Conventional Interpretation Techniques Research Articles

A Computational Platform for Automatic Signal Processing for Bender Element Sensors

The small strain shear modulus is an important characteristic of geomaterials that can be measured experimentally using piezoelectric sensors (bender elements). However, most conventional signal interpretation techniques are based on the visual observation of the output signal and therefore inherently subjective. Objective techniques also exist, like the cross-correlation of the input and output signals, but they lack physical insight, as they rely on the (incorrect) assumption that input and output signals are similar. This paper presents GeoHyTE, the first objective and physically consistent toolbox for the automatic processing of the output signal of bender element sensors. GeoHyTE updates a finite element model of the experiment, iteratively searching for the small strain shear modulus that maximises the correlation between the experimental and numerical output signals. The method is objective, as the results do not depend on the experience of the user, and physically consistent, as the wave propagation process is modelled in full and signals of the same nature (output) are correlated. Moreover, GeoHyTE is nearly insensitive to grossly erroneous input by the user, both in terms of the starting point of the iterative maximisation process and refinement of the finite element model. The results obtained with GeoHyTE are validated against benchmark measurements reported in the literature and experimental data obtained by the authors. A detailed statistical analysis of the results obtained with GeoHyTE and conventional interpretation techniques is also presented.

AI FOR INFANT WELL-BEING: ADVANCED TECHNIQUES IN CRY INTERPRETATION AND MONITORING

In order to improve the welfare of newborns, this study investigates the use of sound-recognition-based artificial intelligence (AI) approaches to the interpretation and monitoring of infant screams. Crying has long been a problem because it is the primary means of communication between infants and caregivers. The limitations of conventional interpretation techniques are discussed. These limitations include the subjective nature of interpretation and the inability to detect subtle variations in crying patterns. The goal of the research is to categorize crying patterns based on the cries of male and female infants and identify noises that are a sign of distress. The study utilized the Mel Frequency Cepstral Coefficients (MFCC) method to extract features from internet-sourced MP3 and WAV audio data. The technique successfully captured the unique qualities of each crying sound using various machine-learning models, including Random Forest and XGBoost. These models outperformed others with accuracy rates of 94.5% and 94.2%, respectively. These findings show how well these algorithms perform in correctly categorizing various newborn cries. The findings of this study establish the platform for possible Internet of Things (IoT) and healthcare framework implementations targeted at supporting parents in caring for their newborns by offering an insightful understanding of the distinctive vocalizations connected with weeping.

Anatomy of fractures: Quantifying fracture geometry utilizing X-ray computed tomography in a chalk-marl reservoir; the Lower Cretaceous Valdemar Field (Danish Central Graben)

Naturally fractured reservoirs tend to show heterogeneous permeability with open fractures enhancing the inherent diagenetic and depositional matrix fabric. Natural open fractures may function as pathways for fluid flow in tight (low matrix permeability) carbonate reservoirs and have a significant impact on the directions, magnitudes and heterogeneities of fluid flow and reservoir rock strength. Open fractures may however also facilitate the invasion of formation water while fractures that are partially or completely cemented can act as barriers to fluid flow. As a result, natural fractures significantly impact reservoir flow rates, anisotropy, storage, connectivity, recovery, and production. X-ray Computed Tomography (CT) offers a non-destructive technique to evaluate petrophysical parameters, such as porosity and permeability, and to assess the geometry, connectivity, and density of natural fractures, which are important constituents of chalk reservoirs. The purpose of this study is to investigate different types of naturally occurring fractures within the Lower Cretaceous succession of the Valdemar Field in the Danish North Sea. Six samples of core displaying at least one natural fracture on the surface are imagined; two samples of open fractures with plumose structure and hackle marks, three samples of shear fractures and finally one sample with deformation bands. The samples are imaged by CT and internal fractures are manually mapped. Image analysis provides detailed information on the fracture geometry in addition it indicates the presence of numerous microfractures hitherto not recorded in the Lower Cretaceous of the Danish North Sea. Core data show that fracture intensity can be significantly greater than calculated by conventional core interpretation techniques based on core surface observations. Further, apertures of both major and minor fractures are measured in CT images of all samples to determine if specific fracture types display characteristic that can distinguish them from other fracture types. As open fracture intensity increases, so too does permeability, likely enhancing reservoir connectivity on the field scale.

