis first-arrival data to generate an interpretable model of P-wave velocity for a site in the Nechako Basin, south-central British Columbia, Canada. We use constrained 3D traveltime inversion followed by 2D full-waveform inversion to process long-offset (14.4 km) first-arrival refraction waveforms, resulting in a velocity model of significantly higher detail than a conventional refraction-statics model generated for a processing workflow. The crooked-line acquisition of the data set makes 2D full-waveform inversion difficult. Thus, a procedure that improves the tractability of waveform tomography processing of vibroseis data recorded on crooked roads is developed to generate a near-surface ([Formula: see text]) velocity model for the study area. The data waveforms are first static corrected using a time shift determined by 3D raytracing, which accounts for the crossline offsets produced by the crooked-line acquisition. The velocity model generated from waveform tomography exhibits substantial improvement when compared with a conventional refraction-statics model. It also shows improved resolution of sharp discontinuities and low-velocity regions when compared to the model from traveltime tomography alone, especially in regions where the geometry errors are moderate. Interpretation of the near-surface velocity model indicates possible subbasins in the Nechako Basin and delineates the Eocene volcanic rocks of the study area. This approach limits the ability of the full-waveform inversion to fit some propagation modes; however, the tractability of the inversion in the near-surface region is improved. This new development is especially useful in studies that do not warrant 3D seismic acquisition and processing.

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

2022 ◽  
Vol 41 (1) ◽  
pp. 40-46
Author(s):  
Öz Yilmaz ◽  
Kai Gao ◽  
Milos Delic ◽  
Jianghai Xia ◽  
Lianjie Huang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Surface Modeling ◽  
P Wave ◽  
Borehole Data ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Wave Inversion ◽  
Full Wave ◽  
Elastic Inversion

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

Interpretation of 3-D effects in long‐offset transient electromagnetic (LOTEM) soundings in the Münsterland area/Germany

Geophysics ◽  
1992 ◽  
Vol 57 (9) ◽  
pp. 1127-1137 ◽  
Author(s):  
Andreas Hördt ◽  
Vladimir L. Druskin ◽  
Leonid A. Knizhnerman ◽  
Kurt‐Martin Strack
Keyword(s):  
Integral Equation ◽  
Thin Sheet ◽  
Three Dimensional ◽  
Equation Modeling ◽  
Data Sets ◽  
Sign Reversal ◽  
Near Surface ◽  
Transient Electromagnetic ◽  
Long Offset ◽  
Geological Situation

The interpretation of long‐offset transient electromagnetic (LOTEM) data is usually based on layered earth models. Effects of lateral conductivity variations are commonly explained qualitatively, because three‐dimensional (3-D) numerical modeling is not readily available for complex geology. One of the first quantitative 3-D interpretations of LOTEM data is carried out using measurements from the Münsterland basin in northern Germany. In this survey area, four data sets show effects of lateral variations including a sign reversal in the measured voltage curve at one site. This sign reversal is a clear indicator of two‐dimensional (2-D) or 3-D conductivity structure, and can be caused by current channeling in a near‐surface conductive body. Our interpretation strategy involves three different 3-D forward modeling programs. A thin‐sheet integral equation modeling routine used with inversion gives a first guess about the location and strike of the anomaly. A volume integral equation program allows models that may be considered possible geological explanations for the conductivity anomaly. A new finite‐difference algorithm permits modeling of much more complex conductivity structures for simulating a realistic geological situation. The final model has the zone of anomalous conductivity aligned below a creek system at the surface. Since the creeks flow along weak zones in this area, the interpretation seems geologically reasonable. The interpreted model also yields a good fit to the data.

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