scholarly journals Anisotropic P-wave traveltime tomography implementing Thomsen's weak approximation in TOMO3D

2019 ◽  
Author(s):  
Adrià Meléndez ◽  
Clara Estela Jiménez ◽  
Valentí Sallarès ◽  
César R. Ranero
Keyword(s):  
P Wave ◽  
Weak Approximation ◽  
Weak Anisotropy ◽  
Traveltime Tomography ◽  
Simultaneous Inversion ◽  
Advantages And Disadvantages ◽  
Parameter Combination ◽  
P Wave Velocity ◽  
Rough Approximation ◽  
Vti Media

Abstract. We present the implementation of Thomsen's weak anisotropy approximation for VTI media within TOMO3D, our code for 2-D and 3-D joint refraction and reflection traveltime tomographic inversion. In addition to the inversion of seismic P-wave velocity and reflector depth, the code can now retrieve models of the Thomsen's parameters δ and ε. Here we test this new implementation following four different strategies on a canonical synthetic experiment. First, we study the sensitivity of traveltimes to the presence of a 25 % anomaly in each of the parameters. Next, we invert for two combinations of parameters, (v, δ, ε) and (v, δ, v⟂), following two inversion strategies, simultaneous and sequential, and compare the results to study their performances and discuss their advantages and disadvantages. Simultaneous inversion is the preferred strategy and the parameter combination (v, δ, ε) produces the best overall results. The only advantage of the parameter combination (v, δ, v⟂) is a better recovery of the magnitude of v. In each case we derive the fourth parameter from the equation relating ε, v⟂ and v. Recovery of v, ε and v⟂ is satisfactory whereas δ proves to be impossible to recover even in the most favorable scenario. However, this does not hinder the recovery of the other parameters, and we show that it is still possible to obtain a rough approximation of δ distribution in the medium by sampling a reasonable range of homogeneous initial δ models and averaging the final δ models that are satisfactory in terms of data fit.

scholarly journals Anisotropic P-wave travel-time tomography implementing Thomsen's weak approximation in TOMO3D

Solid Earth ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 1857-1876
Author(s):  
Adrià Meléndez ◽  
Clara Estela Jiménez ◽  
Valentí Sallarès ◽  
César R. Ranero
Keyword(s):  
Travel Time ◽  
Transverse Isotropy ◽  
P Wave ◽  
Weak Approximation ◽  
Weak Anisotropy ◽  
Simultaneous Inversion ◽  
Advantages And Disadvantages ◽  
Parameter Combination ◽  
Rough Approximation ◽  
Vertical Transverse Isotropy

Abstract. We present the implementation of Thomsen's weak anisotropy approximation for vertical transverse isotropy (VTI) media within TOMO3D, our code for 2-D and 3-D joint refraction and reflection travel-time tomographic inversion. In addition to the inversion of seismic P-wave velocity and reflector depth, the code can now retrieve models of Thomsen's parameters (δ and ε). Here, we test this new implementation following four different strategies on a canonical synthetic experiment in ideal conditions with the purpose of estimating the maximum capabilities and potential weak points of our modeling tool and strategies. First, we study the sensitivity of travel times to the presence of a 25 % anomaly in each of the parameters. Next, we invert for two combinations of parameters (v, δ, ε and v, δ, v⊥), following two inversion strategies, simultaneous and sequential, and compare the results to study their performance and discuss their advantages and disadvantages. Simultaneous inversion is the preferred strategy and the parameter combination (v, δ, ε) produces the best overall results. The only advantage of the parameter combination (v, δ, v⊥) is a better recovery of the magnitude of v. In each case, we derive the fourth parameter from the equation relating ε, v⊥ and v. Recovery of v, ε and v⊥ is satisfactory, whereas δ proves to be impossible to recover even in the most favorable scenario. However, this does not hinder the recovery of the other parameters, and we show that it is still possible to obtain a rough approximation of the δ distribution in the medium by sampling a reasonable range of homogeneous initial δ models and averaging the final δ models that are satisfactory in terms of data fit.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

Iterative P-wave velocity inversion using Markov chain Monte Carlo method

2020 ◽  
Author(s):  
Hyunggu Jun ◽  
Hyeong-Tae Jou ◽  
Han-Joon Kim ◽  
Sang Hoon Lee
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Structure ◽  
Low Frequency ◽  
P Wave ◽  
Velocity Analysis ◽  
Seismic Section ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Frequency Components

