scholarly journals A P-wave velocity model of the upper crust of the Sannio region (Southern Apennines, Italy)

1998 ◽  
Vol 41 (4) ◽  
Author(s):  
G. Iannaccone ◽  
L. Improta ◽  
P. Capuano ◽  
A. Zollo ◽  
G. Biella ◽  
...  

This paper describes the results of a seismic refraction profile conducted in October 1992 in the Sannio region, Southern Italy, to obtain a detailed P-wave velocity model of the upper crust. The profile, 75 km long, extended parallel to the Apenninic chain in a region frequently damaged in historical time by strong earthquakes. Six shots were fired at five sites and recorded by a number of seismic stations ranging from 41 to 71 with a spacing of 1-2 km along the recording line. We used a two-dimensional raytracing technique to model travel times and amplitudes of first and second arrivals. The obtained P-wave velocity model has a shallow structure with strong lateral variations in the southern portion of the profile. Near surface sediments of the Tertiary age are characterized by seismic velocities in the 3.0-4.1 km/s range. In the northern part of the profile these deposits overlie a layer with a velocity of 4.8 km/s that has been interpreted as a Mesozoic sedimentary succession. A high velocity body, corresponding to the limestones of the Western Carbonate Platform with a velocity of 6 km/s, characterizes the southernmost part of the profile at shallow depths. At a depth of about 4 km the model becomes laterally homogeneous showing a continuous layer with a thickness in the 3-4 km range and a velocity of 6 km/s corresponding to the Meso-Cenozoic limestone succession of the Apulia Carbonate Platform. This platform appears to be layered, as indicated by an increase in seismic velocity from 6 to 6.7 km/s at depths in the 6-8 km range, that has been interpreted as a lithological transition from limestones to Triassic dolomites and anhydrites of the Burano formation. A lower P-wave velocity of about 5.0-5.5 km/s is hypothesized at the bottom of the Apulia Platform at depths ranging from 10 km down to 12.5 km; these low velocities could be related to Permo-Triassic siliciclastic deposits of the Verrucano sequence drilled at the bottom of the Apulia Platform in the Apulia Foreland.

Geophysics ◽  
1995 ◽  
Vol 60 (4) ◽  
pp. 1178-1186 ◽  
Author(s):  
M. Reza Daneshvar ◽  
Clarence S. Clay ◽  
Martha K. Savage

We have developed a method of processing seismic signals generated by microearthquakes to image local subsurface structure beneath a recording station. This technique uses the autocorrelation of the vertically traveling earthquake signals to generate pseudoreflection seismograms that can be interpreted for subsurface structure. Processed pseudoreflection data, from microearthquakes recorded in the island of Hawaii, show consistent reflectivity patterns that are interpreted as near‐surface horizontal features. Forward modeling of the pseudoreflection data results in a P‐wave velocity model that shows reasonable agreement with the velocity model derived from a refraction study in the region. Usable signal‐to‐noise ratio is obtained down to 2 s. A shear‐wave velocity model was also generated by applying this technique to horizontal component data.


Solid Earth ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 1989-2000 ◽  
Author(s):  
Ines Dumke ◽  
Christian Berndt

Abstract. Measurements of seismic velocity as a function of depth are generally restricted to borehole locations and are therefore sparse in the world's oceans. Consequently, in the absence of measurements or suitable seismic data, studies requiring knowledge of seismic velocities often obtain these from simple empirical relationships. However, empirically derived velocities may be inaccurate, as they are typically limited to certain geological settings, and other parameters potentially influencing seismic velocities, such as depth to basement, crustal age, or heat flow, are not taken into account. Here, we present a machine learning approach to predict the overall trend of seismic P-wave velocity (vp) as a function of depth (z) for any marine location. Based on a training dataset consisting of vp(z) data from 333 boreholes and 38 geological and spatial predictors obtained from publicly available global datasets, a prediction model was created using the random forests method. In 60 % of the tested locations, the predicted seismic velocities were superior to those calculated empirically. The results indicate a promising potential for global prediction of vp(z) data, which will allow the improvement of geophysical models in areas lacking first-hand velocity data.


1982 ◽  
Vol 19 (8) ◽  
pp. 1535-1547 ◽  
Author(s):  
C. Wright

Seismological experiments have been undertaken at a test site near Chalk River, Ontario that consists of crystalline rocks covered by glacial sediments. Near-surface P and S wave velocity and amplitude variations have been measured along profiles less than 2 km in length. The P and S wave velocities were generally in the range 4.5–5.6 and 2.9–3.2 km/s, respectively. These results are consistent with propagation through fractured gneiss and monzonite, which form the bulk of the rock body. The P wave velocity falls below 5.0 km/s in a region where there is a major fault and in an area of high electrical conductivity; such velocity minima are therefore associated with fracture systems. For some paths, the P and 5 wave velocities were in the ranges 6.2–6.6 and 3.7–4.1 km/s, respectively, showing the presence of thin sheets of gabbro. Temporal changes in P travel times of up to 1.4% over a 12 h period were observed where the sediment cover was thickest. The cause may be changes in the water table. The absence of polarized SH arrivals from specially designed shear wave sources indicates the inhomogeneity of the test site. A Q value of 243 ± 53 for P waves was derived over one relatively homogeneous profile of about 600 m length. P wave velocity minima measured between depths of 25 and 250 m in a borehole correlate well with the distribution of fractures inferred from optical examination of borehole cores, laboratory measurements of seismic velocities, and tube wave studies.


