Exploration Team Research Articles

Fluid identification with Graph Transformer using well logging data

The prediction of fluid through well logging is a cornerstone in guiding exploratory efforts in the energy sector. Comprehending the fluid composition beneath the surface empowers exploration teams to effectively gauge the extent, reserves, and caliber of oil and gas resources. This leads to enhanced strategies in exploration and the judicious use of resources. We introduce an innovative machine learning framework named “Graph Transformer” for predicting fluid. This model melds graph convolutional layers with a Transformer module. It excels in decoding spatial and temporal patterns within well logging data, thus unraveling complex geological dependencies by factoring in the interconnectedness of various data points. Additionally, it features a Positional Encoding module to enhance understanding of sequential data points in terms of depth, thereby overcoming the limitations of sequence independence. The Transformer's Multi-Head Self-Attention mechanism is pivotal in discerning and integrating spatial and temporal interconnections across various depths, elevating its capability to represent geological structures. Initially, the model harnesses key well log data like Density, Acoustic, Gamma-ray, and Compensated Neutron Logs for extracting geological features. These insights are then processed through the Graph Transformer to establish relationship between fluid characteristics and logging parameters. Furthermore, we compare this model with other leading models using precision, recall, and accuracy metrics. Experimental findings affirm the model's high accuracy in predicting fluid within intricate geological settings. Its exceptional adaptability makes it apt for various geological conditions and logging tools. Thus, our Graph Transformer model stands out as a sophisticated, efficient machine learning solution in the realm of well logging fluid prediction, offering geologists and engineers precise tools for exploration and development.

Drawing and Disrupting Borders in the Wake of the US-Mexico War

This article advances an argument about two “imagined environments”—my term for the frameworks that human groups use to depict and dwell in more-than-human worlds. The first of these imagined environments arose around the US-Mexican Boundary Commission (1849–59): while failing to fulfill its official objective of building physical border monuments, this team of explorers and engineers still made media that helped two settler states conceptualize and control their borderlands. Against this increasingly efficient exploitation of both humans and nonhumans, a second imagined environment persisted in Mescalero Apache pictographs, Chihene Apache performances, and other Apache media. On our precarious planet, this imagined environment can teach us to stop defining ourselves around fixed places and to remain resilient while migrating through shifting spaces.

Autonomous Drone Landing: Marked Landing Pads and Solidified Lava Flows

Landing is the most challenging and risky aspect of basic multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than global positioning system (GPS) can provide (e.g. package delivery in an urban canyon). We expand on previous methods by actuating the drone’s camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the Rover-Aerial Vehicle Exploration Network (RAVEN) project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone’s behavior, mission structure, or operational environments.

MANAJEMEN ETIK JURNALISTIK (Studi Kasus Pemberitaan Pemilu AJI Kota Kupang)

Choosing a quality leader, especially a political leader, depends on the information that voters receive in the election process. Two important components that play a role in providing information are political parties and the mass media. Between these two components, the mass media holds an important key. Therefore this study aims to determine the management of news coverage in the mass media; how about the news, especially published political news, is processed ethically within the framework of management functions in general. This research was conducted at AJI Kupang City using a qualitative approach. Observations and interviews, supported by scientific references, are techniques for obtaining and analyzing data. The results of the study show that news management in the mass media is generally in line with management functions. The problem is in the online media. In online media, news management does not function properly due to limited human resources. Field studies show that the media and journalists are caught up in ethical issues when reporting on political events; especially news about prospective political leaders. The mass media and journalists are trapped in a decision that violates the journalistic code of ethics by siding with a particular candidate. This partiality can be seen in every report regarding a candidate who is supported by other candidates. Self-integrity is the reason journalists are trapped in this problem. As a solution, researchers offer a management model that is expected to minimize violations of journalistic ethics. The news management model in applying management functions, pays attention to the following elements: 1) Issue exploration team, 2) Supervisory Board, 3) Complaint Center, 4) Education and Training implementing agency, 5) Technical Rules

A Fast VVC Intra Prediction Based on Gradient Analysis and Multi-Feature Fusion CNN

