Surface wave analysis using active-source multi-channel seismic data in the Median Tectonic Line (MTL): Comparison of S-wave velocity along the MTL

2019 ◽  
Author(s):  
Yohei Morifuji ◽  
Takeshi Tsuji ◽  
Tatsunori Ikeda ◽  
Michiharu Ikeda ◽  
Naoki Nishizaka ◽  
...  
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Seismic Data ◽  
Wave Analysis ◽  
S Wave ◽  
Median Tectonic Line ◽  
Active Source ◽  
Tectonic Line

scholarly journals Surface-wave analysis for identifying unfrozen zones in subglacial sediments

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. EN17-EN27 ◽  
Author(s):  
Takeshi Tsuji ◽  
Tor Arne Johansen ◽  
Bent Ole Ruud ◽  
Tatsunori Ikeda ◽  
Toshifumi Matsuoka
Keyword(s):  
Surface Wave ◽  
Velocity Distribution ◽  
Wave Velocity ◽  
Radar Data ◽  
Pore Fluid ◽  
Ice Thickness ◽  
Wave Analysis ◽  
S Wave ◽  
Low Velocity ◽  
Seismic Line

To reveal the extent of freezing in subglacial sediments, we estimated S-wave velocity along a glacier using surface-wave analysis. Because the S-wave velocity varies significantly with the degree of freezing of the pore fluid in the sediments, this information is useful for identifying unfrozen zones within subglacial sediments, which again is important for glacier dynamics. We used active-source multichannel seismic data originally acquired for reflection analysis along a glacier at Spitsbergen in the Norwegian Arctic and proposed an effective approach of multichannel analysis of surface waves (MASW) in a glacier environment. Common-midpoint crosscorrelation gathers were used for the MASW to improve lateral resolution because the glacier bed has a rough topology. We used multimode analysis with a genetic algorithm inversion to estimate the S-wave velocity due to the potential existence of a low-velocity layer beneath the glacier ice and the observation of higher modes in the dispersion curves. In the inversion, we included information of ice thickness derived from high-resolution ground-penetrating radar data because a simulation study demonstrated that the ice thickness was necessary to estimate accurate S-wave velocity distribution of deep subglacial sediment. The estimated S-wave velocity distribution along the seismic line indicated that low velocities occurred below the glacier, especially beneath thick ice ([Formula: see text] for ice thicknesses larger than 50 m). Because this velocity was much lower than the velocity in pure ice ([Formula: see text]), the pore fluid was partially melted at the ice–sediment interface. At the shallower subglacial sediments (ice thickness less than 50 m), the S-wave velocity was similar to that of the pure ice, suggesting that shallow subglacial sediments are more frozen than sediments beneath thick ice.

Surface wave tomography using 3D active-source seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. EN13-EN26
Author(s):  
Ilaria Barone ◽  
Emanuel Kästle ◽  
Claudio Strobbia ◽  
Giorgio Cassiani
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Wave Velocity ◽  
Computational Time ◽  
Surface Wave Tomography ◽  
S Wave ◽  
Data Set ◽  
Velocity Models ◽  
Active Source ◽  
Wave Tomography

Surface wave tomography (SWT) is a powerful and well-established technique to retrieve 3D shear-wave (S-wave) velocity models at the regional scale from earthquakes and seismic noise measurements. We have applied SWT to 3D active-source data, in which higher modes and heterogeneous spatial sampling make phase extraction challenging. First, synthetic traveltimes calculated on a dense, regular-spaced station array are used to test the performance of three different tomography algorithms (linearized inversion, Markov chain Monte Carlo [MCMC], and eikonal tomography). The tests suggest that the lowest misfit to the input model is achieved with the MCMC algorithm, at the cost of a much longer computational time. Then, real phases were extracted from a 3D exploration data set at different frequencies. This operation included an automated procedure to isolate the fundamental mode from higher order modes, phase unwrapping in two dimensions, and the estimation of the zero-offset phase. These phases are used to compute traveltimes between each source-receiver couple, which are input into the previously tested tomography algorithms. The resulting phase-velocity maps show good correspondence, highlighting the same geologic structures for all three methods. Finally, individual dispersion curves obtained by the superposition of phase-velocity maps at different frequencies are depth inverted to retrieve a 3D S-wave velocity model.

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.

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

Ground roll: A potential tool for constraining shallow shear‐wave structure

Geophysics ◽  
1993 ◽  
Vol 58 (5) ◽  
pp. 713-719 ◽  
Author(s):  
Ghassan I. Al‐Eqabi ◽  
Robert B. Herrmann
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Wave Velocity ◽  
Wave Structure ◽  
S Wave ◽  
Data Set ◽  
Velocity Inversion ◽  
Ground Roll ◽  
Lateral Variations ◽  
Wave Arrival

The objective of this study is to demonstrate that a laterally varying shallow S‐wave structure, derived from the dispersion of the ground roll, can explain observed lateral variations in the direct S‐wave arrival. The data set consists of multichannel seismic refraction data from a USGS-GSC survey in the state of Maine and the province of Quebec. These data exhibit significant lateral changes in the moveout of the ground‐roll as well as the S‐wave first arrivals. A sequence of surface‐wave processing steps are used to obtain a final laterally varying S‐wave velocity model. These steps include visual examination of the data, stacking, waveform inversion of selected traces, phase velocity adjustment by crosscorrelation, and phase velocity inversion. These models are used to predict the S‐wave first arrivals by using two‐dimensional (2D) ray tracing techniques. Observed and calculated S‐wave arrivals match well over 30 km long data paths, where lateral variations in the S‐wave velocity in the upper 1–2 km are as much as ±8 percent. The modeled correlation between the lateral variations in the ground‐roll and S‐wave arrival demonstrates that a laterally varying structure can be constrained by using surface‐wave data. The application of this technique to data from shorter spreads and shallower depths is discussed.

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