scholarly journals Ultrasonic Spectrum Analysis of Granite Damage Evolution Based on Dry-Coupled Ultrasonic Monitoring Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yang Liu ◽  
Lan Qiao ◽  
Yuan Li ◽  
Guodong Ma ◽  
Andrei M. Golosov ◽  
...  
Keyword(s):  
Crack Growth ◽  
Wave Velocity ◽  
Growth Stage ◽  
Dominant Frequency ◽  
The State ◽  
P Wave ◽  
Ultrasonic Monitoring ◽  
S Wave ◽  
Spectral Parameters ◽  
Acoustic Spectrum

The self-developed dry-coupled rock ultrasonic monitoring system is adopted to set up a multidirectional and multiwaveform ultrasonic monitoring network, which aims to analyse the evolution law of acoustic spectrum parameters in the process of granite loading failure under uniaxial compression, to explore the dominant acoustic spectrum characteristic information at different stages of granite loading, and to verify in situ the damage monitoring of time-effect deformation. The results show that the wave velocity, amplitude, and amplitude-frequency of the first wave and the velocity of P-wave and S-wave show a significant upward trend in the rock compaction section. After entering the elastic stage, the three spectral parameters become peacefully stable, and the stage transformation is obvious. In the stable crack growth stage, with the initiation of the crack, the dominant frequency of S-wave shows a significant stage transition compared with the global ultrasonic wave velocity and the first arrived amplitude, and the dominant frequency decreases by 6%. In the unstable crack growth stage, the three acoustic spectrum parameters present obvious downward trend, and the first arrived wave amplitude of S-wave is found to have a significant decline of 39.1%. On the eve of failure, the amplitude-frequency of S-wave shows different feature from the P-wave; that is, S-wave transfers from the state of multipeak in wide frequency to the state of single peak in low frequency, which is the failure precursor of the rock sample.

scholarly journals Metode Seismik dalam Pemodelan Fisis Letusan Gunung Lumpur Bledug Kuwu

2019 ◽  
Vol 2 (2) ◽  
pp. 61-66
Author(s):  
Ahmad Fauzi Pohan ◽  
Rusnoviandi Rusnoviandi
Keyword(s):  
Wave Velocity ◽  
Physical Modeling ◽  
Vertical Component ◽  
Dominant Frequency ◽  
P Wave ◽  
Physical Parameters ◽  
S Wave ◽  
Interesting Phenomenon ◽  
Central Java ◽  
P Wave Velocity

Aktivitas gunung lumpur Bledug Kuwu di Jawa  Tengah merupakan fenomena yang menarik dikaji menggunakan pemodelan fisis. Tujuan penelitian ini adalah mengetahui parameter dari medium gunung lumpur Bledug Kuwu. Adapun pemodelan fisis yang dilakukan dengan menggunakan media fisis akuarium berukuran 59 × 59 × 37,3 cm yang diisi material dari lumpur Bledug Kuwu. Sumber letusan dihasilkan dari tekanan kompresor yang dapat diatur kedalaman (10.5, 13, dan 15.5 cm) dan sudut (30o, 45o dan 60o) sumbernya. Sensor yang digunakan geophone komponen vertikal sebanyak 3 buah dengan durasi perekaman selama 5 dan 2,5 detik. Data diambil dengan frekuensi sampel 2 dan 4 kHz untuk masing-masing durasi perekaman. Konfigurasi sumber dan geophone dibuat sesuai dengan pemodelan fisisnya. Pengukuran desnsitas lumpur menunjukkan angka sebesar 1200 kg/m3. Berdasarkan hasil analisis seismogram model fisis diperoleh kecepatan perambatan gelombang-P pada medium lumpur Bledug Kuwu adalah sebesar 48,74 m/s,dan gelombang-S sebesar 28,14 m/s dengan frekuensi dominan antara 20 sampai 25 Hz.   Bledug Kuwu mud volcano activity in Central Java is an interesting phenomenon to be studied using both physical  modeling. The objective of this study was to determine the physical parameters of the medium of Bledug Kuwu. The Physical model was an aquarium with a dimension of 59 × 59 × 37.3 cm filled with Bledug Kuwu’s mud. The eruption source is generated by a compressor pressure that can be controled both the depth(10.5, 13, and 15.5 cm) and the angel of the source (30o, 45o and 60o). The resulting seismic signals were recorded by using 3 vertical component geophones for 10 and 5 seconds durations at a frequency of 2 and 4 kHz respectivel, mud density 1200 kg/m3 . The physical modeling shows that the P-wave velocity of the Bledug Kuwu’s medium is 48.7 m/s, S-wave velocity of Bledug Kuwu’s is 28,14 m/s  with a dominant frequency of 20 to 25 Hz.