<p>Imaging the subsurface structure through seismic data needs various information and one of the most important information is the subsurface P-wave velocity. The P-wave velocity structure mainly influences on the location of the reflectors during the subsurface imaging, thus many algorithms has been developed to invert the accurate P-wave velocity such as conventional velocity analysis, traveltime tomography, migration velocity analysis (MVA) and full waveform inversion (FWI). Among those methods, conventional velocity analysis and MVA can be widely applied to the seismic data but generate the velocity with low resolution. On the other hands, the traveltime tomography and FWI can invert relatively accurate velocity structure, but they essentially need long offset seismic data containing sufficiently low frequency components. Recently, the stochastic method such as Markov chain Monte Carlo (McMC) inversion was applied to invert the accurate P-wave velocity with the seismic data without long offset or low frequency components. This method uses global optimization instead of local optimization and poststack seismic data instead of prestack seismic data. Therefore, it can avoid the problem of the local minima and limitation of the offset. However, the accuracy of the poststack seismic section directly affects the McMC inversion result. In this study, we tried to overcome the dependency of the McMC inversion on the poststack seismic section and iterative workflow was applied to the McMC inversion to invert the accurate P-wave velocity from the simple background velocity and inaccurate poststack seismic section. The numerical test showed that the suggested method could successfully invert the subsurface P-wave velocity.</p>

scholarly journals 3D P-wave velocity image beneath the Pannonian Basin using traveltime tomography

2019 ◽  
Vol 54 (3) ◽  
pp. 373-386 ◽  
Author(s):  
Máté Timkó ◽  
István Kovács ◽  
Zoltán Wéber
Keyword(s):  
Wave Velocity ◽  
Pannonian Basin ◽  
P Wave ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Velocity Image

Using 2D long-streamer seismic data waveform tomography to decipher sedimentary record of fault activity

2020 ◽  
Author(s):  
Amin Kahrizi ◽  
Matthias Delescluse ◽  
Mathieu Rodriguez ◽  
Pierre-Henri Roche ◽  
Anne Becel ◽  
...  
Keyword(s):  
Mass Transport ◽  
Seismic Data ◽  
Velocity Model ◽  
P Wave ◽  
Fault System ◽  
Fault Activity ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Waveform Tomography ◽  
Mass Transport Deposits

<p>Acoustic full-waveform inversion (FWI), or waveform tomography, involves use of both phase and amplitude of the recorded compressional waves to obtain a high-resolution P-wave velocity model of the propagation medium. Recent theoretical and computing advances now allow the application of this highly non-linear technique to field data. This led to common use of the FWI for industrial purposes related to reservoir imaging, physical properties of rocks, and fluid flow. Application of FWI in the academic domain has, so far, been limited, mostly because of the lack of adequate seismic data. While refraction seismic datasets include large source-receiver offsets that are useful to find a suitable starting velocity model through traveltime tomography, these acquisitions rarely reach the high density of receivers necessary for waveform tomography. On the other hand, multichannel seismic (MCS) reflection data acquisition has a dense receiver spacing but only modern long-streamer data have offsets that, in some cases, enable constraining subsurface velocities at a significant enough depth to be useful for structural or tectonic purposes.</p><p>In this study, we show how FWI can help decipher the record of a fault activity through time at the Shumagin Gap in Alaska. The MCS data were acquired on RV Marcus G. Langseth during the ALEUT cruise in the summer of 2011 using two 8-km-long seismic streamers and a 6600 cu. in. tuned airgun array. One of the most noticeable reflection features imaged on two profiles is a large, landward-dipping normal fault in the overriding plate; a structural configuration making the area prone to generating both transoceanic and local tsunamis, including from landslides. This fault dips ~40°- 45°, cuts the entire crust and connects to the plate boundary fault at ~35 km depth, near the intersection of the megathrust with the forearc mantle wedge. The fault system reaches the surface at the shelf edge 75 km from the trench, forming the Sanak basin where the record of the recent activity of the fault is not clear. Indeed, contouritic currents tend to be trapped by the topography created by faults, even after they are no longer active.  Erosion surfaces and onlaps from contouritic processes as well as gravity collapses and mass transport deposits results in complex structures that make it challenging to evaluate the fault activity. The long streamers used facilitated recording of refraction arrivals in the target continental slope area, which permitted running streamer traveltime tomography followed by FWI to produce coincident detailed velocity profiles to complement the reflection sections. FWI imaging of the Sanak basin reveals low velocities of mass transport deposits and velocity inversions indicate mechanically weak layers linking some faults to gravity sliding on a décollement. These details question previous interpretation of a present-day active fault. Our goal is to further analyze the behavior of the fault system using the P-wave velocity models from FWI to quantitatively detect fluids and constrain sediment properties.</p>