The Joint Video Exploration Team (JVET) has created the Versatile Video Coding Standard (VVC/H.266), the most up-to-date video coding standard, offering a broad selection of coding tools. The maturity of commercial VVC codecs can significantly reduce costs and improve coding efficiency. However, the latest video coding standards have introduced binomial and trinomial tree partitioning methods, which cause the coding units (CUs) to have various shapes, increasing the complexity of coding. This article proposes a technique to simplify VVC intra prediction through the use of gradient analysis and a multi-feature fusion CNN. The gradient of CUs is computed by employing the Sobel operator, the calculation results are used for predecision-making. Further decisions can be made by CNN for coding units that cannot be judged whether they should be segmented or not. We calculate the standard deviation (SD) and the initial depth as the input features of the CNN. To implement this method, the initial depth can be determined by constructing a segmented depth prediction dictionary. For the initial segmentation depth of the coding unit, regardless of its shape, it can also be determined by consulting the dictionary. The algorithm can determine whether to split CUs of varying sizes, decreasing the complexity of the CU division process and making VVC more practical. Experimental results demonstrate that the proposed algorithm can reduce encoding time by 36.56% with a minimal increase of 1.06% Bjøntegaard delta bit rate (BD-BR) compared to the original algorithm.

Dueling Legacies: Which Sultan Deserves Credit for Oman's Oil-Driven Development?

This article argues that Sultan Sa'id bin Taymur played a more fundamental role in developing Oman than what is commonly portrayed. With British backing, Sultan Sa'id centralized control over the contested territories where oil was discovered, despite frequent attacks on exploration teams. When Sa'id was over-thrown in 1970, he relinquished a young but healthy oil sector to his son, Qaboos, paving the way for enormous advances in Omani life. Despite the credit he gets for this transformation, Sultan Qaboos's initial years in power were marked by mis management and declines in production, and poor administration left Oman unable to fully capitalize on the oil price booms of the 1970s.

Fast intra partition and mode prediction for equirectangular projection 360‐degree video coding

360-degree videos have drawn great attention from both the academia and the industry. For transmission and storage of 360-degree video, the joint video exploration team proposed the Versatile Video Coding (VVC) standard. VVC can significantly reduce the bitrate while maintaining the same subjective visual quality compared to the preceding high efficiency video coding. However, the computational complexity of VVC is extremely high which hinders the interactive applications. This paper proposes a fast intra partition method and mode prediction algorithm for equirectangular projection 360-degree video coding. First, a latitude-based preprocessing is introduced to early terminate the Coding Unit (CU) partition in the polar region. Second, the support vector machine is used to predict the CU partition type. Third, the fast intra mode search method accelerates the intra mode prediction. Experimental results show that the proposed algorithm can significantly obtain an average time reduction rate of 60.40% and a Bjontegaard delta rate increase of 1.96%.

Michel Bornens (1938–2022), a visionary scientist

Michel Bornens (1938–2022), a visionary scientist

Predicting mineralization and targeting exploration criteria based on machine-learning in the Serra de Jacobina quartz-pebble-metaconglomerate Au-(U) deposits, São Francisco Craton, Brazil

Defining mineral exploration criteria is a laborious, time-consuming, and generally an empirical task often biased and limited to expert knowledge. To address this problem with a different approach, we used data-driven analysis to make predictions and provide insights about gold mineralization in rocks of the Jacobina Group, São Francisco Craton. The input variables were petrophysical parameters (density, magnetic susceptibility, and electric conductivity) and lithogeochemistry data obtained by X-Ray Fluorescence assays. A machine learning model based on the Random Forests algorithm was applied to predict mineralization in drill core samples. The database used for algorithm training was balanced using the Borderline-SMOTE technique to provide approximately the same numbers of samples of the two classes in the mineral status parameter (i.e., ore and barren samples). The quality of the predictions was assessed with different datasets (i.e., training, testing, each drill core separately, and all samples) and by parameters. The average accuracies were 0.87 for cross-validation training, 0.91 for testing, and 0.86 for all samples. Also, the model allowed us to estimate and rank the importance of the input variables to the prediction. These estimates were validated by an interpretation of optical and scanning electron microscopy petrographic analysis, which was carried out to understand the relationship between minerals of different stages and gold mineralization. Thus, the techniques applied in this work are helpful to decreasing the time spent in data integration and interpretation, since mineral exploration teams can easily replicate this approach.

