Interval Velocity Research Articles

Evaluation of top seal capacity by pseudo-capillary pressure model in Suriname offshore Basin

Drilled well data and 3D seismic data are integrated to model the seal capacity of the Late Campanian- Maastrichtian mudstone in the exploration Block 52 operated by PETRONAS Suriname Exploration & Production BV. The study proposes simple relationships between interval velocity and porosity, porosity against permeability, and normal compaction of the mudstone with depth, based on the drilled well data. The most likely model of the top seal capacity suggests a maximum column height to be in the range of 330 – 350 m in the study area for gas. The model also suggests a gradual increase in the top seal risk towards the northeastern direction of the study area which may be due to the effect of sediment fairway from the nearby Demerara High. On the other hand, an increasing trend of top seal capacity towards the southwestern side of the block relates to the possible effect of the thicker deposition of transgressive fine-grained package with more compaction resulting in a decreasing trend of porosity and permeability of the mudstone.

Seismic Inversion Techniques and Attribute Analysis for Accurate Well Placement in Offshore Niger Delta Basin, Nigeria

Before this study, simple methods like seismic attribute analysis have often been used for reservoir characterization with successes however, there is still the need to reduce exploration uncertainty to a negligible level and boost investors’ confidence. This study integrated seismic inversion with seismic attribute analysis to better characterize the reservoirs in MTW Field in deep-offshore Niger Delta Basin. Five (5) wells with complete suite of petrophysical logs, three-Dimensional (3-D) seismic data, checkshot and other well information were used. The well data were thoroughly quality checked, reservoirs were litho-stratigraphically delineated and used for petrophysical analysis across the wells. This was followed by seismic-to- well-tie, seismic interpretation, and seismic attribute analysis (Root Mean Square-RMS) generated using depth surface maps. 3-D static reservoir model and volumetric evaluation were carried out. Petrophysical properties were derived and distributed across the 3-D static model using sequential gaussian simulation algorithm to ascertain shale volume spread across the model. To improve the seismic resolution and reduce interpretation uncertainty at greater depth, model- based post-stack seismic inversion was performed to obtain acoustic impedance cube. Litho-stratigraphic and petrophysical analysis result revealed five reservoir sands (A, B, C, D, and E). Reservoir-A was seen to be more viable with a thickness of 13.42 m, high effective porosity of 27%, permeability of 3187.53 mD, low water saturation of 34% and low shale volume of 11% which are indication good reservoir quality and producibility. The seismic interpretation revealed thirty-one (31) growth and antithetic faults oriented in the NE-SW and NW-SE directions, respectively. The RMS result revealed high amplitude reflectivity which is a measure of zone of interest. Based on this, seven (7) prospects and three (3) leads were identified. The seismic inversion result shows a high level of accuracy with a correlation coefficient of 0.997; 0.997; 0.995; and 0.996 in MTW-001, MTW-003ST1, MTW-004ST1 and MTW-005 wells, respectively. The acoustic impedance successfully resolved and improved on the resolution of the seismic stacking velocity especially at reservoir layers and at depth deeper than 3600 ms. Acoustic impedance as a layer property has improved on the lateral and vertical resolutions of the data beyond what the usual seismic interval velocity could image thus, validating some of the prospects and leads identified in this study. This demonstrated that uncertainty can be reduced by a blend of RMS and seismic inversion in identifying reservoir for accurate placement of wells. It is therefore recommended that E and P operators should adopt the technique in their future hydrocarbon exploration endeavor in frontier and matured basins.

Automatic stack velocity picking using a semi‐supervised ensemble learning method

AbstractPicking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method.

Second-order optimization for multiparameter reflection waveform inversion in acoustic VTI media

SUMMARY The seismic wave velocity in subsurface formations typically varies both spatially and directionally, resulting in a heterogeneous and anisotropic medium. To successfully image complex structures and potential reservoirs using multichannel seismic data, high-resolution interval velocity models are required. However, most controlled-source seismic recordings lack wide-angle signals, such as diving and refracted waves that propagate through deep formations. Consequently, geophysicists rely on the traveltimes and waveforms of reflections or back-scatterings in finite-frequency and finite-offset seismograms to estimate velocity and account for possible anisotropy. Recently, wave-equation based reflection waveform inversion (RWI) has emerged as an active research topic due to its ability to recover intermediate-wavelength model components that cannot be retrieved in reflection traveltime tomography. Nevertheless, the widely used gradient-type local optimization suffers from multiparameter trade-off and slow convergence of RWI in anisotropic media. Analysis of the Fréchet derivative reveals that the sensitivity of reflections to changes in long-to-intermediate wavelengths of the anisotropic velocities is jointly controlled by the specular reflection on the interface and the radiation pattern of scattered fields concerning the model parameters along the wave paths. Anatomy of the approximate Hessian reveals characteristics of parameter coupling and spatial resolution in the context of multiparameter RWI, inspiring the development of a matrix-free Gauss–Newton algorithm based on the second-order adjoint-state method for large-scale applications. Synthetic and real data examples demonstrate that the proposed approach can appropriately handle overburden heterogeneities and anisotropies, and thus improve imaging of underlying complex structures such as dipping and small-scale faults in the deep parts.

Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning

The unreliable prediction of the low-frequency components from inverted acoustic impedance causes uncertainty in quantitative seismic interpretation. To address this issue, we first calculate various seismic and geological attributes that contain low-frequency information, such as relative geological age, interval velocity, and integrated instantaneous amplitude. Then, we develop a method to predict the low-frequency content of seismic data using these attributes, their high-frequency components, and recurrent neural networks. Next, we test how to predict the low-frequency components using stacking velocity obtained from velocity analysis. Using all the attributes and seismic data, we propose a supervised deep learning method to predict the low-frequency components of the inverted acoustic impedance. The results obtained in both synthetic and real data cases show that the proposed method can improve the prediction accuracy of the low-frequency components of the inverted acoustic impedance, with the best improvement in a real data example of 57.7% compared with the impedance predicted using well-log interpolation.

Evaluation of the Possibilities Validation of Interval Velocity Models Using Non-Seismic Data and Its Impact on Geological Interpretation of PreSDM Results

The paper presents the problem of generation and validation of Velocity Interval Depth (VID) models with the application of non-seismic geophysical and geological data. The study area is a part of the Carpathian Foredeep located close to its contact with the Carpathian Overthrust. In this area of complicated geological structure, hydrocarbon deposits have been successfully explored for decades with seismic methods and drilling. The research applied the Simultaneous Joint Inversion (SJI) of independent geophysical data, which is a modern methodology of geophysical data processing, that is still under development. Such an attempt was necessary due to the lack of a sufficiently dense grid of wells in the study area, in which seismic velocities would be correctly recorded. Such data would be then applied for the generation of relevant VID models, which in turn, could be used to perform the Prestack Depth Migration (PreSDM) procedures. The application of procedures taking advantage of independent geophysical and geological data enabled researchers to control the generation process of the spatial VID model in the areas without wells. The analyses aimed to verify the correctness of VID model evaluation and its influence on the quality of seismic imaging in the area of the Carpathian Overthrust. Precisely, the influence was tested of such non-standard generation procedure of seismic velocity fields, not only on the PreSDM results but also on the geological interpretation of both the Rączyna and the Jodłówka gas deposits. The latter aspect of the presented results seems to be crucial to the effectiveness of petroleum exploration in the transition zone between the Carpathian Orogen and the Carpathian Foredeep.

Characteristics of cavity dynamics, forces, and trajectories on vertical water entries with two spheres side-by-side

In the present work, a three-dimensional numerical model with six-degrees-of-freedom is adopted to carry out the investigation of twin water entries with two spheres side-by-side. The shear-stress transport k–ω model is used to detail the turbulence structure, and the air–water interface is tracked through the volume of fluid method. The motion of consecutive water entry with spheres is simulated using an independent overset grid technology. Numerical results reveal the variation of different kinds of lateral forces experienced by the trailing sphere following water entry, while simultaneously clarifying the governing law regarding the effects of time interval, lateral distance, and entry velocity on the trajectory behaviors. The repulsive and attractive forces are found upon the trailing sphere (sphere II) entering water, inducing different deviation directions, which are affected by the time interval and lateral distance. In addition, increasing water-entry velocity results in an increase in both attractive and repulsive force acted on sphere II; notwithstanding, the effect of repulsive force on trajectory characteristics is more noticeable.

