Workflow Activities Research Articles

Computer-Aided formulation design for pharmaceutical drug product development, part 01: Materials exploration through a visualization tool

An interactive tool has been developed to help design oral solid dosage form formulations. The tool enables quantitative explorations and comparisons of physical, bulk, and mechanical properties, and takes into account functional characteristics as well. In this manner, comparisons and clustering of both excipients and APIs can be carried out. These comparisons enable the generation of alternatives as well as surrogate identification, so as to spare resources and material.Multiple data sources were merged to create a “joint” data table with all relevant properties. Four main workflow activities are supported: Explore Materials, Search Similar APIs, Search Similar Excipients and Search Material Clusters.Multi-dimensional filtering can be superimposed to each functionality. Suggested visualizations are made particularly accessible by providing them as “standard plots”.The underlying philosophy is to empower formulation scientists to explore options, rather than prescribe decisions on exclusively mathematical grounds. The tool described here is the first step towards a holistic optimization incorporating predictions of mixture properties. Methodology of use is illustrated through three material selection application examples.

Pediatric Sedation Assessment and Management System (PSAMS) for Pediatric Sedation in China: Development and Implementation Report.

Recently, the growing demand for pediatric sedation services outside the operating room has imposed a heavy burden on pediatric centers in China. There is an urgent need to develop a novel system for improved sedation services. This study aimed to develop and implement a computerized system, the Pediatric Sedation Assessment and Management System (PSAMS), to streamline pediatric sedation services at a major children's hospital in Southwest China. PSAMS was designed to reflect the actual workflow of pediatric sedation. It consists of 3 main components: server-hosted software; client applications on tablets and computers; and specialized devices like gun-type scanners, desktop label printers, and pulse oximeters. With the participation of a multidisciplinary team, PSAMS was developed and refined during its application in the sedation process. This study analyzed data from the first 2 years after the system's deployment. From January 2020 to December 2021, a total of 127,325 sedations were performed on 85,281 patients using the PSAMS database. Besides basic variables imported from Hospital Information Systems (HIS), the PSAMS database currently contains 33 additional variables that capture comprehensive information from presedation assessment to postprocedural recovery. The recorded data from PSAMS indicates a one-time sedation success rate of 97.1% (50,752/52,282) in 2020 and 97.5% (73,184/75,043) in 2021. The observed adverse events rate was 3.5% (95% CI 3.4%-3.7%) in 2020 and 2.8% (95% CI 2.7%-2.9%) in 2021. PSAMS streamlined the entire sedation workflow, reduced the burden of data collection, and laid a foundation for future cooperation of multiple pediatric health care centers.

From skin checking to suturing: efficacy of a sequential dermatology simulation for medical students.

Clinical simulations have been shown to increase medical students’ understanding of and interest in dermatology. The New York University Grossman School of Medicine Dermatology Interest Group developed and evaluated a new simulation that more fully mimics the process of diagnosing pigmented lesions by emulating a dermatologist’s actual workflow. After participating in the simulation, students demonstrated increased knowledge in both short- and long-term assessments, and 96% felt that the simulation should be a mandatory part of their curriculum.

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

Design of an Intelligent Platform for Financial Expense Reimbursement in Colleges Based on Intelligent Data Storage of Process Information Sampling

The design is based on GSM process information wireless measurement system. The system is composed of related sensors and data acquisition modules, GSM wireless communication modules and main control computer, and has strong versatility. At present, colleges and universities have developed an information system that can meet the needs of financial management in colleges and universities by using advanced software development technology and combining with the actual work flow of financial management. Using object-oriented programming technology to develop a budget management system with a friendly operation interface, changing the complexity of manual budget preparation in the past and improving the efficiency of budget preparation.

Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows’ deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experiments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process.

A new children's hospital with a larger floor space, single rooms, and V-shaped ward design: A pre-post evaluation of nurse time providing patient care and nurse, patient, and family experiences.