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

Tutorial: Petrophysics of Thinly Bedded Formations

This paper was written in response to a personal request from the Editor to contribute a “tutorial” on thin-bed petrophysics. The paper is not intended to be an exhaustive review of all the work that has been done regarding thin-bed petrophysics. Instead, it is an introduction to the different types of methods available, with some discussion on the strengths and weaknesses of each. Thin beds have posed problems for petrophysicists for many decades, mostly because conventional interpretation techniques in thin beds tend to overestimate water saturation, owing to the very low resistivities often seen in hydrocarbon-bearing intervals. There are a number of different techniques available to the petrophysicist for evaluating thin beds, including conventional “bulk volume” techniques, various types of high-resolution modeling, and different low-resolution modeling techniques, both with and without triaxial resistivity measurements. There is no single best technique to use. Instead, the most appropriate method is dependent on the formation complexity and the types of data available. Generally, it is very useful to have triaxial resistivity measurements to understand the formation anisotropy better. However, in some cases, simple “triple-combo” logging suites may be sufficient, provided the interpretation can be improved and verified with core data. In other cases, complex formation modeling is required to provide the best results with low uncertainty. If using either low- or high-resolution modeling, the reporting of evaluation results is not straightforward. Net reservoir and net pay cutoffs require special consideration, and the results are specific to the formation components rather than the bulk volume formation, as they are with conventional reservoir summaries. Furthermore, the quantification of petrophysical uncertainties in formation modeling of thinly bedded formations is better done using scenario modeling rather than Monte Carlo processing, which is commonly applied with bulk volume techniques.

Which seismic attributes are best for subtle fault detection?

Subtle fault detection plays a vital role in reservoir development studies because faults may form baffles or conduits that significantly control how a petroleum reservoir is swept. Small-throw faults are often overlooked in interpreting seismic amplitude data. However, seismic attributes can aid in mapping small faults. Over the years, dozens of seismic attributes have been developed that offer additional features for interpreters with associated caveats. Using the Maui 3D seismic data acquired in the Offshore Taranaki Basin, New Zealand, we have generated seismic attributes that are typically useful for fault detection. We find that multiattribute analysis provides greater geologic information than would be obtained by the analysis of individual attribute volumes. We extract the geologic content of multiple attributes in two ways: interactive corendering of different seismic attributes and the unsupervised machine learning algorithm self-organizing maps (SOM). Corendering seismic attributes that are mathematically independent but geologically interrelated provides a well-integrated structural image. We suggest eight combinations of 16 various attributes useful for a human interpreter with interest in fault and fracture detection. Current interpretation display capabilities constrain corendering to only four attribute volumes. Therefore, we use principal component analysis and SOM techniques to efficiently integrate the geologic information contained within many attributes. This approach gathers the data into one classification volume based on the interrelationships between seismic attributes. We show that our resulting SOM classification volume better highlights small faults that are difficult to image using conventional seismic interpretation techniques. We find that SOM works best when a fault exhibits anomalous features for multiple attributes within the same voxel. However, human interpreters are more adept at recognizing spatial patterns within various attributes and can place them in an appropriate geologic context.

Seismic impedance inversion and attribute analysis for reservoir characterization in an offshore depobelt, Niger Delta, Nigeria

The task of minimizing pitfall in quantitative reservoir assessment and evaluation is an integral aspect of any promising petroleum exploration and evaluation project. The Niger Delta oil province in Nigeria is chiefly composed of several pay zones with many stratigraphic intervals of sand and shale formations in varying proportion. Whereas conventional seismic interpretation technique poses certain degree of uncertainty in reservoir characterization, seismic inversion constraint by log data brings significant improvement in interpretation that reduces uncertainty in the delineation of reservoir characteristics, volume and distribution. In this present study, post-stack seismic inversion and seismic attributes analysis were used to evaluate the potentials and prospects of mapped reservoirs in a field, offshore depobelt, Niger Delta. The implementation of the tasks consists of mapping of structural framework, horizon picking, log editing and correlation, wavelet extraction, building of low-frequency model, acoustic impedance inversion and crossplot analysis of reservoir properties and attributes. A hybrid approach (formulated using Greenberg-Castagna equation) specific for the Niger Delta depobelts was used to estimate and generate shear wave velocity for the wells. Results obtained from inversion depict an acoustic impedance values in the range 150–280 ft/s g/cc and $$V_{\text{P}}/V_{\text{S}}$$ of 1.25–2.50 at a depth range of 9250–9280 ft within the hydrocarbon-bearing sands in the two wells. Region of low acoustic impedance (representing high reservoir porosity) coupled with a corresponding low $$V_{\text{P}}/V_{\text{S}}$$ ratio (indicating hydrocarbon accumulation) is presumed to have high prospects for exploration and well development. Altogether the degree of correlation between the inverted and actual impedance was found to be about 90% across the wells. It is evident that the acoustic impedance slice at 3000 ms depicts low acoustic impedance sand within 18,500–20,000 ft/s g/cc at the northeastern and southeastern regions of the field. High reflection amplitudes (bright spots) signifying hydrocarbon accumulation are also revealed on seismic attribute maps with surrounding shale diaper as the source rock. This study has revealed the prospect (i.e., existence of active petroleum system of probable economic value) of the delineated field in the Niger Delta depobelt.