Kinematic mechanism of the rehabilitative effect of 4-channel NMES: post-hoc analysis of a prospective randomized controlled study

The sequential 4-channel neuromuscular electrical stimulation (NMES), based on the normal contractile sequences of swallowing-related muscles, is a new rehabilitative treatment. The objective of this study was to explore the mechanism of the rehabilitative effect of the 4-channel NMES using kinematic analysis of videofluoroscopic swallowing study (VFSS) data. For this post-hoc analysis, we included a subset of participants from the prospective randomized controlled study on the clinical effectiveness of the sequential 4-channel NMES compared with that of the conventional 2-channel NMES. Seventeen subjects (11 and six in the 4- and 2-channel NMES groups, respectively) were eligible for the kinematic analysis of VFSS data. The hyoid bone movement was analyzed by evaluating the distance and time parameters with four peak points (A, B, C, D). The 4-channel NMES group showed significant improvement in vertical distances (A–C), horizontal distance (A–B, A–C), time interval (A–B–C) and total time, compared with their pretreatment data. The 2-channel NMES group showed significant improvements in time interval (A–B); however, the Euclidean distance (A–D) and mean velocity of the Euclidean distance (A-C) were significantly decreased. When the two groups were directly compared, the 4-channel group showed significantly greater improvement in horizontal distance (A–B), Euclidean distance (A–D), time interval (A–B–C), and mean velocity the Euclidean distance (A–D). The results in this study suggest that the sequential 4-channel NMES might lead to the physiologic circular movement of the hyoid bone during swallowing, and therefore be an effective treatment for dysphagia.Trial registration: Clinicaltrials.gov, registration number: NCT03670498.

Pre-Stack Imaging for the Messinian Sub Salt Sequences in the Levantine Basin of the East Mediterranean: A Case Study, Offshore Lebanon

The Mediterranean Sea has a progressive geological significance; it is considered a vast extensive promising basin for gas exploration. Herodotus and Levantine basins are the main basins in the eastern Mediterranean basin, which are similar in their evolution stages and accommodate similar sedimentary sequences. The recent geophysical and petrophysical studies proved that Herodotus and Levantine basins are promising for gas with possible great reserves exceeding 2,000 bcm. Recent discoveries and increased exploration activities including seismic acquisition and offshore drilling made the Eastern Mediterranean a hot area for gas exploration, especially after the discovery of Zohr gas Field in 2015, offshore Egypt. However, the regional extension of the complex Messinian salt beds and domes make the exploration of high risk. The subsalt seismic imaging in a time domain and the resolution of the underlying carbonate/clastic reservoirs' primary reflections are a big challenge in this area. Therefore, the main objective of the present study is to improve sub-salt imaging with much more accurate interval velocity estimation. In the study area, offshore Lebanon, the delineated structures in the sub-salt imaging after the Pre-stack Depth Imaging were more significant compared to the Pre-Stack Time Imaging which provided optimum sub-salt imaging and enabled the accurate interpretation of the main geologic features which may increase the certainty level of the hydrocarbon exploration and discoveries.

Seismic attribute assisted analysis of the interpretational variations in the time and depth migrated datasets: An example from Taranaki Basin, New Zealand

In the regions with complex geology together with the apparent stratigraphic and seismic sequence events, time migration algorithms do not provide high-quality imaging for seismic interpretation due to strong lateral velocity contrasts, usage of average velocities in the vertical and horizontal directions, and without defining the ray-bending at interfaces. Therefore, the depth migration method using interval velocities and bended ray-traces at interfaces, provides more accurate results. There are also interpretational differences between the time and depth-migrated seismic data. The objective of this research is to compare these differences using several seismic attributes. In this context, we used the Toro 3-D pre-stack time and pre-stack depth migrated datasets from the Taranaki Basin, offshore New Zealand, because the Giant Foresets Formation presents suitable examples for the different types of channels like distributary, sinuous and meandering channels. Comparisons indicate that dip angles of the faults increased, channel walls become steeper and indicate more curvature, and structures get narrower in the depth-migrated data. There are also amplitude and phase variations in and around the channels. Most of the seismic data in the world are in the time domain and there is no sufficient borehole and velocity data. This situation may affect the attribute calculations in both migration data sets. The main goal of this investigation is to provide an understanding about the possible seismic differences in the time and depth migrations, the reasons behind them and, relationship between the lithology and probable seismic responses by using geometrical and physical attributes used in the seismic industry. These steps allow an accurate interpretation and more reliable evaluation of the subsurface structures. This study will also provide a toolkit and guidance for the interpreters in the time and depth migrations.