The impact of hospital building design on patients, families and nurses related to nursing care interactions is not well understood. This study reports a pre-post intervention study to understand the effects of the move to a new children's hospital in Perth, Australia, on nurse workflow activities and on patient, family, and nurse experiences. We used a pre-post explanatory sequential design involving observation of nurse work tasks; measurement of the Practice Environment Scale and Nurse Work Index; weekly surveys of nurse, patient and family experiences; and nurse focus groups and interviews with patients and families. Survey data were analysed using linear regression; qualitative data analysis used a thematic approach. Nurse time spent walking almost doubled (p < 0.001), from an estimated 10 min at T1 (pre-move) to around 20 min at T4 (12 months post-move), but there was no difference in nurse time providing patient care (p = 0.114). The Practice Environment Scale and Nurse Work Index showed significantly reduced scores for nursing foundations for quality of care (adjusted mean difference -0.08, p = 0.016) and staffing and resource adequacy (adjusted mean difference -0.19, p < 0.001).This fall was mirrored in nurse experience surveys with a reduction in mean scores from T1 to T3 (3 months post-move) of -0.7 (p < 0.001) and from T1 to T4 of -0.4 (p = 0.002). Thematic analysis of qualitative data found that initial challenges appeared to reduce over time. Nurses reported difficulties managing workflow in the new wards and feelings of exhaustion at T3, but this changed to more positive accounts at T4. For patients and families there was a tension between leaving the old and familiar, enjoying the light and space of the new and shared observations that nurses appeared to be busier at T3. At T4, these experiences had changed to 'being a family in hospital' and confidence that a nurse was always close by. Both benefits and challenges of the new hospital design were encountered from the perspective of nurses, patients, and families. Nurses spent double the time walking in the new environment, but time spent providing patient care was unchanged. Over time, the initial practice challenges reduced as nurses adapted to working in the new environment. ACTRN12618000775213.

TBSSvis: Visual analytics for Temporal Blind Source Separation

Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to Principal Component Analysis (PCA) as it separates the input data into univariate components and is applicable to suitable datasets from various domains, such as medicine, finance, or civil engineering. Despite TBSS’s broad applicability, the involved tasks are not well supported in current tools, which offer only text-based interactions and single static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and sets of time series. Additionally, parameter settings have a big impact on separation performance, but as a consequence of improper tooling, analysts currently do not consider the whole parameter space. We propose to solve these problems by applying visual analytics (VA) principles. Our primary contribution is a design study for TBSS, which so far has not been explored by the visualization community. We developed a task abstraction and visualization design in a user-centered design process. Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution. We present TBSSvis, an interactive web-based VA prototype, which we evaluated extensively in two interviews with five TBSS experts. Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.

Lean Design of the Pediatric Intensive Care Unit Patient Room for Efficient and Safe Care Delivery.

The pediatric intensive care unit (PICU) is an environment where seriously ill children receive complex care, delivered mostly by specialty-trained nurses (registered nurses [RNs]) who must perform multiple high-level tasks. With stressors on healthcare systems at an all-time high, design that optimizes RN workflow has taken on a renewed imperative. To employ a multimodal approach (1) to identify environmental factors in the PICU patient room that contribute to caregiver workflow inefficiencies, (2) to optimize safety by identifying high-touch surfaces that cause hospital-acquired infections, (3) to develop human-centered design recommendations. This mixed-method case study was conducted in a 23-bed urban hospital PICU. The activities, movements, and workflows of 13 RNs were recorded using spatial movement mapping, behavioral mapping, and clinical activity mapping. Frequency of RN contact with surfaces was documented to assess relative infection transmission risk. Face-to-face interviews were conducted with RNs to elicit their views on care delivery and their physical work environment. Direct patient care occupied 50% of RNs' time. Of the direct patient care workflow activities recorded, 26% were to prepare for care around the bedside, while 27% were for random travel between clean and soiled areas. The surfaces most frequently touched were (1) patient bedrails, (2) intravenous pumps and poles, (3) tubing and medical equipment, and (4) vital sign monitors. Value-added tasks account for only about 20% of nurses' work. Combining technology and strategic interior design to streamline workflow and enhance infection prevention optimizes efficiency and empowers frontline providers to maximize their time at the bedside performing value-added tasks.

