Good Quality Data Research Articles

Training a Seismogram Discriminator Based on ResNet

Selecting appropriate data of good quality is the primary precondition for conducting meaningful analysis in seismological research. The data set used in a study is usually selected by setting a threshold for searching the earthquake catalog to ensure data quality, such as parameters of magnitude or hypocentral distance. While the threshold approach is useful, it cannot guarantee the consistently good quality of the selected seismograms. For that reason, a manual checking process is often required for quality control, which is inefficient and fallible for large data sets. In this study, we develop an automated seismogram discriminator that is capable of selecting quality seismograms from massive events. The discriminator is created based on a machine learning technique using a residual neural network (ResNet), a well-designed architecture in computer image recognition. Three-component seismic records of an earthquake from the catalog are considered as images by the ResNet. Using the images of seismic records, the ResNet can be trained to distinguish between good and poor seismic records. This discriminatory ability is evaluated using a blind testing data set of approximately 20 000 three-component seismic records related to the events that occurred in Sichuan between January 2014 and May 2018. The results show that our seismogram discriminator achieves an accuracy of greater than 95%.

Applications of Information Theory in Rock Engineering

Rock engineering relies heavily on empirical systems to identify significant parameters influencing rock mass behaviour. The empirical and inductive nature of rock engineering design is such that it is not possible to eliminate uncertainty. One way of managing uncertainty during the design process is by collecting good quality data in a standardized and objective manner. However, difficulties arise when defining and determining what constitutes good quality data. We believe that information theory and the concept of Shannon’s entropy could be effectively used to better audit rock engineering data. This paper builds on established concepts by expanding and refining the application of information theory to rock mass classification systems, specifically the rock mass rating and the Q-system. One of the objectives is to provide and showcase a method whereby information auditing is used to flag uncertain (or poor quality) data. In the future it is not difficult to envision data collection processes that include improved core logging and data processing where imaging technologies are coupled with machine learning processing capability. Such an approach requires more quantitative and objective rock mass descriptions; in this context it easy to appreciate the role that information theory might have in the future in rock engineering.

High-intensity focused ultrasound (HIFU) ablation versus surgical interventions for the treatment of symptomatic uterine fibroids: a meta-analysis.

To compare the treatment success and safety of ultrasound- and MR-guided high-intensity focused ultrasound (HIFU) with surgery for treating symptomatic uterine fibroids. We searched studies comparing HIFU with surgery for fibroids in different databases from January 2000 to July 2020. The mean difference (MD) or relative risk (RR) with 95% confidence interval (CI) for different outcome parameters was synthesized. We included 10 studies involving 4450 women. Compared with the surgery group, the decrease in uterine fibroid severity score at 6- and 12-month follow-up was higher in the HIFU group (MD - 4.16, 95% CI - 7.39 to - 0.94, and MD - 2.44, 95% CI - 3.67 to - 1.20, p < 0.05). The increase in quality-of-life (QoL) score at 6- and 12-month follow-up was higher in the HIFU group (MD 2.13, 95% CI 0.86 to 3.14, and MD 2.34, 95% CI 0.82 to 3.85, p < 0.05). The duration of hospital stay and the time to return to work was shorter in the HIFU group (MD - 3.41 days, 95% CI - 5.11 to - 1.70, and MD - 11.61 days, 95% CI - 19.73 to - 3.50, p < 0.05). The incidence of significant complications was lower in the HIFU group (RR 0.33, 95% CI 0.13 to 0.81, p < 0.05). The differences in the outcomes of adverse events, symptom recurrence, re-intervention, and pregnancy were not statistically significant (p > 0.05). HIFU is superior to surgery in terms of symptomatic relief, improvement in QoL, recovery, and significant complications. However, HIFU showed comparable effects to surgery regarding the incidence of adverse events, symptom recurrence, re-intervention, and pregnancy. • HIFU ablation is superior to surgery in terms of symptomatic relief, improvement in QoL, recovery, and significant complications. • HIFU has comparable effects to surgery in terms of symptom recurrence rate, re-intervention rate, and pregnancy rate, indicating that HIFU is a promising non-invasive therapy that seems not to raise the risk of recurrence and re-intervention or deteriorate fertility compared to surgical approaches in women with fibroids. • There is still a lack of good-quality comparative data and further randomized studies are necessary to provide sufficient and reliable data, especially on re-intervention rate and pregnancy outcome.