Shear-wave velocity estimation based on rock physics modelling of a limestone gas reservoir in the Pannonian Basin

In the Pannonian Basin, especially in Croatia, there are a small number of wells for which acquired shear-wave (S-wave) velocities have been obtained. Often, quantitative interpretation relies only on compressional-wave (P-wave) velocity data, and S-wave velocity must be modelled. S-wave velocity estimation in combination with other petrophysical data is essential for detailed reservoir characterization. P- and S-wave velocities enable seismic modelling of different saturation states in a reservoir. This paper demonstrates a workflow for S-wave velocity estimation where S-wave velocity data are absent in the gas field and neighbouring fields with the same lithology, based on the Kuster–Toksöz and Xu–Payne models applied to the Pannonian Basin limestone reservoir. The results are calibrated with the P-wave velocity obtained from borehole data and the vertical seismic profile (VSP) S-wave interval velocity. Although rock physics models are idealized analogues of real rocks, a very good correlation was obtained between the modelled and measured P-wave velocity, as well as between the modelled S-wave velocity and the VSP interval velocity. The study also illustrates the problem of defining the pore aspect ratio in zones of increasing shale content. Due to the limited research on the limestones of the Pannonian Basin, these results enable a better understanding of the seismic parameters of the Pannonian Basin limestones. The results indicate that the proposed workflow gives an adequate estimation of S-wave velocities.

Automatic velocity analysis with physics-constrained optimal surface picking

Stacking velocity is generally obtained by picking the energy peaks in velocity semblance which can be time consuming when performed manually. Numerous automatic methods have been proposed for accelerating the velocity picking but often generate physically unreasonable picking results when strong and spatially consistent energy anomalies (due to noise and multiples) appear in the semblance map. We develop a constrained optimal surface picking method to automatically pick a 2D velocity field from a 3D semblance volume with high efficiency and robustness. This method is improved from the 2D dynamic programming algorithm by incorporating vertical physical constraints in the time direction and lateral smoothness constraints in the common-midpoint (CMP) direction. The time-direction physical constraint ensures that the picked velocity is positive when converted to an interval velocity, whereas the CMP-direction smoothness constraint ensures that the picked 2D velocity field is laterally continuous. Tests on the Marmousi-2 model indicate that our constrained optimal surface picking algorithm improves the spatial structure consistency of the picked velocity field and is able to robustly avoid picking the strong and consistent energy features generated in the semblance volume by multiples. We further determine the robustness of our method in a 2D real data example by comparing our automatic picking from a 3D semblance volume with a result that is manually picked from individual 2D semblance slices. The comparison indicates that the general trend of our result is consistent with the manual picking and our result looks geologically more reasonable in detail and generates a better stacking image with improved, focused, and more continuous reflections.

Multi-window spectrum analysis of Búzios oil field PSDM seismic data applied to frequency attenuation study

The intrinsic attenuation is often quantified using the inverse quality factor Q. Methods to estimate Q are mainly based on time domain or frequency domain and sensitive to amplitude and/or frequency band variations. In this sense, noise, signal artifacts or processing steps can alter the amplitude, frequency content and the waveform, what may lead to inaccurate calculations of Q. A way to better understand the frequency band variation in seismic data and verify patterns or problems occurring in the signal frequency band is performing layered or window-by-window spectrum analysis. This work describes a multi-window spectrum analysis method used to better understand the frequency attenuation process as the seismic wave travels through the sediments from the sea bottom down to the reservoir depths in a 3D Kirchhoff Pre-Stack Depth Migrated (PSDM) seismic data from Búzios oil field. To perform the spectral analysis in frequency domain, the PSDM volume was converted to time domain using the same interval velocity volume used to depth-migrate the data. The variation of frequency band as the signal propagates through the sediment package was analyzed dividing the studied crossline sections in 13 layers of 200 ms each, from sea bottom down to the base of pre-salt reservoir formations. The results suggest that most part of higher frequency loss has been occurred in post-salt package. This may be an indication of significant viscoelastic behavior or strong stratigraphic filtering of this interval. Sequentially, higher frequency content is lost slowly from salt top down to the last layer located inside pre-salt formation. Very slight frequency spectrum variation below salt top indicates elastic behavior of the salt formation and underling pre-salt formation in this interval. The spectrum analysis method proposed here has shown to be useful and efficient to identify intervals of different attenuation patterns in seismic data. Additionally, the frequency band study produced by our spectrum analysis can be used to quality-control Q-factor estimation computed by different methods.