Automated portfolio-based strategic asset management based on deep neural image classification

Despite the popularity of image-based classification techniques in identifying building materials and elements in building and construction sites, the real feasibility of it accommodating the needs of operation and maintenance (O&M) inspection-repair processes in the real project scale is still awaiting testing due to the vast number of project assets, hundreds of asset categories, and requirements of portfolio-based strategic asset management (SAM) tasks. The building's O&M phase does not yet have a large-scale inspection-repair dataset created on images based on the actual workflow of building and infrastructure projects. This paper described a MobileNet1.0-based image classification method for optimising and automating a series of portfolio-based SAM service processes, including condition surveying, data validation, standardisation and integration. By examining the performance of image classification in the building O&M phase's SAM tasks, the paper examines whether the constructed MobileNet1.0 model can achieve satisfactory built assets category classification performance based on commercial surveying data with the enhancement of the online image training dataset. The constructed MobileNet1.0 model achieved satisfactory built assets category classification results for both building asset type category classification (with 85.6% level-1 test accuracy) and different building data attributes (e.g., built assets' condition, activity cycle, failure type, etc.). The experiment results proved the online enhanced image training dataset can further boost the on-site image classification performance. The results of this paper demonstrate a broader application of image-based technologies in portfolio-based SAM and other building and infrastructure projects' O&M phase applications. Future research can be conducted to exploit the performance of image classification methods over more building asset categories and building and building asset data attribute types.

THE APPROACH AND THE SOFTWARE TOOL TO CALCULATE SEMANTIC QUALITY MEASURES OF BUSINESS PROCESS MODELS

Business process models are essential business process management artifacts that help describe visually ongoing business activities to facilitate communication between information technology and business stakeholders. Business process models are used to find inefficient spots within described workflows and resolve detected shortcomings by automation via configurable software solutions or unified workflow engines. However, this is impossible when using syntactically or semantically poor business process models. It is the same as building a house using the blueprint with windows on the floor and typos in text labels. Therefore, it is extremely important to keep created business process models clear and relevant to the actual workflows they describe. Hence, in this paper, we propose the approach and the software tool to calculate semantic quality measures of business process models. The proposed approach uses a special procedure to extract the modeling domain statements using natural language processing techniques. According to the proposed approach, the initial textual descriptions of business process models should be tokenized. Then obtained tokens should be turned to the lower case style and cleansed to remove non-alphabetic tokens and stop words. Finally, the remaining tokens should be stemmed and the existing duplicates should be removed. The same procedure is then repeated for text labels attached to the business process model activities. Then, tokens present in the result of textual description’s processing but missing in the result of labels’ processing are considered incomplete (i.e. incorrect in the modeling domain). Similarly, tokens present in the result of labels’ processing but missing in the result of textual description’s processing are considered invalid (i.e. irrelevant to the modeling domain). Therefore, respective semantic quality measures can be calculated. The software tool is created using the Python programming language because of its powerful natural language processing packages.

Augmenting systems-level implementation of patient-reported outcomes for depression care through the use of structured analysis and design technique.

Health systems increasingly need to implement complex practice changes such as the routine capture of patient-reported outcome (PRO) measures. Yet, health systems have met challenges when trying to bring practice change to scale across systems at large. While implementation science can guide the evaluation of implementation determinants, teams first need tools to systematically understand and compare workflow activities across practice sites. Structured analysis and design technique (SADT), a system engineering method of workflow modeling, may offer an opportunity to enhance the scalability of implementation evaluation for complex practice change like PROs. We utilized SADT to identify the core workflow activities needed to implement PROs across diverse settings and goals for use, establishing a generalizable PRO workflow diagram. We then used the PRO workflow diagram to guide implementation monitoring and evaluation for a 1-year pilot implementation of the electronic Patient Health Questionnaire-9 (ePHQ). The pilot occurred across multiple clinical settings and for two clinical use cases: depression screening and depression management. SADT identified five activities central to the use of PROs in clinical care: deploying PRO measures, collecting PRO data, tracking PRO completion, reviewing PRO results, and documenting PRO data for future use. During the 1-year pilot, 8,596 patients received the ePHQ for depression screening via the patient portal, of which 1,719 (21%) submitted the ePHQ; 367 patients received the ePHQ for depression management, of which 174 (47%) submitted the ePHQ. We present three case examples of how the SADT PRO workflow diagram augmented implementation monitoring, tailoring, and evaluation activities. Use of a generalizable PRO workflow diagram aided the ability to systematically assess barriers and facilitators to fidelity and identify needed adaptations. The use of SADT offers an opportunity to align systems science and implementation science approaches, augmenting the capacity for health systems to advance system-level implementation. Health systems increasingly need to implement complex practice changes such as the routine capture of patient-reported outcome (PRO) measures. Yet these system-level changes can be challenging to manage given the variability in practice sites and implementation context across the system at large. We utilized a systems engineering method-structured analysis and design technique-to develop a generalizable diagram of PRO workflow that captures five common workflow activities: deploying PRO measures, collecting PRO data, tracking PRO completion, reviewing PRO results, and documenting PRO data for future use. Next, we used the PRO workflow diagram to guide our implementation of PROs for depression care in multiple clinics. Our experience showed that use of a standard workflow diagram supported our implementation evaluation activities in a systematic way. The use of structured analysis and design technique may enhance future implementation efforts in complex health settings.