High‐resolution crosswell imaging of a west Texas carbonate reservoir: Part 5—Core analysis

Geophysics ◽  
1995 ◽  
Vol 60 (3) ◽  
pp. 712-726 ◽  
Author(s):  
Richard C. Nolen‐Hoeksema ◽  
Zhijing Wang ◽  
Jerry M. Harris ◽  
Robert T. Langan
Keyword(s):  
Wave Velocity ◽  
Field Experiments ◽  
Velocity Ratio ◽  
Carbonate Reservoir ◽  
P Wave ◽  
Core Analysis ◽  
S Wave ◽  
Traveltime Tomography ◽  
West Texas ◽  
P Wave Velocity

We conducted a core analysis program to provide supporting data to a series of crosswell field experiments being carried out in McElroy Field by Stanford University’s Seismic Tomography Project. The objective of these experiments is to demonstrate the use of crosswell seismic profiling for reservoir characterization and for monitoring [Formula: see text] flooding. For these west Texas carbonates, we estimate that [Formula: see text] saturation causes P‐wave velocity to change by −1.9% (pooled average, range = −6.3 to +0.1%), S‐wave velocity by +0.6% (range = 0 to 2.7%), and the P‐to‐S velocity ratio by −2.4% (range = −6.4 to −0.3%). When we compare these results to the precisions we can expect from traveltime tomography (about ±1% for P‐ and S‐wave velocity and about ±2% for the P‐to‐S velocity ratio), we conclude that time‐lapse traveltime tomography is sensitive enough to resolve changes in the P‐wave velocity, S‐wave velocity, and P‐to‐S velocity ratio that result from [Formula: see text] saturation. We concentrated here on the potential for [Formula: see text] saturation to affect seismic velocities. The potential for [Formula: see text] saturation to affect other seismic properties, not discussed here, may prove to be more significant (e.g., P‐wave and S‐wave impedance).

Simultaneous inversion of P-wave impedance and the ratio of S- to P-wave velocity

2018 ◽  
Author(s):  
Xin Fu ◽  
Feng Zhang ◽  
Xiang-Yang Li ◽  
Shuai Yang ◽  
Guo-Rui Ma
Keyword(s):  
Wave Velocity ◽  
Wave Impedance ◽  
P Wave ◽  
Simultaneous Inversion ◽  
P Wave Velocity

scholarly journals P-wave velocity structure of the uppermost mantle beneath Hawaii from traveltime tomography

2001 ◽  
Vol 146 (3) ◽  
pp. 594-606 ◽  
Author(s):  
Frederik J. Tilmann ◽  
Harley M. Benz ◽  
Keith F. Priestley ◽  
Paul G. Okubo
Keyword(s):  
Wave Velocity ◽  
Velocity Structure ◽  
P Wave ◽  
Uppermost Mantle ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
P Wave Velocity Structure

Cross‐borehole tomography in anisotropic media

Geophysics ◽  
1992 ◽  
Vol 57 (9) ◽  
pp. 1194-1198 ◽  
Author(s):  
Philip Carrion ◽  
Jesse Costa ◽  
Jose E. Ferrer Pinheiro ◽  
Michael Schoenberg
Keyword(s):  
Ray Tracing ◽  
Anisotropic Media ◽  
P Wave ◽  
Weak Anisotropy ◽  
Tomographic Inversion ◽  
Traveltime Tomography ◽  
Accurate Imaging

Anisotropy has significant effect on traveltime cross‐borehole tomography. Even relatively weak anisotropy cannot be ignored if accurate velocity estimates are desired, since isotropic traveltime tomography treats anisotropy as inhomogeneity. Traveltime data in our examples were synthetically generated by a ray‐tracing code for anisotropic media, and the computed quasi‐P‐wave traveltimes were subsequently inverted using the “dual tomography” technique (Carrion, 1991). The results of the tomographic inversion show typical artifacts due to the anisotropy, and that accurate imaging is impossible without taking the anisotropy into account.

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