Data Acquisition Points Research Articles

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

Modeling microwave heating for enhanced shale gas recovery: Fully coupled two-phase flows with heat transfer and electromagnetism in deformable reservoirs

To investigate the mechanisms of fluid migration during shale gas extraction under microwave irradiation, we developed a coupled Electromagnetic-Thermal-Hydraulic-Mechanical (ETHM) model. This model accounts for changes in fracture permeability due to variations in temperature, gas pressure, and methane desorption. The impact of microwave power on both gas production and the alteration of reservoir geological properties under microwave stimulation was investigated. Simulation results revealed that with a microwave injection power of 4,000 W, cumulative gas production increased by 45 %, reaching 64.9 million cubic meters. Additionally, the influence of initial water saturation and the diffusion damping coefficient on both gas and water production was examined. The findings show that cumulative water production was 33 % higher when microwave heating was applied compared to scenarios without microwave stimulation. An increase in initial water saturation led to a decrease in daily gas production during the early stages of extraction. Under microwave irradiation, the methane adsorption capacity at data acquisition point MP2 decreased to 0.0075 m3/kg, representing a 69 % reduction compared to conditions without microwave stimulation. Our analysis also found that daily gas production is highly sensitive to the diffusion damping coefficient. The diffusion attenuation effect had a notably negative impact on improving the recovery rate of shale gas reservoirs. In a word, this research holds significant implications for understanding the trends in gas and water production as well as the sensitivity of geological parameters during microwave-assisted thermal recovery of shale gas.

A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques

South Korea has announced a plan to increase the proportion of renewable energy generation to 20% and reduce traditional energy generation by 2030. Among renewable energy resources, wind power has the advantage of relatively low power generation costs. However, it is difficult to forecast, as the output varies significantly depending on changing wind conditions such as the temperature, wind speed, and wind direction. We believe that short-term wind energy forecasts are the most important part for coping with these fluctuations and minimizing scheduling errors, thereby making the grid more reliable and reducing market service costs. Accordingly, we proposed a practical short-term wind power output forecasting method using a novel ensemble model based on a wavelet transform and autoregressive integrated moving average with explanatory variable (ARIMAX) approach. To demonstrate that the model has a good forecasting performance, we applied historical wind speed and wind power output data obtained from Jeju Island's wind farm to the model, and compared them with forecasted values. The normalized mean absolute error (NMAE) was used as the error metric. The comparison results were described for three supervisory control and data acquisition points. The average NMAE was approximately 3%. In addition, an N-1 contingency analysis was conducted to check the voltage profiles and flow limits in the context of a real power system, to ensure that the power system operated stably even with the forecasted values. The system worked successfully with the forecasted values, and can be deployed as application software for energy management systems in South Korea.

ソフトウェア構造情報と動作ログを活用した企業情報システムの実装言語刷新支援方式

Legacy enterprise information systems are required to convert their implementation languages into modern languages to enhance their maintainability. However, in a planning phase, it is difficult for engineers to identify units of programs to be converted appropriately and quickly. In this paper, we propose a support method that can effectively identify the units of programs for language conversion and test data acquisition points by relating application logs to software structure. We implemented this method by using graph database, and through the application of this method to real enterprise information system, we confirmed that we can effectively identify units of program conversion and test data acquisition points.

Spatial distribution of manganese oxide minerals in the natural ferromanganese nodule of the Arctic Sea: A view from Raman spectroscopy