Energy-effective artificial internet-of-things application deployment in edge-cloud systems

Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA^3D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.

Evaluating Simplified Web Interfaces of Risk Models for Clinical Use: Pilot Survey Study

BackgroundIn this pilot study, we investigated sociotechnical factors that affect intention to use a simplified web model to support clinical decision making.ObjectiveWe investigated factors that are known to affect technology adoption using the unified theory of acceptance and use of technology (UTAUT2) model. The goal was to pilot and test a tool to better support complex clinical assessments.MethodsBased on the results of a previously published work, we developed a web-based mobile user interface, WebModel, to allow users to work with regression equations and their predictions to evaluate the impact of various characteristics or treatments on key outcomes (eg, survival time) for chronic obstructive pulmonary disease. The WebModel provides a way to combat information overload and more easily compare treatment options. It limits the number of web forms presented to a user to between 1 and 20, rather than the dozens of detailed calculations typically required. The WebModel uses responsive design and can be used on multiple devices. To test the WebModel, we designed a questionnaire to probe the efficacy of the WebModel and assess the usability and usefulness of the system. The study was live for one month, and participants had access to it over that time. The questionnaire was administered online, and data from 674 clinical users who had access to the WebModel were captured. SPSS and R were used for statistical analysis.ResultsThe regression model developed from UTAUT2 constructs was a fit. Specifically, five of the seven factors were significant positive coefficients in the regression: performance expectancy (β=.2730; t=7.994; P<.001), effort expectancy (β=.1473; t=3.870; P=.001), facilitating conditions (β=.1644; t=3.849; P<.001), hedonic motivation (β=.2321; t=3.991; P<.001), and habit (β=.2943; t=12.732). Social influence was not a significant factor, while price value had a significant negative influence on intention to use the WebModel.ConclusionsOur results indicate that multiple influences impact positive response to the system, many of which relate to the efficiency of the interface to provide clear information. Although we found that the price value was a negative factor, it is possible this was due to the removal of health workers from purchasing decisions. Given that this was a pilot test, and that the system was not used in a clinical setting, we could not examine factors related to actual workflow, patient safety, or social influence. This study shows that the concept of a simplified WebModel could be effective and efficient in reducing information overload in complex clinical decision making. We recommend further study to test this in a clinical setting and gather qualitative data from users regarding the value of the tool in practice.

Beyond the workflow: archivists' aspirations for digital curation practices.

The documentation of archival workflows plays an important role in digital curation practice. Capturing the various steps, tools, people, and software involved at different stages, workflow documentation visually represents complex activities, and at times, invisible labor. In this article, we reflect on findings from the OSSArcFlow project, a three-year, grant-funded initiative to investigate and document workflow activities of 12 cultural heritage institutions using three open-source software systems. Building on previously published research on documentation of current digital curation practices, this article reflects on the challenges the project team encountered in modeling archivists’ aspirations for their workflows. While current practices could be accurately represented in linear process models, archivists’ aspirations for how they might advance digital curation practices extended beyond adding or changing discrete workflow steps and often involved sociotechnical factors that could not be easily mapped. This article presents a taxonomy of archivists’ aspirations for their born-digital archives, grouping these goals together around major themes that emerged throughout the research. Our findings show that workflow documentation is an essential artifact in helping archivists to understand gaps and challenges in their current workflows and to imagine the further development of digital curation tools, systems, and practices. Project participants especially benefited from engaging in this reflection on workflow documentation as part of a community of practitioners, with the opportunity to compare across institutional contexts.