Analysis of the Relationships between Balance Ability and Walking in Terms of Muscle Activities and Lower Limb Kinematics and Kinetics

There is a lack of evidence about the ways in which balance ability influences the kinematic and kinetic parameters and muscle activities during gait among healthy individuals. The hypothesis is that balance ability would be associated with the lower limb kinematics, kinetics and muscle activities during gait. Twenty-nine healthy volunteers (Age 32.8 ± 9.1; 18 males and 11 females) performed a Star Excursion Balance test to measure their dynamic balance and walked for at least three trials in order to obtain a good quality of data. A Vicon® 3D motion capture system and AMTI® force plates were used for the collection of the movement data. The selected muscle activities were recorded using Delsys® Electromyography (EMG). The EMG activities were compared using the maximum values and root mean squared (RMS) values within the participants. The joint angle, moment, force and power were calculated using a Vicon Plug-in-Gait model. Descriptive analysis, correlation analysis and multivariate linear regression analysis were performed using SPSS version 23. In the muscle activities, positive linear correlations were found between the walking and balance test in all muscles, e.g., in the multifidus (RMS) (r = 0.800 p &lt; 0.0001), vastus lateralis (RMS) (r = 0.639, p &lt; 0.0001) and tibialis anterior (RMS) (r = 0.539, p &lt; 0.0001). The regression analysis models showed that there was a strong association between balance ability (i.e., reaching distance) and the lower limb muscle activities (i.e., vastus medialis–RMS) (R = 0.885, p &lt; 0.0001), and also between balance ability (i.e., reaching distance) and the lower limb kinematics and kinetics during gait (R = 0.906, p &lt; 0.0001). In conclusion, the results showed that vastus medialis (RMS) muscle activity mainly contributes to balance ability, and that balance ability influences the lower limb kinetics and kinematics during gait.

A Large-Scale and Serverless Computational Approach for Improving Quality of NGS Data Supporting Big Multi-Omics Data Analyses.

Various types of analyses performed over multi-omics data are driven today by next-generation sequencing (NGS) techniques that produce large volumes of DNA/RNA sequences. Although many tools allow for parallel processing of NGS data in a Big Data distributed environment, they do not facilitate the improvement of the quality of NGS data for a large scale in a simple declarative manner. Meanwhile, large sequencing projects and routine DNA/RNA sequencing associated with molecular profiling of diseases for personalized treatment require both good quality data and appropriate infrastructure for efficient storing and processing of the data. To solve the problems, we adapt the concept of Data Lake for storing and processing big NGS data. We also propose a dedicated library that allows cleaning the DNA/RNA sequences obtained with single-read and paired-end sequencing techniques. To accommodate the growth of NGS data, our solution is largely scalable on the Cloud and may rapidly and flexibly adjust to the amount of data that should be processed. Moreover, to simplify the utilization of the data cleaning methods and implementation of other phases of data analysis workflows, our library extends the declarative U-SQL query language providing a set of capabilities for data extraction, processing, and storing. The results of our experiments prove that the whole solution supports requirements for ample storage and highly parallel, scalable processing that accompanies NGS-based multi-omics data analyses.

Cumulated burden of COVID-19 in Spain from a Bayesian perspective.

BackgroundThe main goal of this work is to estimate the actual number of cases of Covid-19 in Spain in the period 01-31-2020/06-01-2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately re-estimate the lethality of the disease in Spain, taking into account unreported cases.MethodsA hierarchical Bayesian model recently proposed in the literature has been adapted to model the actual number of Covid-19 cases in Spain.ResultsThe results of this work show that the real load of Covid-19 in Spain in the period considered is well above the data registered by the public health system. Specifically, the model estimates show that, cumulatively until June 1st, 2020, there were 2 425 930 cases of Covid-19 in Spain with characteristics similar to those reported (95% credibility interval: 2 148 261 2 813 864), from which were actually registered only 518 664.ConclusionsConsidering the results obtained from the second wave of the Spanish seroprevalence study, which estimates 2 350 324 cases of Covid-19 produced in Spain, in the period of time considered, it can be seen that the estimates provided by the model are quite good. This work clearly shows the key importance of having good quality data to optimize decision-making in the critical context of dealing with a pandemic.