scholarly journals Delineation of Geothermal Manifestation in Sangubanyu Area Based on Microtremor HVSR Method

2019 ◽  
Vol 125 ◽  
pp. 14012
Author(s):  
Dwi Sudarmawan Sudjono ◽  
Udi Harmoko ◽  
Gatot Yuliyanto
Keyword(s):  
Wave Velocity ◽  
Hot Springs ◽  
Amplification Factor ◽  
Hot Spring ◽  
Dominant Frequency ◽  
P Wave ◽  
Normal Faults ◽  
S Wave ◽  
Central Java ◽  
Hvsr Method

The aims of this study is to describe the distribution of P wave velocity and S wave velocity value and Vp/Vs ratio based on microtremor HVSR method and its relationship with the appearance of hot spring at Sangubanyu Village. Geothermal manifestations in the Sangubanyu area are located in Bawang sub-district, Batang regency, and Plantungan sub-district, Kendal regency, Central Java province. HVSR method is used to process microtremor data which produces the dominant frequency value and amplification factor then do inversion on the H/V curve to get the value of the Vp and Vs. The results of the microtremor HVSR processing obtained the distribution of the dominant frequency values in the study area between 0.62 to 0.73 Hz and amplification factor values 1.09 to 1.39, the inversion results on the H/V curve obtained the distribution of Vp values between 131.76 m/s to 2181.19 m/s, Vs values between 76.61 to 1129.42 m/s, Vp/Vs ratio 1.64 – 3.35. Based on the results of the analysis and wave velocity interpretation, the subsurface structure of manifestation area of Sangubanyu is composed of tuffaceous sandstone and volcanic breccia, the normal faults with southern blocks relatively downthrown, that cause the appearance of hot springs.

scholarly journals Metode Seismik dalam Pemodelan Fisis Letusan Gunung Lumpur Bledug Kuwu

2019 ◽  
Vol 2 (2) ◽  
pp. 61-66
Author(s):  
Ahmad Fauzi Pohan ◽  
Rusnoviandi Rusnoviandi
Keyword(s):  
Wave Velocity ◽  
Physical Modeling ◽  
Vertical Component ◽  
Dominant Frequency ◽  
P Wave ◽  
Physical Parameters ◽  
S Wave ◽  
Interesting Phenomenon ◽  
Central Java ◽  
P Wave Velocity

Bledug Kuwu mud volcano activity in Central Java is an interesting phenomenon to be studied using both physical modeling. The objective of this study was to determine the physical parameters of the medium of Bledug Kuwu. The Physical model was an aquarium with a dimension of 59 × 59 × 37.3 cm filled with Bledug Kuwu’s mud. The eruption source is generated by a compressor pressure that can be controled both the depth(10.5, 13, and 15.5 cm) and the angel of the source  (30o, 45o  and 60o). The resulting seismic signals were recorded by using 3 vertical component geophones for 10 and 5 seconds durations at a frequency of 2 and 4 kHz respectivel, mud density 1200 kg/m3 . The physical modeling shows that the P-wave velocity of the Bledug Kuwu’s medium is 48.7 m/s, S-wave velocity of Bledug Kuwu’s is 28,14 m/s  with a dominant frequency of 20 to 25 Hz.

Numerical and Theoretical Investigation on the Origin of the Multiple Peaks of the H/V Ratio Curve

2021 ◽  
Author(s):  
Wanbo Xiao ◽  
Siqi Lu ◽  
Yanbin Wang
Keyword(s):  
Rayleigh Wave ◽  
Wave Velocity ◽  
Subsurface Layer ◽  
P Wave ◽  
Theoretical Formula ◽  
S Wave ◽  
Multiple Peaks ◽  
Ratio Curve ◽  
Multiple Layer ◽  
Wave Resonance