The Jurassic-Cretaceous Paleogeography Of The Sula Area, North Maluku

The study of paleogeography and hydrocarbon potentiality of the Sula area, North Maluku has been conducted by the Lemigas Exploration team. This paper specifically presents a paleogeography of the Jurassic-Cretaceous age of the Sula area as a part of the result of this study. In this paper, paleogeography means palaeoenvironment which is defined based on biostratigraphy. Data used in this paper are mostly secondary data obtained from National Data Center which is combined with primary data collected during field work campaign. The subsurface data analysis allows subdivision of 7 depositional sequences throughout Jurassic-Cretaceous succession. In fact, each sequence mostly consists of transgressive and highstand system tracts. Lowstand system tract only occurs in the earliest sequence. Sequences 1 (Bobong Formation), 2, 3 and 4 (Buya Formation) are assigned to the Jurassic age, whilst sequences 5, 6 and 7 (Buya Formation) are attributed to the Cretaceous age. Generally, the depositional environment of most sequences is getting deeper toward the North. The shallowest environment takes place in non-marine setting, whereas the deepest environment occurs in outer neritic (100m-200m). It is most likely that Jurassic-Cretaceous depocenter was situated in the northern part of the study area. However, it is required additional data to confirm this interpretation.

A Novel Mining Approach for Data Analysis and Processing Using Unmanned Aerial Vehicles

In the mining industry, smart surveying and exploration operations for the minerals are essential during mining missions. Usually, these missions are performed in remote areas that do not have a wireless communications infrastructure. This paper proposes to use the unmanned aerial vehicle (UAV) as a relay communication node between the exploration team and the ground control station (GCs). UAV can act as a relay node to provide mobile, flexible, and reliable communication links in remote environments and complex topologies. In this work, the pathloss models in millimeter‐wave technology are considered because they provide massive data rates for line of sight scenarios. The optimization problem of identifying a 3D location and trajectory of the UAV relay node is formulated to maximize the total team members’ data rate. Because the problem is non‐convex, the particle swarm optimization algorithm is used to solve it and determine an efficient location and trajectory of the UAV.

Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network

Versatile video coding (H.266/VVC), which was newly released by the Joint Video Exploration Team (JVET), introduces quad-tree plus multi-type tree (QTMT) partition structure on the basis of quad-tree (QT) partition structure in High Efficiency Video Coding (H.265/HEVC). More complicated coding unit (CU) partitioning processes in H.266/VVC significantly improve video compression efficiency, but greatly increase the computational complexity compared. The ultra-high encoding complexity has obstructed its real-time applications. In order to solve this problem, a CU partition algorithm using convolutional neural network (CNN) is proposed in this paper to speed up the H.266/VVC CU partition process. Firstly, 64 × 64 CU is divided into smooth texture CU, mildly complex texture CU and complex texture CU according to the CU texture characteristics. Second, CU texture complexity classification convolutional neural network (CUTCC-CNN) is proposed to classify CUs. Finally, according to the classification results, the encoder is guided to skip different RDO search process. And optimal CU partition results will be determined. Experimental results show that the proposed method reduces the average coding time by 32.2% with only 0.55% BD-BR loss compared with VTM 10.2.

Notice of Violation of IEEE Publication Principles: VVC/H.266 Intra Mode QTMT Based CU Partition Using CNN