Knowledge of the spatial distribution of manganese oxide minerals in the natural ferromanganese nodules provides improved prospects for understanding the geochemical processes involved in nodule formation. In this study, we report a series of micro-laser Raman spectra along with a line profile from the center to the rim of a spherical ferromanganese nodule from the East Siberian Sea in the Arctic region. For the Raman spectra obtained at the center of the nodule, the characteristic Raman bands for todorokite at ∼ 733, ∼ 508, and ∼ 240 cm‐1 were clearly observed, suggesting that the todorokite is predominant in the central part of the nodule. As the data acquisition point moved away from the center to the rim (D), the Raman bands corresponding to the todorokite decreased, while the intensity of the 588 cm‐1 band corresponding to the layered manganate minerals (i.e., birnessite) increased, suggesting that the proportion of manganate phases with a tunnel structure decreases with nodule growth. In addition, the distinct Raman bands at ∼ 280 and 403 cm‐1 indicated that the triclinic birnessite is prevalent in the outer regions of the nodule. Semi-quantitative measurement of the intensity of diagnostic Raman bands for todorokite (I733) allows us to effectively observe the spatial distribution of manganese oxide minerals in the ferromanganese nodule. At the center of the nodule, I733 was ∼ 0.7. Despite a few fluctuations, I733 clearly decreased from ∼ 0.7 to ∼ 0.2 with increasing D from 0 to 7500 μm, indicating that the relative fraction of todorokite in nodules significantly decreases with nodule growth. This study highlights that a highly resolved spatial distribution of manganate mineral phases in ferromanganese nodules can provide a historical record of the formation process and geochemical conditions.

Health systems and health equity: Advancing the agenda

Despite a compelling body of evidence and decades of policy recommendations, deep inequities in health persist with historically marginalized groups. Operationalizing strategies to achieve equity in health and health care continues to remain elusive to health systems. We propose several focus areas; attention to semantics and concepts, building knowledge of health inequities, redesigning care and transforming cultures, to advance health equity work by health system nurse leaders and clinical nurses. Health equity frameworks, traditionally applied in population and public health, are also discussed to tackle health equity issues and formulate, implement and evaluate solutions to inequities. Examples illustrate ongoing work in our health system in targeted areas and challenges in advancing health equity work. Future efforts by health system nurse leaders should concentrate on technology for point of care health screening and data acquisition, data-driven decisions, and organizational performance measures to narrow health equity gaps.

Fiber Optic Pressure Measurements Open Up New Experimental Possibilities in Hydrogeology.

Fiber-optic (FO) technology is being used increasingly for measurement methods in a variety of environmental applications. However, FO pressure transducers are rarely used in hydrogeological applications. We review the current state of Fabry-Pérot interferometry-based FO pressure transducers, including their advantages and limitations, as another option for high-resolution pressure- or head-change measurements in conventional or advanced aquifer testing. Resolution and precision specifications of FO transducers meet or exceed commonly used non-FO pressure transducers. Due to their design, FO transducers can be used in small-diameter (inner diameter ≥1/4inch) and continuous multichannel tubing (CMT), sampling points, multilevel packer systems, and Direct Push-based in situ installations and testing. The small diameter of FO transducers provides logistical advantages-especially for tests with monitoring at many zones in a number of wells and/or CMTs (e.g., no reels, placement just below water level in access tubes vs. within isolated zones, reduced weight and volume, small footprint at single point of data acquisition). Principal limitations are small measurement drift that may become evident for tests longer than a few hours, and higher-than-average cost. We present field examples of FO transducer performance in short-term tests with high consistency of acquired data and higher resolution (i.e., capturing significant hydrologic information) compared with commonly used non-FO transducers. Given the above, including advantageous logistical features, FO transducers can open new experimental possibilities in areas of high-resolution three-dimensional (3D) heterogeneity (flow and transport, remediation, critical zones); 3D fracture networks and fundamental hydromechanical behavior; complex 3D flow and leak detection (mines, dams, repositories, geothermal systems).

Methodology for Implementing the State Estimation in Renewable Energy Management Systems