<p>Despite the popularity of the horizontal to vertical spectral ratio (HVSR) method in site effect studies, the origin of the H/V peaks has been controversial since this method was proposed. Many previous studies mainly focused on the explanation of the first or single peak of the H/V ratio, trying to distinguish between the two hypotheses — the S-wave resonance and ellipticity of Rayleigh wave. However, it is common both in numerical simulations and practical experiments that the H/V ratio exhibits multiple peaks, which is essential to explore the origin of the H/V peaks.</p><p>The cause for the multiple H/V peaks has not been clearly figured out, and once was simply explained as the result of multi subsurface layers. Therefore, we adopted numerical method to simulate the ambient noise in various layered half-space models and calculated the H/V ratio curves for further comparisons. The peak frequencies of the H/V curves accord well with the theoretical frequencies of S-wave resonance in two-layer models, whose frequencies only depend on the S wave velocity and the thickness of the subsurface layer. The same is true for models with varying model parameters. Besides, the theoretical formula of the S-wave resonance in multiple-layer models is proposed and then supported by numerical investigations as in the cases of two-layer models. We also extended the S-wave resonance to P-wave resonance and found that its theoretical frequencies fit well with the V/H peaks, which could be an evidence to support the S-wave resonance theory from a new perspective. By contrast, there are obvious differences between the higher orders of the H/V ratio peaks and the higher orders of Rayleigh wave ellipticity curves both in two-layer and multiple-layer models. The Rayleigh wave ellipticity curves are found to be sensitive to the Poisson’s ratio and the thickness of the subsurface layer, so the variation of the P wave velocity can affect the peak frequencies of the Rayleigh wave ellipticity curves while the H/V peaks show slight change. The Rayleigh wave ellipticity theory is thus proved to be inappropriate for the explanation of the multiple H/V peaks, while the possible effects of the Rayleigh wave on the fundamental H/V peak still cannot be excluded.</p><p>Based on the analyses above, we proposed a new evidence to support the claim that the peak frequencies of the H/V ratio curve, except the fundamental peaks, are caused by S-wave resonance. The relationship between the P-wave resonance and the V/H peaks may also find further application.</p>

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.

Constraints on the composition of the crust and uppermost mantle in northwestern Canada: Vp/Vs variations along Lithoprobe's SNorCLE transect

2005 ◽  
Vol 42 (6) ◽  
pp. 1205-1222 ◽  
Author(s):  
Gabriela Fernández-Viejo ◽  
Ron M Clowes ◽  
J Kim Welford
Keyword(s):  
Wave Velocity ◽  
Upper Mantle ◽  
Laboratory Data ◽  
High Ratio ◽  
P Wave ◽  
S Wave ◽  
Uppermost Mantle ◽  
Crust And Upper Mantle ◽  
Slave Craton ◽  
Crustal Composition

Shear-wave seismic data recorded along four profiles during the SNoRE 97 (1997 Slave – Northern Cordillera Refraction Experiment) refraction – wide-angle reflection experiment in northwestern Canada are analyzed to provide S-wave velocity (Vs) models. These are combined with previous P-wave velocity (Vp) models to produce cross sections of the ratio Vp/Vs for the crust and upper mantle. The Vp/Vs values are related to rock types through comparisons with published laboratory data. The Slave craton has low Vp/Vs values of 1.68–1.72, indicating a predominantly silicic crustal composition. Higher values (1.78) for the Great Bear and eastern Hottah domains of the Wopmay orogen imply a more mafic than average crustal composition. In the western Hottah and Fort Simpson arc, values of Vp/Vs drop to ∼1.69. These low values continue westward for 700 km into the Foreland and Omineca belts of the Cordillera, providing support for the interpretation from coincident seismic reflection studies that much of the crust from east of the Cordilleran deformation front to the Stikinia terrane of the Intermontane Belt consists of quartzose metasedimentary rocks. Stikinia shows values of 1.78–1.73, consistent with its derivation as a volcanic arc terrane. Upper mantle velocity and ratio values beneath the Slave craton indicate an ultramafic peridotitic composition. In the Wopmay orogen, the presence of low Vp/Vs ratios beneath the Hottah – Fort Simpson transition indicates the presence of pyroxenite in the upper mantle. Across the northern Cordillera, low Vp values and a moderate-to-high ratio in the uppermost mantle are consistent with the region's high heat flow and the possible presence of partial melt.