Seismic identification of carbonate reservoir sweet spots using unsupervised machine learning: A case study from Brazil deep water Aptian pre-salt data

The pre-salt high-yield carbonate reservoirs of Brazil are responsible for about 75% of national oil production. Still, seventeen years since its discovery, detailed seismic characterization of these reservoirs remains challenging, with producing and nonproducing lithologies often exhibiting the same amplitude response. Those reservoirs are complex, composed of high productivity lacustrine carbonates (stromatolites and grainstones), mixed with non-producing mudstones, clays, and igneous intrusions. To address this heterogeneity, we evaluate unsupervised machine learning clustering techniques including Self Organizing Maps (SOM), Generative Topographic Maps (GTM), and K-means and apply them to a suite of instantaneous, geometric, interval velocity, and AVO seismic attributes. We use both static and dynamic reservoir data from 13 wells at the Mero field that penetrated lacustrine carbonates from Barra Velha formation to validate the accuracy of our unsupervised learning models. These wells are excellent blind tests as they were not used for any algorithm training. In this pioneer work, the first done using machine learning in the Mero field, we find that SOM, GTM, and K-means models provide clusters that can be linked to sweet spots associated with fracturing, karstification, and hydrothermal alteration. Additionally, clusters related to sill intrusions, platform facies and distal fine grained non-reservoirs were identified. The results can be extrapolated to the adjacent Central Libra Area (still in exploration phase), where we have identified the same clusters of the sweet spots reservoirs in Mero Field and, thus, they can be considered as future targets. For accuracy assessment, the same attribute combination SOM clustering was performed in another Petrobras’ pre-salt confidential dataset and the results were satisfactory. So, for exploration projects optimization purposes, the new proposed workflows can also be applied to other areas with geological similarity to the Mero Field.

Inversion of seismic data to modeling the Interval Velocity in an Oilfield of SW Iran

Seismic velocity is considered the best attribute related to formation pressure changes. Integrating seismic attributes and well-logging data through seismic inversion predicts the reservoir characteristics across the field with the highest accuracy. This study especially presents seismic velocity for the whole south Azadegan Field in SW Iran for carbonate formations. The considered dataset includes 3D seismic data, vertical seismic profiling (VSP), logging data of 23 wells, and geological information. Here, we estimated the interval velocity using post-stack migration velocity, seismic inversion, and the relationship between the acoustic impedance (AI) model and the sonic log to predict formation pressure. As a result, the correlation coefficient of 0.71 and a high inversion accuracy (8.76% relative error) is concluded. The actual and predicted P-wave (Vp) correlation coefficient is calculated as 0.74 and all sevens as 0.79 using an AI seismic attribute. Thus, the estimated Vp agrees with the original well-log values. Inverted AI cubes in the deeper formations of the field are about 8000-15000 [(m/s)*(g/cm3)], which could be referred to as calcareous formations. The correlation of the Vp cube resulting from the Sequential Gaussian simulation (SGS) considering co-kriging with the AI, with the initial velocity cube using the inverse distance weighted (IDW) method being 0.54 is more than the same method applied with interval migration velocity trend in co-kriging. The anisotropy of the final Vp cube for the vertical variogram range is 96m, and for major and minor directions is 11850 m.

Integrated Analysis of the 3D Geostress and 1D Geomechanics of an Exploration Well in a New Gas Field

The aim of this study was to propose the workflow for integrated analysis of the 3D geostress and 1D geomechanics of an exploration in a new gas field. This integrated analysis will allow for problems associated with the inaccuracy of 1D geomechanical analysis to be overcome in a region with obvious anticline/syncline structures. The 1D geomechanical analysis of the well in the exploration of a new gas field mainly included the prediction of pore pressure and calculation of the mud weight window for safe drilling. In general, this integrated workflow included both a method for pore pressure prediction and a method for the calculation of the mud weight window, with the numerical solution of 3D geostress plus the interval velocity of formations. The procedure for the calculation of the 3D geostress of a target block was also introduced. Numerical solution of the 3D geostress of the target gas field, as well as solutions of 1D geomechanical analysis, have demonstrated the efficiency and practical use of the proposed theory in the successful drilling of the LT-1 well in the Junggar Basin, Xinjiang, China. For this LT-1 well in the target TS block, there was no logging data to refer to when predicting the pore pressure of undrilled formations. Only 3D geostress could be used to calculate the mud weight window. Influences of anticline structures were considered in the calculation of 3D geostress. Since the accuracy of the numerical solution of 3D geostress is higher than the accuracy of the 1D geostress solution for a single well analysis, the results of pore pressure and the mud weight window are more accurate than those obtained with conventional 1D geostress analysis. Details of the finite element modeling of the 3D geostress field of the TS block is presented along with the solution of the 3D geostress field. With the data of the interval velocity of formations and 3D geostress solution of the TS block, pore pressure prediction was carried out for the 7000 m-deep pilot LT-1 well. Finally, calculations were performed for the values of the mud weight window of the LT-1 well.