An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study

AbstractThe main reservoir in Huizhou sub-basin is Zhujiang Formation of early Miocene age. The petrophysical analysis shows that the Zhujiang Formation contains thin carbonate intervals, which have good hydrocarbon potential. However, the accurate interpretation of thin carbonate intervals is always challenging as conventional seismic interpretation techniques do not provide much success in such cases. In this study, well logs, three-layer forward amplitude versus offset (AVO) model and the wedge model are integrated to analyze the effect of tuning thickness on AVO responses. It is observed that zones having a thickness greater than or equal to 15 m can be delineated with seismic data having a dominant frequency of more than 45 Hz. The results are also successfully verified by analyzing AVO attributes, i.e., intercept and gradient. The study will be helpful to enhance the characterization of thin reservoir intervals and minimize the risk of exploration in the Huizhou sub-basin, China.

A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies

Mudstone reservoirs demand accurate information about subsurface lithofacies for field development and production. Normally, quantitative lithofacies modeling is performed using well logs data to identify subsurface lithofacies. Well logs data, recorded from these unconventional mudstone formations, are complex in nature. Therefore, identification of lithofacies, using conventional interpretation techniques, is a challenging task. Several data-driven machine learning models have been proposed in the literature to recognize mudstone lithofacies. Recently, heterogeneous ensemble methods (HEMs) have emerged as robust, more reliable and accurate intelligent techniques for solving pattern recognition problems. In this paper, two HEMs, namely voting and stacking, ensembles have been applied for the quantitative modeling of mudstone lithofacies using Kansas oil-field data. The prediction performance of HEMs is also compared with four state-of-the-art classifiers, namely support vector machine, multilayer perceptron, gradient boosting, and random forest. Moreover, the contribution of each well logs on the prediction performance of classifiers has been analyzed using the Relief algorithm. Further, validation curve and grid search techniques have also been applied to obtain valid search ranges and optimum values for HEM parameters. The comparison of the test results confirms the superiority of stacking ensemble over all the above-mentioned paradigms applied in the paper for lithofacies modeling. This research work is specially designed to evaluate worst- to best-case scenarios in lithofacies modeling. Prediction accuracy of individual facies has also been determined, and maximum overall prediction accuracy is obtained using stacking ensemble.

3D approach for fault identification within power transformers using frequency response analysis

With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

Sill Cube: An automated approach for the interpretation of magmatic sill complexes on seismic reflection data

Abstract The use of conventional interpretation techniques like manual interpretation, seismic attribute analysis and opacity rendering or geobody extraction have demonstrated significant success in interpreting magmatic sills from seismic reflection data. Nonetheless, the downsides of seismic imaging and interpretation of magmatic sills are that they are rarely drilled, or too thin to be detected. We present an automated approach for interpreting magmatic sills from seismic reflection data by using a newly designed meta-attribute, called as the Sill Cube (SC). The SC meta-attribute is generated by amalgamating a number of different attributes that are trained over the interpreter's knowledge of the sill networks through a supervised scheme of neural learning. The approach has shown its efficiency by delivering enhanced images of magmatic sills from seismic data in three frontier areas i.e., the Kora prospect (offshore Taranaki Basin, New Zealand), the Utgard and the Tulipan prospects (both offshore Voring Basin, Norway). The SC meta-attribute has completely captured the saucer-shape geometry of most of the sills. Additionally, the meta-attribute has brought out different structural elements such as the limbs, bridges, junctions and fingers and highlighted the overlying forced folds. The interpreted sills cover areas of 6–16 km2 in the Kora prospect, 105–1140 km2 in the Utgard prospect and 15–60 km2 in the Tulipan prospect. Such an interpretational approach does not only honour the interpreter's knowledge of sills networks but also add values in understanding the tectonic architecture.