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

Guest Editorial: Robust Resource-Constrained Systems for Machine Learning

Machine learning (ML) is nowadays embedded in several computing devices, consumer electronics, and cyber-physical systems. Smart sensors are deployed everywhere, in applications such as wearables and perceptual computing devices, and intelligent algorithms power our connected world. These devices collect and aggregate volumes of data, and in doing so, they augment our society in multiple ways; from healthcare, to social networks, to consumer electronics, and many more. To process these immense volumes of data, ML is emerging as the de facto analysis tool that powers several aspects of our Big Data society. Applications spanning from infrastructure (smart cities, intelligent transportation systems, smart grids, and to name a few), to social networks and content delivery, to e-commerce and smart factories, and emerging concepts such as self-driving cars and autonomous robots, are powered by ML technologies. These emerging systems require real-time inference and decision support; such scenarios, therefore, may use customized hardware accelerators, are typically bound by limited resources, and are restricted to limited connectivity and bandwidth. Thus, near-sensor computation and near-sensor intelligence have started emerging as necessities to continue supporting the paradigm shift of our connected world. The need for real-time intelligent data analytics (especially in the era of Big Data) for decision support near the data acquisition points emphasizes the need for revolutionizing the way we design, build, test, and verify processors, accelerators, and systems that facilitate ML (and deep learning, in particular) implemented in resource-constrained environments for use at the edge and the fog. As such, traditional von Neumann architectures are no longer sufficient and suitable, primarily because of limitations in both performance and energy efficiency caused especially by large amounts of data movement. Furthermore, due to the connected nature of such systems, security and reliability are also critically important. Robustness, therefore, in the form of reliability and operational capability in the presence of faults, whether malicious or accidental, is a critical need for such systems. Moreover, the operating nature of these systems relies on input data that is characterized by the four “V’s”: velocity (speed of data generation), variability (variable forms and types), veracity (unreliable and unpredictable), and volume (i.e., large amounts of data). Thus, the robustness of such systems needs to consider this issue as well. Furthermore, robustness in terms of security, and in terms of reliability to hardware and software faults, in particular, besides their importance when it comes to safety-critical applications, is also a positive factor in building trustworthiness toward these disrupting technologies from our society. To achieve this envisioned robustness, we need to refocus on problems such as design, verification, architecture, scheduling and allocation policies, optimization, and many more, for determining the most efficient, secure, and reliable way of implementing these novel applications within a robust, resource-constrained system, which may or may not be connected. This special issue, therefore, addresses a key aspect of fog and edge-based ML algorithms; robustness (as defined above) under resource-constraint scenarios. The special issue presents emerging works in how we design robust systems, both in terms of reliability as well as fault tolerance and security, while operating with a limited number of resources, and possibly in the presence of harsh environments that may eliminate connectivity and pollute the input data.

Cloud connection oriented real‐time monitoring system for atmospheric particles

To meet the requirements of data acquisition from mobile pollution sources and unfixed data acquisition points in atmospheric particle monitoring system, this study designs a real-time monitoring system for atmospheric particles such as PM2.5 based on a single-chip microcomputer, an ET-iLink open cloud platform and an Android operating system. The movable data acquisition nodes for atmospheric particles are designed. The laser scattering method is used to obtain the data of atmospheric particle concentration, which are uploaded to the cloud platform through the GPRS network. The Android application obtains the real time data of atmospheric particle concentration from the cloud platform. Thus the real-time remote monitoring for atmospheric particles is realised. The test results show that the system has the following advantages: stable performance, high scalability and low error rate.

Geodynamics and its implications to environmental protection: A case study of Aule area, Akure, Ondo State, Southwestern Nigeria

This research work evaluates the effects of geodynamics processes on the emerging local geology of an area, especially against the background of global tectonic activities, the gradual but continuous earth movement and the depletion of the ozone layer, as it were and its implications on the local geology of a place, especially as it affects the rate of weathering, fracturing and other geologic processes which may pose environmental challenges. A ten year template was adopted for this study, while geophysical and metrological approach was engaged in the study with the use of electrical resistivity method and rainfall data. Two techniques involving lateral resistivity profiling of near E-W orientation with a profile length of 150 m with electrode separation at a = 10 and 20 meters while electrode movement was at 5 meters. Nine vertical electrical soundings (VES) with maximum spread AB/2 = 100 m was carried out at same locations and period (July to August) as was obtained ten years ago for the purpose of effective correlation from the point of data acquisition, data analysis, interpretation, results and findings. Rain data within study area was also collected covering a period of ten (10) years was analysed and presented as bar chart. The result obtained revealed the nature of changes that has taken place within the study area in terms of weathering, and the rate of weathering in the last ten years. Vertical variation/changes occurred at a weathering rate of 1.573, 1.489 and 1.711 cm along the three traverses with average rate of 1.591 cm per year. Horizontal variations also indicated some changes in position of strata, implying an average yearly weathering of about 1.591 cm when compared with the principle of plate tectonics and gradual but continuous earth movement which is at the rate of about 2 cm per year. Therefore, the local geodynamic constant was determined from product of lateral movement and vertical earth weathering trend/gradient which is 3.182 cm2. The rainfall analysis reflects the global weathered changes which is a major contributory factor to geological processes/weathering.

Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection.

The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards.

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.

A Review of GPS Trajectories Classification Based on Transportation Mode.

GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

In an advanced metering infrastructure (AMI), data acquisition points (DAPs) are responsible for collecting traffic from several smart meters and automated devices and transmitting them to the utility control center. Although the problem of optimized data collector placement has already been addressed for wireless broadband and sensor networks, DAP placement is quite a new research area for AMIs. In this paper, we investigate the minimum required number of DAPs and their optimized locations on top of the existing utility poles in a distribution grid such that smart grid quality of service requirements can best be provided. In order to solve the problem for large-scale AMIs, we devise a novel heuristic algorithm using a greedy approach for identifying potential pole locations for DAP placement and the Dijkstra shortest path algorithm for constructing reliable routes. We employ the characteristics of medium access schemes from the IEEE 802.15.4g smart utility network (SUN) standard, and consider mission-critical and non-critical smart grid traffic. The performance and time-complexity of our algorithm are compared with those obtained by the IBM CPLEX software for small scenarios. Finally, we apply our devised DAP placement algorithm to examples of realistic smart grid AMI topologies.

Optimized Data Acquisition Point Placement for an Advanced Metering Infrastructure Based on Power Line Communication Technology

Different communication technologies have been suggested for developing the smart grid communication network. Among these communication technologies, power line communication (PLC) has widely been used, as it has a large coverage range and can access remote areas using existing infrastructures. In this paper, we derive a mathematical model for devising an advanced metering infrastructure (AMI) in the distribution grid based on PLC technology. In order to collect the traffic from thousands of smart meters, intermediary data collectors are placed on selected distribution transformers. However, an optimized placement of data collectors is necessary in order to meet the strict latency requirements needed for time-critical traffic from the meters. For this, we first formulate the latency based on the medium access characteristics of the powerline intelligent metering evolution standard. We then propose an optimization platform for efficiently placing data collectors in such a way that the reliability requirement for the smart grid traffic is ensured and also the installation cost is minimized. We apply the devised optimization solution to realistic examples of AMIs, and we show the effectiveness of our approach through numerical performance evaluation.

Identification of plastic anisotropy using spherical indentation on different anisotropic yield criterions

PurposeThis paper aims to first apply more advanced anisotropic yield criterions as Yld91 and Yld2004 to spherical indentation simulations, and investigate plastic anisotropy identified from indentation simulations following different yield criterions (Hill48, Yld91, Yld2004) to discover laws. It also aims to compare the difference in plastic anisotropy identified from indentation on three yield criterions and evaluate the applicability of plastic anisotropy.Design/methodology/approachThis paper uses indentation simulations on different yield criterions to identify plastic anisotropy. First, the trust-region techniques based on the nonlinear least-squares method are used to determine anisotropy coefficients of Yld91 and Yld2004. Then, Yld91 and Yld2004 are implemented into ABAQUS software using user-defined material (UMAT) subroutines with the proposed universal structure. Finally, through considering comprehensively the key factors, the locations of the optimal data acquisition points in indentation simulations on different yield criterions are determined. And, the identified stress–strain curves are compared with experimental data.FindingsThis paper discovers that indentation on Yld2004 is able to fully identify difference in equivalent plastic strain between 0° and 90° directions when indentation depth ht is relatively smaller. And, this research demonstrates conclusively that plastic anisotropy identified from indentation on Yld2004 and Yld91 is more applicable at larger strains than that on Hill48, and that on Yld2004 is more applicable than that on Yld91, overall. In addition, the method on the determination of the locations of the optimal data acquisition points is demonstrated to be also valid for anisotropic material.Originality/valueThis paper first investigates plastic anisotropic properties and laws identified from indentation simulations following more advanced anisotropic yield criterions and provides reference for later research.

Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

As a significant business paradigm, data trading has attracted increasing attention. However, the study of data acquisition in data markets is still in its infancy. Mobile crowdsensing has been recognized as an efficient and scalable way to acquire large-scale data. Designing a practical data acquisition scheme for crowd-sensed data markets has to consider three major challenges: crowd-sensed data trading format determination, profit maximization with polynomial computational complexity, and payment minimization in strategic environments. In this paper, we jointly consider these design challenges, and propose VENUS, which is the first profit-driVEN data acqUiSition framework for crowd-sensed data markets. Specifically, VENUS consists of two complementary mechanisms: VENUS-PRO for profit maximization and VENUS-PAY for payment minimization. Given the expected payment for each of the data acquisition points, VENUS-PRO greedily selects the most “cost-efficient” data acquisition points to achieve a sub-optimal profit. To determine the minimum payment for each data acquisition point, we further design VENUS-PAY, which is a data procurement auction in Bayesian setting. Our theoretical analysis shows that VENUS-PAY can achieve both strategy-proofness and optimal expected payment. We evaluate VENUS on a public sensory data set, collected by Intel Research, Berkeley Laboratory. Our evaluation results show that VENUS-PRO approaches the optimal profit, and VENUS-PAY outperforms the canonical second-price reverse auction, in terms of total payment.

Benefit Oriented Production Data Acquisition for the Production Planning and Control

In order to stay competitive, many manufacturing companies, especially small and medium sized enterprises (SME), face the challenge to transform their production and the corresponding production planning and control (PPC) processes for the upcoming Internet of Things (IoT) Era. Since their time and cost budget is a constraint, SME need to focus particularly on relevant data types, data acquisition points and technologies that will be beneficial for their manufacturing processes. State of the art approaches are lacking in supporting SME sustainably, systematically and company-specific on their way to IoT from a PPC-perspective. Therefore, in this paper a systematic approach is described, which is providing a sustainable, benefit-oriented, and gradual guideline for companies which aim to build an IoT-supported production data acquisition for PPC processes, in order to enable sustainable manufacturing. The developed method is taking into account company-specific production structures through quantitative key performance indicators and qualitative morphological checklists. With the help of the approach proposed in this paper, SMEs are supported systematically on their transformation path of the production data acquisition for the PPC into the IoT Era in order to enable a sustainable production.

Determination of the Physical Properties of Near Surface Layers of Omerulu Area, Nigeria, Using Seismic Refraction Method

Seismic refraction method is a geophysical technique used to study physical properties of the subsurface such as layer thicknesses, travel times and velocities of seismic signals through the layers. The process of turning seismic refraction data to useful information involves use of first arrival times and offsets. In this study, the near surface investigation of the physical properties of the unconsolidated and consolidated layers was carried out in Omerelu, Rivers State of Nigeria. Omerelu lies between latitude 050 08’N and 050 13’N and longitude 060 51’E and 060 58’E. Previous investigations were based on refection method in which the near surface effect caused misalignment in the deeper horizons observed in the final stack of the reflection data. This problem was solved by running a seismic refraction survey over the area. The study involved identification of points at which data would be acquired to give a good overview of the area under consideration. Twelve sampling points were picked with a grid of approximately 4 × 4 km. A 100 m line with two source points at each end was cleared at each data acquisition point after which the coordinates were taken using Leica Total station (TC 1203 survey equipment). Seismic signals were recorded using OYO McSeis 160M coupled with a 12-geophone harness along with a blasting unit. Upsphere processing software was used to plot time - offset graphs to determine the velocities of the unconsolidated and consolidated layers. The intercept times were also graphically obtained and used to determine the thickness of the unconsolidated layer. Results show that the thickness of unconsolidated/weathered layer in the study area varies between 12.25 and 13.60 m, while the velocities of the unconsolidated and the consolidated layers vary between 500 – 550 m/s and 1790 – 1875 m/s respectively. The results obtained when applied to the reflection data, were able to resolve the static problems; thereby increasing and improving the quality of data available on the lithology of the study area.