Motivációk a nem pénzügyi információk közzététele mögött – interjús kutatás a közérdeklődésre számot tartó szervezetek kommunikációjáról

THE AIM OF THE PAPER One channel of communication with stakeholders about environmental, social and governance (ESG) information is the non-financial report. Publication of such reports is compulsory for public interest entities in the European Union from the 2017 business year onwards. In our present research, we examined this regulation and the reporting-related motivations and decisions of the companies listed on the Hungarian stock exchange subject to Directive 2014/95/EU. The aim of our study is to identify the factors influencing the content and format of sustainability-related disclosures. METHODOLOGYIn the course of our research, we conducted expert interviews with communication professionals of reporting companies, an auditor, and an institutional investor. Corporate communication related to sustainability is an integral part of the work of all our interviewees, whether they are responsible for producing of such data or are present as users of ESG information.MOST IMPORTANT RESULTSThe motivating role of the information needs of the stakeholders formulated in the literature has been proved: key roles are primarily played by investors, as well as employees and customers. The best-known standard is the GRI, and entities follow the requirements of a number of rating agencies when compiling reports. The interviews revealed that it is a challenge for reporting firms and stakeholders to produce good-quality data and to evaluate those as a consequence of the diversity of non-financial information. RECOMMENDATIONS Due to the diversity of non-financial information and the wide range of forms of communication, it is difficult for stakeholders to compare the non-financial performance of individual companies. It is therefore recommended for entities to use one of the widely accepted standards (e.g. GRI). In the interests of stakeholders, regulators are encouraged to consider a standardized format for reporting purposes. Acknowledgements: This research was supported by the project nr. EFOP-3.6.2-16-2017-00007, titled Aspects on the development of intelligent, sustainable and inclusive society: social, technological, innovation networks in employment and digital economy. The project has been supported by the European Union, co-financed by the European Social Fund and the budget of Hungary.

Cocultivation Study of Monascus spp. and Aspergillus niger Inspired From Black-Skin-Red-Koji by a Double-Sided Petri Dish.

Cocultivation is an emerging and potential way to investigate microbial interaction in the laboratory. Extensive researches has been carried out over the years, but some microorganism cocultivation are not easy to implement in the laboratory, especially the fungus-fungus (FF) cocultivation, owing to the obstacles such as fungal different growth rate, limited growing space, hyphae intertwining, and difficulty of sample separation, etc. In this research, a double-sided petri dish (DSPD) was designed and carried out as a tool to study FF cocultivation in the laboratory. A natural FF cocultivation of Monascus spp. and Aspergillus niger inspired from black-skin-red-koji (BSRK), were studied. By using DSPD, the aforementioned obstacles in the FF cocultivation study were overcome through co-culturing Monascus spp. and A. niger on each side of DSPD. The characteristics of monocultured and co-cultured Monascus spp. and A. niger were compared and analyzed, including colonial and microscopic morphologies, and main secondary metabolites (SMs) of Monascus spp. analyzed by high performance liquid chromatography. And a novel SM was found to be produced by Monascus ruber M7 when co-cultured with A. niger CBS 513.88. Since the above mentioned obstacles, were overcome, we obtained good quality of transcriptome data for further analysis. These results indicate that DSPD might be an efficient tool for investigation of microbial interaction, in particular, for FF interaction.

Reliability check via weight similarity in privacy-preserving multi-party machine learning

Multi-party machine learning is a paradigm in which multiple participants collaboratively train a machine learning model to achieve a common learning objective without sharing their privately owned data. The paradigm has recently received a lot of attention from the research community aimed at addressing its associated privacy concerns. In this work, we focus on addressing the concerns of data privacy, model privacy, and data quality associated with privacy-preserving multi-party machine learning, i.e., we present a scheme for privacy-preserving collaborative learning that checks the participants’ data quality while guaranteeing data and model privacy. In particular, we propose a novel metric called weight similarity that is securely computed and used to check whether a participant can be categorized as a reliable participant (holds good quality data) or not. The problems of model and data privacy are tackled by integrating homomorphic encryption in our scheme and uploading encrypted weights, which prevent leakages to the server and malicious participants, respectively. The analytical and experimental evaluations of our scheme demonstrate that it is accurate and ensures data and model privacy.