Notice of Violation of IEEE Publication Principles <br><br>“VVC/H. 266 Intra Mode QTMT Based CU Partition Using CNN” <br>by Sameena Javaid, Safdar Rizvi, Muhammad Talha Ubaid, Abdullah Tariq <br>in IEEE Access, April 2022 <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper copied content from the article below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> “DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC” <br> by Tianyi Li, Mai Xu, Runzhi Tang, Ying Chen, Qunliang Xing <br>in IEEE Transactions on Image Processing, Vol 30, 2021 <br><br> <br/> The latest standard for video coding is versatile video coding (VVC) / H.266 which is developed by the joint video exploration team (JVET). Its coding structure is a multi-type tree (MTT) structure, which consists of two types of trees that are Ternary Tree (TT) and Binary Tree (BT). Due to the use of brute force quest for residual rate-distortion the quad-tree and multi-type tree (QTMT) structure of the coding unit (CU) split and contributes over 98% of the encoding time. This structure is efficient in coding, however, increases computational complexity. The current paper proposes a deep learning technique to predict the QTMT based CU split rather than just the brute-force QTMT method to substantially speed up the time of the encoding process for VVC/H.266 intra mode. In the first phase, we developed an extensive database containing ample CU splitting patterns and various streaming videos. In the second phase, we suggest a multi-level exit CNN (MLE-CNN) model with a redundancy removal mechanism at different levels to determine the CU partition. In the third phase, for the training of MLECNN model we have established the adaptive loss function and analyzing the both unknown number of partition modes and the focus on RD cost minimization. Finally, a variable threshold decision system is established to achieve the targeted low complexity and RD performance. Ultimately experimental findings show that VVC/H.266 encoding time has reduced to 69.11% from 47.91% with insignificant bjontegaard delta bit rate (BDBR) to 2.919% from 1.023% which performs better than the existing futuristic and modern approaches.

Significance of Sample Repeatability Index in the Mining Industry

The objective of this research is to establish the importance of Sample Repeatability Index (SRI) as a measure to ensure error minimisation in exploration drilling protocols, during sample preparation of the prospect areas. Almost all the stages entailed in the exploration stages involve sampling. Samples collected and prepared within the field are well checked for a good Quality Assurance and Quality Control (QA/QC) methods. However, samples sent out for testing in the laboratory outside the field are not under the control of the exploration team, hence a measure must be taken to assess the efficiency of the laboratory and accuracy of the laboratory results. Sample Repeatability Index is one of the tools that can be used to ensure the degree of confidence in the laboratory results. To achieve this, QA/QC procedures were employed to monitor precision, accuracy and potential sample contamination in order to ensure confidence in the sampling system. A total of twelve thousand and eighty-one samples taken from eight different deposits, considered within a 4-year period were used in this study. Field duplicate pairs were used to check sample repeatability, Relative Difference and Half Absolute Relative Difference (HARD) plot were used to calculate Sample Repeatability Indexes for the deposits. Results of the study showed that Sample Repeatability Index for all the deposits with exception of deposit 2 fell below the Customer Specification Threshold (CST). Deposit 2 produced somewhat better of 77% at 20% precision as compared to the other deposits. Deposit 3 recorded poorly of 60% at 20% precision. It is advisable to have sampling protocol designed to suit different geological domain on each individual deposit.

CNN-based Fast Split Mode Decision Algorithm for Versatile Video Coding (VVC) Inter Prediction

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

Relationships between personality and social functioning, attitudes towards the team and mission, and well-being in an ICE environment

For long-duration space missions to be successful, it is necessary to develop an understanding of the psychological factors that promote effective coping in isolated, confined, and extreme (ICE) environments. We evaluated relationships between Big Five personality traits and social functioning (liking one's teammates, social conflict, and social cohesion), anxiety and sad affect, well-being (health and psychological difficulties, symptoms of depression), and attitudes towards the mission and team. These variables were assessed with pre- (N = 105) and post- (N = 34) mission surveys as well as daily reports (N = 74) during the mission. Participants were crew members with a variety of work roles who completed 1- to 18-month missions in one of three stations in Antarctica. Findings indicate that neuroticism is related to increased social conflict, anxiety, and health and psychological problems, as well as more negative perceptions of teammates and social cohesion. Alternately, conscientiousness in particular may predispose crew members to form positive attitudes towards their expedition and may protect against symptoms of depression. These results carry important implications for selecting individuals for Antarctic expeditions and, with necessary caveats, space exploration teams.