Nonlinear two‐dimensional elastic inversion of multioffset seismic data

Geophysics ◽  
1987 ◽  
Vol 52 (9) ◽  
pp. 1211-1228 ◽  
Author(s):  
Peter Mora
Keyword(s):  
Wave Equation ◽  
Wave Velocity ◽  
A Priori ◽  
Gaussian Model ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Elastic Wave Equation ◽  
Gradient Direction ◽  
P Wave Velocity

The treatment of multioffset seismic data as an acoustic wave field is becoming increasingly disturbing to many geophysicists who see a multitude of wave phenomena, such as amplitude‐offset variations and shearwave events, which can only be explained by using the more correct elastic wave equation. Not only are such phenomena ignored by acoustic theory, but they are also treated as undesirable noise when they should be used to provide extra information, such as S‐wave velocity, about the subsurface. The problems of using the conventional acoustic wave equation approach can be eliminated via an elastic approach. In this paper, equations have been derived to perform an inversion for P‐wave velocity, S‐wave velocity, and density as well as the P‐wave impedance, S‐wave impedance, and density. These are better resolved than the Lamé parameters. The inversion is based on nonlinear least squares and proceeds by iteratively updating the earth parameters until a good fit is achieved between the observed data and the modeled data corresponding to these earth parameters. The iterations are based on the preconditioned conjugate gradient algorithm. The fundamental requirement of such a least‐squares algorithm is the gradient direction which tells how to update the model parameters. The gradient direction can be derived directly from the wave equation and it may be computed by several wave propagations. Although in principle any scheme could be chosen to perform the wave propagations, the elastic finite‐ difference method is used because it directly simulates the elastic wave equation and can handle complex, and thus realistic, distributions of elastic parameters. This method of inversion is costly since it is similar to an iterative prestack shot‐profile migration. However, it has greater power than any migration since it solves for the P‐wave velocity, S‐wave velocity, and density and can handle very general situations including transmission problems. Three main weaknesses of this technique are that it requires fairly accurate a priori knowledge of the low‐ wavenumber velocity model, it assumes Gaussian model statistics, and it is very computer‐intensive. All these problems seem surmountable. The low‐wavenumber information can be obtained either by a prior tomographic step, by the conventional normal‐moveout method, by a priori knowledge and empirical relationships, or by adding an additional inversion step for low wavenumbers to each iteration. The Gaussian statistics can be altered by preconditioning the gradient direction, perhaps to make the solution blocky in appearance like well logs, or by using large model variances in the inversion to reduce the effect of the Gaussian model constraints. Moreover, with some improvements to the algorithm and more parallel computers, it is hoped the technique will soon become routinely feasible.

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

scholarly journals Discussion on the Theoretical Basis for Cross-Over Method Applied to Downhole Wave Velocity Test

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qing Dong ◽  
Zheng-hua Zhou ◽  
Su Jie ◽  
Bing Hao ◽  
Yuan-dong Li
Keyword(s):  
Numerical Simulation ◽  
Numerical Simulations ◽  
Wave Velocity ◽  
Theoretical Basis ◽  
Influence Factors ◽  
P Wave ◽  
Engineering Practice ◽  
S Wave ◽  
Simulation Results ◽  
The Cross

At engineering practice, the theoretical basis for the cross-over method, used to obtain shear wave arrival time in the downhole method of the wave velocity test by surface forward and backward strike, is that the polarity of P-wave keeps the same, while the polarity of S-wave transforms when the direction of strike inverted. However, the characteristics of signals recorded in tests are often found to conflict with this theoretical basis for the cross-over method, namely, the polarity of the P-wave also transforms under the action of surface forward and backward strike. Therefore, 3D finite element numerical simulations were conducted to study the validity of the theoretical basis for the cross-over method. The results show that both shear and compression waves are observed to be in 180° phase difference between horizontal signal traces, consistent with the direction of excitation generated by reversed impulse. Furthermore, numerical simulation results prove to be reliable by the analytic solution; it shows that the theoretical basis for the cross-over method applied to the downhole wave velocity test is improper. In meanwhile, numerical simulations reveal the factors (inclining excitation, geophone deflection, inclination, and background noise) that may cause the polarity of the P-wave not to reverse under surface forward and backward strike. Then, as to reduce the influence factors, we propose a method for the downhole wave velocity test under surface strike, the time difference of arrival is based between source peak and response peak, and numerical simulation results show that the S-wave velocity by this method is close to the theoretical S-wave velocity of soil.

scholarly journals Research on prediction method of S-wave velocity based on deep learning

2021 ◽  
Vol 2083 (4) ◽  
pp. 042065
Author(s):  
Guojie Yang ◽  
Shuhua Wang
Keyword(s):  
Deep Learning ◽  
Wave Velocity ◽  
Prediction Method ◽  
P Wave ◽  
Learning Technology ◽  
S Wave ◽  
Advantages And Disadvantages ◽  
Wave Prediction ◽  
Wide Range ◽  
Velocity Prediction

Abstract Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.

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