BREC: an R package/Shiny app for automatically identifying heterochromatin boundaries and estimating local recombination rates along chromosomes

BackgroundMeiotic recombination is a vital biological process playing an essential role in genome's structural and functional dynamics. Genomes exhibit highly various recombination profiles along chromosomes associated with several chromatin states. However, eu-heterochromatin boundaries are not available nor easily provided for non-model organisms, especially for newly sequenced ones. Hence, we miss accurate local recombination rates necessary to address evolutionary questions.ResultsHere, we propose an automated computational tool, based on the Marey maps method, allowing to identify heterochromatin boundaries along chromosomes and estimating local recombination rates. Our method, called BREC (heterochromatin Boundaries and RECombination rate estimates) is non-genome-specific, running even on non-model genomes as long as genetic and physical maps are available. BREC is based on pure statistics and is data-driven, implying that good input data quality remains a strong requirement. Therefore, a data pre-processing module (data quality control and cleaning) is provided. Experiments show that BREC handles different markers' density and distribution issues.ConclusionsBREC's heterochromatin boundaries have been validated with cytological equivalents experimentally generated on the fruit fly Drosophila melanogaster genome, for which BREC returns congruent corresponding values. Also, BREC's recombination rates have been compared with previously reported estimates. Based on the promising results, we believe our tool has the potential to help bring data science into the service of genome biology and evolution. We introduce BREC within an R-package and a Shiny web-based user-friendly application yielding a fast, easy-to-use, and broadly accessible resource. The BREC R-package is available at the GitHub repository https://github.com/GenomeStructureOrganization.

A Two-Stage Data-Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms.

Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly right-skewed sewer system failure data to predict the failure risk, leveraging a bidimensional space-time approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodness-of-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer systemmaintenance.

EVOLUTION OF THE URBAN WASTE MANAGEMENT SYSTEM IN THE EMILIA-ROMAGNA REGION

This multidisciplinary study aims to analyse how the urban waste management system has changed in the Italian region of Emilia-Romagna, during the decade in which a single regional regulatory unit, the Emilia-Romagna Territorial Agency for Water and Waste Services (ATERSIR), was established and became operational, and the waste management planning was centralized at regional level. Particularly, the following changes have been analysed: i) the methods of municipal waste management (WM), considering waste generation, separate waste collection and waste treatment; ii) the costs of WM service, with a focus on cost of treatment and disposal of unsorted waste; and iii) the urban solid WM policies, in terms of levels of governance, territorial planning and implementation of policies on the regional territory. The period within which the analysis was carried out covers the years from 2008 to 2018, comparing two time frames, before and after ATERSIR establishment. Data at municipal level were gathered and analysed. The results of the technical, economical and institutional assessment show that relevant benefits occurred, such as a constant improvement of environmental performances, the optimisation of the waste flows to plants, a higher level of uniformity of WM cost among Municipalities and a better quality of data collected from waste providers for the technical and economic regulation of the sector. Potential improvements are identified, whilst the institutional reform is positively evaluated in all the analysed aspects.

Systemic Racism in EEG Research: Considerations and Potential Solutions

The current theoretical paper discusses the unintended systemic racism and racial biases that impact neuroscience, specifically in research utilizing electroencephalography (EEG). As a popular technique in affective science research, EEG requires adherence between the electrode and scalp to measure brain activity. To obtain high-quality data, various factors such as hair length, hair type, body movement, and/or extraneous noise from the environment are taken into consideration. As EEG researchers attempt to gather good-quality data, the recruitment and retention of Black American participants is challenging due to hairstyles commonly worn by Black American participants (e.g., cornrows, braids) and hair type. Taken together, the systemic lack of data from Black American participants renders research findings less generalizable and causes disparities in theoretical knowledge applicable to this population. To address this disparity, innovative solutions invented by bioengineers are discussed.

Validation of GPM Rainfall and Drop Size Distribution Products through Disdrometers in Italy

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).

Lung function in children: a simple guide to performing and interpreting spirometry

<h2>Abstract</h2> Spirometry is the measurement of the volume and flow of air during expiration and inspiration. It is non-invasive and inexpensive and probably under-used in children. Whilst remaining a relatively simple test, it gives valuable information that can be used in the diagnosis and monitoring of respiratory conditions. Adequate spirometry requires good patient engagement, effort and technique. With practice, robust spirometry data can be collected in preschool children as young as 4 years of age. Increasing availability of portable spirometry equipment means that good quality data can be collected in a variety of clinical locations outside the lung function laboratory - even in the patient's own home. There are, however, a number of important considerations in both the performance and interpretation of spirometry. This review considers the equipment required, the basic techniques required and the essentials of interpretation of spirometry data.

Automated detection of shockable ECG signals: A review

• Shockable and non-shockable ECG signals are analyzed. • A review study for shockable ECG signal recognition is discussed. • Unique bispectrum and recurrence plots are proposed for shockable and non-shockable ECG signals. • Performance of nonlinear features for differentiating shockable and non-shockable. • ECG signals is compared. Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.

