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 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

Shallow Seismic Reflection Investigation of Unstable Sedimentary Deposits in the Dăneasa Area, Romania

2019 ◽  
Vol 24 (1) ◽  
pp. 169-174
Author(s):  
Ionelia Panea
Keyword(s):  
Wave Velocity ◽  
Seismic Reflection ◽  
Vertical Component ◽  
P Wave ◽  
Seismic Section ◽  
Traveltime Tomography ◽  
Railway Line ◽  
Shallow Seismic ◽  
P Wave Velocity ◽  
National Road

Results of shallow seismic reflection measurements, performed over an area with unstable sedimentary formations in the near subsurface, are presented. The investigated area is located on the Olt Valley, in the vicinity of the Dăneasa village, Romania, crossed by a railway line and a national road on which the traffic is very intense. Data were collected along a linear profile, with a length of 94 m, perpendicular to the railway line and the national road using a fixed spread of 48 vertical-component geophones with the sources rolling along the acquisition line. The surface waves generated by the intense traffic on the national road interfered with the seismic arrivals seen on most of shot gathers, especially with those seen on the traces from receiver locations greater than 48 m. The interrupted continuity of two shallow reflectors, R1 and R2, seen on the depth-converted seismic section at depth values shallower than 30 m, could be interpreted as an effect of the presence of two steep failure planes deveIoped into the ground. A P-wave velocity-depth model down to a depth of 20 m after first-arrival traveltime tomography was then created, on which four sedimentary layers with variable thicknesses and constant velocities, separated by the interfaces L1, L2 and L3, were interpreted. By comparing the depth-converted seismic section with the P-wave velocity-depth model, the reflectors R1 and R2 on the seismic section correspond pretty well with the interfaces L2 and L3 from the velocity-depth model.

Crustal P-wave velocity structure and earthquake distribution in the Jiaodong Peninsula, China

2021 ◽  
pp. 228973
Author(s):  
Junhao Qu ◽  
Stephen S. Gao ◽  
Changzai Wang ◽  
Kelly H. Liu ◽  
Shaohui Zhou ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Velocity Structure ◽  
P Wave ◽  
Jiaodong Peninsula ◽  
P Wave Velocity ◽  
Earthquake Distribution ◽  
P Wave Velocity Structure

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.

Using Seismic Data to Predict Shale Pore Pressure and Overpressure Sweet Spots: A Case Study from the Lower Silurian Longmaxi Formation in Sichuan Basin, China

2021 ◽  
Author(s):  
Sheng Chen ◽  
Qingcai Zeng ◽  
Xiujiao Wang ◽  
Qing Yang ◽  
Chunmeng Dai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wave Velocity ◽  
Shale Gas ◽  
Seismic Data ◽  
P Wave ◽  
Formation Pressure ◽  
S Wave ◽  
P Wave Velocity ◽  
New Formation ◽  
Sweet Spots

Abstract Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.

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