Seismic volumetric interpretation of a disseminated copper system in Kevitsa, northern Finland

Improved mining technology and scarcity of near-surface deposits is forcing the mining industry to explore deeper in the search for economic mineralisation. Reflection seismic is one of the few geophysical methods that have sufficient resolution at depth to constrain geological information of an ore deposit at the drilling scale. Reflection seismic methods can be used to reduce drilling costs by focusing the drilling in strategically important areas. Recently introduced seismic volumetric interpretation techniques have advantages over conventional interpretation techniques where the interpretation is done by slicing the volume in 2D planes. Volumetric interpretation is performed in 3D, in real time, by applying various opacity and transparency filters to the seismic volume from different angles, which enables in–depth understanding of the volume. This initial stage of volumetric interpretation is followed by mapping the interfaces and associated structures of exploration interest. A 3D high–resolution seismic dataset was collected to investigate steeply dipping to sub–vertical structures in Kevitsa, northern Finland. Automatic fault extraction using a modified ant–tracking workflow was done on the seismic volume.

Visualization, interpretation, and cognitive cybernetics

Interpretation of 3D seismic data involves the analysis and integration of many forms and derivatives of the original reflectivity data. This can lead to the generation of an overwhelming amount of data that can be difficult to use effectively when relying on conventional interpretation techniques. Our natural cognitive processes have evolved so that we can absorb and understand large amounts of complex data extremely quickly and effectively. However, these cognitive processes are heavily influenced by context and color perception. Seismic interpretation can benefit greatly through better exploiting the positive aspects of visual cognition and through techniques designed to minimize the pitfalls inherent in the cognitive process. The interpretation of data also requires the ability to combine data analysis with knowledge and expertise that is held by the interpreter. It is this combination of visual perception techniques to see the information, combined with interpreter guidance to understand what is seen, that makes interpretation of seismic data effective. Geological Expression workflows that are data driven and interpreter guided enable us to see and effectively interpret the geology that is present in the seismic data. In effect this gives us a Cognitive Interpretation of the data.

Inversion-based detection of bed boundaries for petrophysical evaluation with well logs: Applications to carbonate and organic-shale formations

Petrophysical interpretation of well logs acquired in organic shales and carbonates is challenging because of the presence of thin beds and spatially complex lithology; conventional interpretation techniques often fail in such cases. Recently introduced methods for thin-bed interpretation enable corrections for shoulder-bed effects on well logs but remain sensitive to incorrectly picked bed boundaries. We introduce a new inversion-based method to detect bed boundaries and to estimate petrophysical and compositional properties of multilayer formations from conventional well logs in the presence of thin beds, complex lithology/fluids, and kerogen. Bed boundaries and bed properties are updated in two serial inversion loops. Numerical simulation of well logs within both inversion loops explicitly takes into account differences in the volume of investigation of all well logs involved in the estimation, thereby enabling corrections for shoulder-bed effects. The successful application of the new interpretation method is documented with synthetic cases and field data acquired in thinly bedded carbonates and in the Haynesville shale-gas formation. Estimates of petrophysical/compositional properties obtained with the new interpretation method were compared to those obtained with (1) nonlinear inversion of well logs with inaccurate bed boundaries, (2) depth-by-depth inversion of well logs, and (3) core/x-ray diffraction measurements. Results indicated that the new method improves the estimation of porosity of thin beds by more than 200% in the carbonate field example and by more than 40% in the shale-gas example, compared to depth-by-depth interpretation results obtained with commercial software. This improvement in the assessment of petrophysical/compositional properties reduces uncertainty in hydrocarbon reserves and aids in the selection of hydraulic fracture locations in organic shale.

Diagnostic fracture-injection tests with complex fracture networks

Diagnostic fracture-injection tests (DFIT) are small-volume fracturing tests used to estimate the minimum principal stress and other formation properties. Conventional DFIT interpretation techniques assume that injection forms a single, planar fracture perpendicular to the minimum principal stress. However, abundant evidence exists that hydraulic fracturing causes the generation of complex networks, especially in shale. A computational simulation of a DFIT test in a “complex” fracture network that includes both new and preexisting fractures was performed with a discrete fracture-network (DFN) algorithm that fully couples fluid flow with the stresses induced by fracture deformation and propagation. Results show that with fracture network complexity, standard DFIT interpretation methods might give erroneous results. More broadly, results suggest that hydraulic-fracture models and interpretation techniques need to be updated to include the effects of fracture-network complexity. The results are preliminary because of several simplifying assumptions made by the simulator. In future work, those assumptions will be relaxed, the simulator will be used to develop applied interpretation techniques, and the techniques will be applied to field data.