Hierarchical transfer learning for deep learning velocity model building

Deep learning is a promising approach to velocity model building because it has the potential of processing large seismic surveys with minimal resources. By leveraging large quantities of model-gather pairs, neural networks (NNs) can automatically map data to the model space, directly providing a solution to the inverse problem. Such mapping requires big data, which proves prohibitive for 2D and 3D surveys of realistic size. We have developed a transfer learning (TL) strategy. A network is first trained on a smaller subproblem, which then becomes the starting solution to a larger, more difficult data set, akin to the hierarchical multiscale strategy for full-waveform inversion. We perform TL by having subobjectives that escalate in complexity and by first training an NN at estimating horizontally layered velocity models and then proceeding to train an augmented network at estimating 2D dipping layered models. TL improves convergence and allows using a lesser quantity of 2D models for training. For synthetic tests, the structural similarity index measure of 2D interval velocity models in the time domain is [Formula: see text] and the root-mean-square (rms) error is [Formula: see text] m/s. We benchmark our algorithm on the Marmousi2 model and observe that our method can apply to velocity models with continuous deformed layers with dips up to 35°. We benchmark our algorithm on 2D marine field data and produce an rms velocity model that leads to coherent stacking and a time interval velocity model that reproduces salient features of the stacked section. TL expedites and regularizes training and data-driven techniques may be applied to field data with minimal preprocessing even though we lack real target velocity models.

Interval Velocity Estimation for Unmanned Underwater Vehicles

This letter addresses the problem of interval velocity estimation for unmanned underwater vehicles subject to multiple model uncertainties. We extend our previous works to the case of large initial bounds on uncertainties by introducing an adaptive mechanism. For improving the accuracy of interval estimation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> approach is used in the design of interval observer to attenuate the effect of uncertainties. Sufficient design conditions are formulated as a set of linear matrix inequalities. Simulation results demonstrate the effectiveness of the proposed method.

Development of a numerical algorithm for solving the problem of processing and determining the basic velocity model

This work is devoted to development of an automatic algorithm for determining the velocity model from the data of the field seismic profile. First of all total energy of received signal and the positions of the first arrivals of reflected waves has to be determined. The traces sort by common depth point and the spectra of possible velocities determine at each point of the trace and each assembly of the common deep point. The procedure is fully automated and as a result, instead of about 150 points of manual processing, 4 million points are obtained on the test profile. The values ​​of chosen effective speeds correspond not only to existing criteria for their determination, but also to requirement of smoothness in first derivative. This makes it possible to determine reliable interval velocity profile at each track point and each collection of the common depth point. Speed ​​determination accuracy in the range of 10-50 m/s. After recalculating the time profile into a depth profile, it becomes possible to obtain high-resolution and detailed model of the velocity structure of the geological profile during migration transformations. An iteration process refines the calculations obtained by geologists and geophysicists in order to obtain consistent and consistent results.

Combining SH- and P-wave seismic reflection survey to support seismic response analysis. A case study from Cavezzo (Italy) after the 2012 Emilia earthquake

In some geological settings, such as thick sedimentary basins, the deeper layers may also significantly contribute to the amplification of the ground motion during an earthquake, particularly at low frequencies. To properly quantify the influence of the deeper geology on the seismic ground motion at the surface, the use of indirect geophysical exploration techniques is particularly suitable, as it can produce realistic representations of the subsoil structure over a large range of depths. This paper focuses on the acquisition and processing of combined shear- (S) and compressional-wave (P) seismic reflection data to obtain an accurate geological/geophysical model with reliable seismic velocities for local-scale seismic site response studies, as needed for hazard assessment and calculation of earthquake scenarios. As an application case, we focus on a microzonation study performed for the Cavezzo municipality in Northern Italy. The investigated site is located in the epicentral area of the 2012 Emilia earthquake in the Po Valley, which is a typical deep sediment-filled basin. Specifically, in this study we were able to: (1) confirm the advantages of reflection seismic prospecting for soft-sediment basin characterization; (2) evaluate the resolution of S- and P-wave signals and their depth of penetration; (3) improve the knowledge on the geological structure of the Cavezzo area by measuring S- and P-wave interval velocities in Plio-Quaternary formations.