Regional Development and Interregional Migration Decisions in Jakarta

Purpose: this study aims to examine the community's decision to migrate between regions in the Jabodetabek area using the KRL Commuterline public transportation and analyse regional criteria based on regional development based on Oriented Development Transit, where these criteria become integration with community movements in migrating to an area.Methods: secondary data is used to fnd the number of people in migrating obtained from pt. Kai Indonesia. While to complete and explain each variable to be studied using primary data with several questions through a questionnaire submitted to 398 people who migrate between regions using logistic regression analysis techniques in their measurements. While to analyze the criteria for regional development in each region using an assessment approach from the Institute for Transportation and Development Policy. With qualitative analysis techniques and to assist in this research, a spatial approach is used which is used to display a picture of the distribution of migration.Results: (1) Regional development in each part of the Jabodetabek area is in the silver standard category which indicates that the regional development project has almost met the performance targets that have been conceptualized by the Institute for Transportation and Development Policy. (2) People in making decisions to migrate between regions will be affected by the variables of distance, travel costs, gender, travel time, migration destination and regional development, while age and transit distance cannot provide a large enough influence on people's movements in migrating.Conclusions and Relevance: the results of the study prove that regional development in the Jabodetabek area tends to be a non-metropolitan area where people who move prefer areas that are integrated with public facilities that lead to the destination rather than towards the metropolitan area, this is evidenced by the standard silver criteria obtained in the area in Jabodetabek.

Elastic least-squares reverse time migration of steeply dipping structures using prismatic reflections

The migration of prismatic reflections can be used to delineate steeply dipping structures, which is crucial for oil and gas exploration and production. Elastic least-squares reverse time migration (ELSRTM), which considers the effects of elastic wave propagation, can be used to obtain reasonable subsurface reflectivity estimations and interpret multicomponent seismic data. In most cases, we can only obtain a smooth migration model. Thus, conventional ELSRTM, which is based on the first-order Born approximation, considers only primary reflections and cannot resolve steeply dipping structures. To address this issue, we develop an ELSRTM framework, called Pris-ELSRTM, which can jointly image primary and prismatic reflections in multicomponent seismic data. When Pris-ELSRTM is directly applied to multicomponent records, near-vertical structures can be resolved. However, the application of imaging conditions established for prismatic reflections to primary reflections destabilizes the process and leads to severe contamination of the results. Therefore, we further improve the Pris-ELSRTM framework by separating prismatic reflections from recorded multicomponent data. By removing artificial imaging conditions from the normal equation, primary and prismatic reflections can be imaged based on unique imaging conditions. The results of synthetic tests and field data applications demonstrate that the improved Pris-ELSRTM framework produces high-quality images of steeply dipping P- and S-wave velocity structures. However, it is difficult to delineate steep density structures because of the insensitivity of the density to prismatic reflections.

Deep learning for enhancing multi-source reverse time migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

Deep learning for enhancing multi-source reverse time migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

The Cost-Effectiveness of Supplemental Carnosine in Type 2 Diabetes

In this paper, we assess the cost-effectiveness of 1 g daily of carnosine (an over the counter supplement) in addition to standard care for the management of type 2 diabetes and compare it to standard care alone. Dynamic multistate life table models were constructed in order to estimate both clinical outcomes and costs of Australians aged 18 years and above with and without type 2 diabetes over a ten-year period, 2020 to 2029. The dynamic nature of the model allowed for population change over time (migration and deaths) and accounted for the development of new cases of diabetes. The three health states were ‘Alive without type 2 diabetes’, ‘Alive with type 2 diabetes’ and ‘Dead’. Transition probabilities, costs, and utilities were obtained from published sources. The main outcome of interest was the incremental cost-effectiveness ratio (ICER) in terms of cost per year of life saved (YoLS) and cost per quality-adjusted life year (QALY) gained. Over the ten-year period, the addition of carnosine to standard care treatment resulted in ICERs (discounted) of AUD 34,836 per YoLS and AUD 43,270 per QALY gained. Assuming the commonly accepted willingness to pay threshold of AUD 50,000 per QALY gained, supplemental dietary carnosine may be a cost-effective treatment option for people with type 2 diabetes in Australia.

Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies

In the quest for denser, nimbler, and lower-cost seismic surveys, the industry is seeing a revolution in the miniaturization of seismic equipment, with autonomous nodes approaching the size of a geophone and sources becoming portable by crews on foot. This has created a paradigm shift in the way seismic is acquired in difficult terrains, making zero-environmental-footprint surveys a reality while reducing cost and health, safety, and environmental risk. The simplification of survey operation and the new entry price of seismic surveys unlocked by these technologies are already benefiting industries beyond oil and gas exploration. High trace density seismic has become accessible to industries playing a key role in the net-zero era, such as geothermal and carbon capture, utilization, and storage (CCUS), to which a good understanding of the subsurface geology is crucial to their success. We describe these benefits as observed during an ultra-high-density seismic survey acquired in June 2020 through a partnership between STRYDE, Explor, and Carbon Management Canada over the Containment and Monitoring Institute site. The smallest and lightest source and receiver equipment in the industry were used to achieve a trace density of 257 million traces/km2 over this test site dedicated to CCUS studies. We discuss the operational efficiency of the seismic acquisition, innovative techniques for data transfer and surveying, and preliminary results of the seismic data processing with a focus on the near-surface model and fast-track time migration.