Seismic reinforcement of a linac installation.

The local building requirements to secure medical equipment in seismically active areas in the United States are based on recommendations of the American Society of Civil Engineers. In our institution we have recently acquired new linear accelerators, one of which had to be installed in an existing vault and one in a new vault. Since we are in a seismic active area, changes in the local code required us to start placing the new linacs seismically stable. Here, we describe the necessary steps taken to ensure a seismically sound installation of our linacs. For the linac installation to be seismically stable, the linac base frame has to be seismically fixed into the vault floor. The installation of a new linac into an existing vault requires verification of a structurally sound base frame. Knowledge of the previously applied fixation of such is needed and exploratory removal of grouted floor helped in the verification. Understanding the additional load requirements for the locality allows to account for the existing fixation and can potentially reduce the work needed to achieve seismic fixation requirements. For a prospective seismic installation the new linac base frame can be directly installed with the necessary strength. In addition the actual workflow is straight forward and vendor recommendations can be used. In both cases the vendor provided seismic calculations serve as baseline from which a facility should be work from. It is the facilities task to verify the correct installation of a linac in their specific location. An understanding of the seismic landscape can facilitate an appropriate installation at minimal additional cost.

Task Duplication-Based Scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

A trial of remote hospital clinical clerkship during the COVID-19 pandemic] コロナ禍における代替方法としての遠隔病院実習の試み

Clinical clerkship students were prohibited from visiting the Tokyo General Hospital to prevent the spread of COVID-19 infection. We decided to start remote training using the Microsoft Teams platform in consultation with the universities. The contents were mainly case studies with trainee SGDs and plenary sessions with pharmacists. The trainees followed the actual workflow of pharmaceutical care when examining each case. The discussions focused on the important points of pharmaceutical care so that the trainees would find it easier to understand. Some problems trainees faced were inexperience and recurring symptoms such as headaches. However, they seemed to understand how hospital pharmacists work. A progress report meeting held in the middle of the training period with the university teachers was also beneficial. We concluded that remote training could be an alternative when onsite practical training cannot be carried out. In addition, we believe the clinical clerkship will be more effective by combining remote and onsite even after the pandemic.

Documenting flooding areas calculation: a PROV approach

Flooding events related to waste-lake dam ruptures are one of the most threatening natural disasters in Brazil. They must be managed in advance by public institutions through the use of adequate hydrographic and environmental information. Although the Open Data paradigm offers an opportunity to share hydrographic data sets, their actual reuse is still low because of metadata quality. Our previous work highlighted a lack of detailed provenance information. The paper presents an Open Data approach to improve the release of hydrographic data sets. We discuss a methodology, based on W3C recommendations, for documenting the provenance of hydrographic data sets, considering the workflow activities related to the study of flood areas caused by the waste-lakes breakdowns. We provide an illustrative example that documents, through W3C PROV metadata model, the generation of flooding area maps by integrating land use classification, from Sentinel images, with hydrographic data sets produced by the Brazilian National Institute for Space Research.

A workflow for seismic imaging with quantified uncertainty

The interpretation of seismic images faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Understanding uncertainties’ role and how they influence the outcome is fundamental in the earth sciences and essential in the oil and gas industry decision-making process. Geophysical imaging is time-consuming. When we add uncertainty quantification, it becomes both time and data-intensive. In this work, we propose a workflow for seismic imaging with quantified uncertainty. We build the workflow upon Bayesian tomography, reverse time migration, and image interpretation based on statistical information. The workflow explores an efficient hybrid parallel computational strategy to decrease the reverse time migration execution time. High levels of data compression are applied to reduce data transfer among workflow activities and data storage. We capture and analyze provenance data at runtime to improve workflow execution, monitoring, and debugging with negligible overhead. Numerical experiments on the Marmousi2 Velocity Model Benchmark demonstrate the workflow capabilities. We observe excellent weak and strong scalability, and results suggest that lossy data compression does not hamper the seismic imaging uncertainty quantification. We explore the propagation of the velocity uncertainties to the seismic images by confidence maps and probability density functions in control points. The multi-modal character revealed by such high-order statistics reinforces the need for the machinery we have put together in our workflow.