Diacritical Seismic Signatures for Complex Geological Structures: Case Studies from Shushan Basin (Egypt) and Arkoma Basin (USA)

Seismic reflection techniques show an imperative role in imaging complex geological structures and are becoming more acceptable as data interpreting tools in 2D/3D view. These subsurface geological structures provide complex seismic signature due to their geometrical behavior. Consequently, it is extremely difficult to interpret these seismic sections in terms of subsurface configuration. The main goal of this paper is to introduce seismic attributes as a powerful tool to interpret complex geological structures in different geological settings. In order to image these complex geological features, multiple seismic attributes such as coherence and curvature have been applied to the seismic data generated over the Shushan Basin (Egypt) and Arkoma Basin (USA). Each type of geological structure event usually generates a unique seismic “signature” that we can recognize and identify by using these seismic attributes. In Shushan Basin (Egypt), they provide a framework and constraint during the interpretation and can help prevent mistakes during a 3D structural modeling. In Arkoma Basin (USA), the seismic attributes results provide useful information for broader analyses of the complex structural relations in the region where the Ouachita orogenic belt intersects with the southern Oklahoma aulacogen. Finally, complex geological structures provide dramatically diacritical seismic signatures that can be easily interpreted by collaborating conventional seismic interpretation techniques with multiple seismic attributes.

Inversion-based petrophysical interpretation of logging-while-drilling nuclear and resistivity measurements

Interpretation of borehole measurements acquired in high-angle (HA) and horizontal (HZ) wells is challenging due to the significant influence of well trajectory and bed geometrical effects. Experience shows that accurate integrated interpretation of well logs acquired in HA/HZ wells requires explicit consideration of 3D measurement physics. The most reliable alternative for interpretation of well logs in HA/HZ wells is with inversion techniques that correct measurements for shoulder-bed, undulating well trajectory, and bed geometrical effects while taking advantage of high data resolution. We discovered an efficient layer-based inversion workflow for combined, quantitative petrophysical and compositional interpretation of logging-while-drilling sector-based nuclear (density, neutron porosity, photoelectric factor, gamma ray) and array propagation resistivity measurements acquired in HA/HZ wells. A challenging synthetic benchmark example confirmed improved formation evaluation with the layer-based inversion workflow across hydrocarbon-bearing zones in HA/HZ wells, where estimated hydrocarbon pore volume and porosity increased by 10% and 15%, respectively, with respect to conventional interpretation methods. Furthermore, application of the inversion-based method to a field example of HZ well across calcite-cemented siltstone layers confirmed its advantage over conventional interpretation techniques.

Toward Routine Use of 3D Histopathology as a Research Tool

Three-dimensional (3D) reconstruction and examination of tissue at microscopic resolution have significant potential to enhance the study of both normal and disease processes, particularly those involving structural changes or those in which the spatial relationship of disease features is important. Although other methods exist for studying tissue in 3D, using conventional histopathological features has significant advantages because it allows for conventional histopathological staining and interpretation techniques. Until now, its use has not been routine in research because of the technical difficulty in constructing 3D tissue models. We describe a novel system for 3D histological reconstruction, integrating whole-slide imaging (virtual slides), image serving, registration, and visualization into one user-friendly package. It produces high-resolution 3D reconstructions with minimal user interaction and can be used in a histopathological laboratory without input from computing specialists. It uses a novel method for slice-to-slice image registration using automatic registration algorithms custom designed for both virtual slides and histopathological images. This system has been applied to >300 separate 3D volumes from eight different tissue types, using a total of 5500 virtual slides comprising 1.45 TB of primary image data. Qualitative and quantitative metrics for the accuracy of 3D reconstruction are provided, with measured registration accuracy approaching 120 μm for a 1-cm piece of tissue. Both 3D tissue volumes and generated 3D models are presented for four demonstrator cases.

On the performance of bender elements in triaxial tests

This paper presents the results of a research programme in which the transfer functions of bender element/soil systems have been measured for various materials, set-ups and stress levels. The resulting database has allowed the characterisation of the inherent multi-vibrational nature of the system, and the interpretation challenges that this imposes. The excitation of multiple modes of vibration may introduce signal distortion and consequently differences between group velocity and phase velocity. This means that the common assumption of conventional interpretation techniques that group velocity is equal to shear-wave velocity is in error. Based on the features observed, system parameters have been introduced and a parametric analysis carried out in order to assess the performance of the system and to better understand the effects of multimodal vibration as an attempt to improve signal interpretation.