Complexity Analysis of New Future Video Coding (FVC) Standard Technology

Future Video Coding (FVC) is a modern standard in the field of video coding that offers much higher compression efficiency than the HEVC standard. FVC was developed by the Joint Video Exploration Team (JVET), formed through collaboration between the ISO/IEC MPEG and ITU-T VCEG. New tools emerging with the FVC bring in super resolution implementation schemes that are being recommended for Ultra-High-Definition (UHD) video coding in both SDR and HDR images. However, a new flexible block structure is adopted in the FVC standard, which is named quadtree plus binary tree (QTBT) in order to enhance compression efficiency. In this paper, we provide a fast FVC algorithm to achieve better performance and to reduce encoding complexity. First, we evaluate the FVC profiles under All Intra, Low-Delay P, and Random Access to determine which coding components consume the most time. Second, a fast FVC mode decision is proposed to reduce encoding computational complexity. Then, a comparison between three configurations, namely, Random Access, Low-Delay B, and Low-Delay P, is proposed, in terms of Bitrate, PSNR, and encoding time. Compared to previous works, the experimental results prove that the time saving reaches 13% with a decrease in the Bitrate of about 0.6% and in the PSNR of 0.01 to 0.2 dB.

ViSTRA2: Video coding using spatial resolution and effective bit depth adaptation

We present a new video compression framework (ViSTRA2) which exploits adaptation of spatial resolution and effective bit depth, down-sampling these parameters at the encoder based on perceptual criteria, and up-sampling at the decoder using a deep convolution neural network. ViSTRA2 has been integrated with the reference software of both the HEVC (HM 16.20) and VVC (VTM 4.0.1), and evaluated under the Joint Video Exploration Team Common Test Conditions using the Random Access configuration. Our results show consistent and significant compression gains against HM and VVC based on Bjønegaard Delta measurements, with average BD-rate savings of 12.6% (PSNR) and 19.5% (VMAF) over HM and 5.5% (PSNR) and 8.6% (VMAF) over VTM.

Adaptive CU partition and early skip mode detection for H.266/VVC

The Joint Video Exploration Team (JVET) has started to develop the next-generation video coding standard-H.266/Versatile Video Coding (H.266/VVC) based on H.265/High Efficiency Video Coding (H.265/HEVC) to provide higher compression performance. The H.266/VVC supports the flexible quadtree with a nested multi-type tree (QTMT) partition structure including quadtree (QT), binary tree (BT), and ternary tree (TT). The coding unit (CU) sizes range from 128 to 4 for the luma component or from 64 to 2 for the chroma component in the QTMT splitting structure. The introduction of small CU size, i.e., 2×N, leads to inefficient hardware implementation because it causes pipeline delayed and needs to process 2×N pixels in the hardware architecture. In addition, the inter or bi-predicted of small CU requires a higher memory bandwidth than the bi-predicted of 8×8 CU in H.266/VVC. To solve the above issues, we introduce a fast method to accelerate CU partition and mode decision, including an adaptive CU partition method and early skip mode detection method. The proposed algorithm consists of two parts: (1) adaptive remove 2×N CUs by skipping BT and TT splitting mode; (2) early skip bi-predicted or inter prediction of small CU. The experimental results demonstrate that the proposed scheme can save 47% coding time while maintaining the coding performance.

Evaluating the Coding Performance of 360° Image Projection Formats Using Objective Quality Metrics

Recently, 360° content has emerged as a new method for offering real-life interaction. Ultra-high resolution 360° content is mapped to the two-dimensional plane to adjust to the input of existing generic coding standards for transmission. Many formats have been proposed, and tremendous work is being done to investigate 360° videos in the Joint Video Exploration Team using projection-based coding. However, the standardization activities for quality assessment of 360° images are limited. In this study, we evaluate the coding performance of various projection formats, including recently-proposed formats adapting to the input of JPEG and JPEG 2000 content. We present an overview of the nine state-of-the-art formats considered in the evaluation. We also propose an evaluation framework for reducing the bias toward the native equi-rectangular (ERP) format. We consider the downsampled ERP image as the ground truth image. Firstly, format conversions are applied to the ERP image. Secondly, each converted image is subjected to the JPEG and JPEG 2000 image coding standards, then decoded and converted back to the downsampled ERP to find the coding gain of each format. The quality metrics designed for 360° content and conventional 2D metrics have been used for both end-to-end distortion measurement and codec level, in two subsampling modes, i.e., YUV (4:2:0 and 4:4:4). Our evaluation results prove that the hybrid equi-angular format and equatorial cylindrical format achieve better coding performance among the compared formats. Our work presents evidence to find the coding gain of these formats over ERP, which is useful for identifying the best image format for a future standard.