2019 ◽  
Author(s):  
Ines Dumke ◽  
Christian Berndt

Abstract. Measurements of seismic velocity as a function of depth are generally restricted to borehole locations and are therefore sparse in the world's oceans. Consequently, in the absence of measurements or suitable seismic data, studies requiring knowledge of seismic velocities often obtain these from simple empirical relationships. However, empirically derived velocities may be inaccurate, as they are typically limited to certain geological settings, and other parameters potentially influencing seismic velocities, such as depth to basement, crustal age, or heatflow, are not taken into account. Here, we present a machine learning approach to predict seismic p-wave velocity (vp) as a function of depth (z) for any marine location. Based on a training dataset consisting of vp(z) data from 333 boreholes and 38 geological and spatial predictors obtained from publically available global datasets, a prediction model was created using the Random Forests method. In 60 % of the tested locations, the predicted seismic velocities were superior to those calculated empirically. The results indicate a promising potential for global prediction of vp(z) data, which will allow improving geophysical models in areas lacking first-hand velocity data.


Geophysics ◽  
1979 ◽  
Vol 44 (5) ◽  
pp. 918-936 ◽  
Author(s):  
Franklyn K. Levin

When a sedimentary earth section is layered on a scale much finer than the wavelength of seismic waves, the waves average the physical properties of the layers; a seismic wave acts as if it were traveling in a single, transversely isotropic solid. We compute the velocities with which P‐waves, SV‐waves, and SH‐waves travel in transversely isotropic solids formed from two‐component solids and find the corresponding moveout velocities from [Formula: see text] plots. The combinations studied are sandstone and shale, shale and limestone, water sand and gas sand, and gypsum and unconsolidated material, one set of typical physical properties being selected for each component of a combination. A reflector at 1524 m and a geophone spread of 0–3048 m are assumed. The moveout velocity for an SH‐wave is always the velocity for a wave traveling in the horizontal direction. The P‐wave moveout velocity found from surface seismic data can be anywhere from the vertical P‐wave velocity to values between those for vertical and horizontal travel; the actual value depends on the elastic parameters and the spread length used for velocity determination. If the two components of the solid have the same Poisson’s ratio, the velocity from surface‐recorded data is the vertical P‐wave velocity. For this case, SH‐wave anisotropy can be computed. SV‐wave data usually do not have hyperbolic time‐distance curves, and the moveout velocity found varies with spread length. Surprisingly, the water sand‐gas sand combination gives a medium with negligible anistropy. A two‐component combination of gypsum in weathered material gives rise to [Formula: see text] plots that seem to explain the unusual behavior of near‐surface SV‐waves seen in field studies reported by Jolly (1956).


Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 345-351 ◽  
Author(s):  
Geoff J.M. Moret ◽  
William P. Clement ◽  
Michael D. Knoll ◽  
Warren Barrash

P‐wave velocity information obtained from vertical seismic profiles (VSPs) can be useful in imaging subsurface structure, either by directly detecting changes in the subsurface or as an aid to the interpretation of seismic reflection data. In the shallow subsurface, P‐wave velocity can change by nearly an order of magnitude over a short distance, so curved rays are needed to accurately model VSP traveltimes. We used a curved‐ray inversion to estimate the velocity profile and the discrepancy principle to estimate the data noise level and to choose the optimum regularization parameter. The curved‐ray routine performed better than a straight‐ray inversion for synthetic models containing high‐velocity contrasts. The application of the inversion to field data produced a velocity model that agreed well with prior information. These results show that curved‐ray inversion should be used to obtain velocity information from VSPs in the shallow subsurface.


1984 ◽  
Vol 74 (4) ◽  
pp. 1263-1274
Author(s):  
Lawrence H. Jaksha ◽  
David H. Evans

Abstract A velocity model of the crust in northwestern New Mexico has been constructed from an interpretation of direct, refracted, and reflected seismic waves. The model suggests a sedimentary section about 3 km thick with an average P-wave velocity of 3.6 km/sec. The crystalline upper crust is 28 km thick and has a P-wave velocity of 6.1 km/sec. The lower crust below the Conrad discontinuity has an average P-wave velocity of about 7.0 km/sec and a thickness near 17 km. Some evidence suggests that velocity in both the upper and lower crust increases with depth. The P-wave velocity in the uppermost mantle is 7.95 ± 0.15 km/sec. The total crustal thickness near Farmington, New Mexico, is about 48 km (datum = 1.6 km above sea level), and there is evidence for crustal thinning to the southeast.


Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Yuzhu Liu ◽  
Xinquan Huang ◽  
Jizhong Yang ◽  
Xueyi Liu ◽  
Bin Li ◽  
...  

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.


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