Conducting Research on Muslims

The aim of the special section in this issue is to identify and examine methodological opportunities for conducting research on Muslims, while also noting challenges and uncertainties. The focus is on methodological and ethical challenges encountered while conducting both qualitative and quantitative research in increasingly unstable socio-political environments. The section examines how researchers address such challenges, and brings together scholars whose research involves studying aspects of Muslim politics from various disciplinary backgrounds. The objective is to identify ethical and practical challenges involved in studying Islam, share insights and experiences of researchers from the Middle East and North Africa, and develop improved techniques to collect good quality data from Muslim groups and societies. The overall aim is arrive at a better understanding of the ethics, politics, and responsibilities involved in studying Islam and Muslims in our age: one characterized by a defiant and resurgent faith.

It\u2019s not about the capture, it\u2019s about what we can learn\u201d: a qualitative study of experts\u2019 opinions and experiences regarding the use of wearable sensors to measure gait and physical activity

BackgroundThe use of wearable sensor technology to collect patient health data, such as gait and physical activity, offers the potential to transform healthcare research. To maximise the use of wearable devices in practice, it is important that they are usable by, and offer value to, all stakeholders. Although previous research has explored participants’ opinions of devices, to date, limited studies have explored the experiences and opinions of the researchers who use and implement them. Researchers offer a unique insight into wearable devices as they may have access to multiple devices and cohorts, and thus gain a thorough understanding as to how and where this area needs to progress. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices to measure gait and physical activity.MethodsTwenty professionals with experience using wearable devices in research were recruited from academic, industry and clinical backgrounds. Independent, semi-structured interviews were conducted, audio-recorded and transcribed. Transcribed texts were analysed using inductive thematic analysis.ResultsFive themes were identified: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Researchers overwhelmingly supported the use of wearable sensor technology due to the insights that they may provide. Though barriers remain, researchers were pragmatic towards these, believing that there is a paradigm shift happening in this area of research that ultimately requires mistakes and significant volumes of further research to allow it to progress.ConclusionsMultiple barriers to the use of wearable devices in research and clinical practice remain, including data management and clear clinical utility. However, researchers strongly believe that the potential benefit of these devices to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary research integrating the expertise of both academia, industry and clinicians is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders.

292 Publication of Individual Surgeon Outcomes Data: The Wishes of Orthopaedic Patients and Their Surgeons

Abstract Background The Paterson report recommended increased release of individual surgeon data to the public. There is limited evidence in the literature about patients’ or orthopaedic surgeons’ wishes regarding release of such data to the public. Method We surveyed 80 joint replacement patients and 41 orthopaedic surgeons to determine their wishes regarding collection and release of individual surgeon data to the public. Results Patients expected more current monitoring of data than actually occurs. Of the patients, 95% wanted data to be available describing surgeon experience, length of stay and complications. Regarding currently available data, 50% of patients who were aware of it were likely to view it on the internet. Surgeons were aware of the data available, but 80% did not think it was accurate. Surgeons did not think this data improved standards, that it negatively affected innovation (61%) and training (76%) and led to risk averse behaviour (61%). Surgeons wanted a minimal data set accurately presented and risk adjusted. Conclusions We recommend an agreed minimum dataset of good quality data is collected and discussed in clinical governance sessions. It can then be released to the public with explanatory notes.

Walkaway VSP in ultra-shallow water images deep Paleozoic targets, offshore UAE

In 2019, ADNOC decided to record walkabove and walkaway VSP surveys in a deep deviated well, in ultra-shallow waters offshore Abu Dhabi. The field is part of a major gas and condensate development project in UAE. The primary objectives included vertical travel times and velocities, robust well to seismic tie, and higher-resolution VSP images of the Palaeozoic-age targets. Other objectives included acoustic impedance inversion and anisotropy calibration. This was the first time a walkaway was acquired with only 2 m of water depth and in a deep hostile downhole environment. This study showed the importance of pre-survey modelling to achieve the VSP objectives. The data were acquired in continuous mode with GPS time stamping. Good data quality was recorded, despite high temperature, deep targets, multiple casings and a small volume seismic source deployed at 1 m depth. Wideband 5–90 Hz data were retrieved across 6 km of complex overburden, resulting in images with three-fold resolution improvement compared to the surface seismic data. The VSP also provided acoustic impedance sections of the deeper targets and measurements to optimize surface seismic processing. The results validate the viability of 3DVSP surveys with similar shallow-water source and downhole receiver array for reservoir characterization and well placement.