Field Of Geosciences Research Articles

Development of a Large-scale Korean Language Model in the Field of Geosciences

Development of a Large-scale Korean Language Model in the Field of Geosciences

Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)

Generative artificial intelligence systems based on the GPT model have shown groundbreaking capabilities in producing scientific texts, revolutionising how geoscientists research and teach. However, there is a noticeable absence of generative AI systems tailored specifically for geoscience, and the efficacy of GPT technology has not been examined within the Earth Science realm. To fill these gaps, we propose a new artificial intelligence system (GeologyOracle) built upon the GPT-4o model and trained on Earth Science data. It is designed to simulate a conversation with a geoscientist, having the capabilities to analyse geologic datasets, suggest new geoscience hypotheses, explain Earth-Science concepts, and interpret geosites. To evaluate the performance of GeologyOracle, the system was tested with 152 geoscience questions provided and evaluated by a panel of geoscience academics on a scale of 0–10. Performance analysis revealed that 79.6% of the answers scored equal to or above the passing mark of 5, with a tendency to deliver high-quality responses (mean: 6.5; median = 7; interquartile range: 5–8). The results demonstrate that GeologyOracle is effective in performing complex geoscience tasks, such as identifying rocks, fossils, and minerals, and interpreting outcrop, core data, and quantitative datasets. The new AI system has exhibited noteworthy ability in the interpretation of internationally-acknowledged geosites situated within geoparks and nature reserves. The performance of the AI system is comparable to that of trained geoscience specialists, suggesting its potential as an assistant in various geoscience fields, including structural geology, palaeontology, geomorphology, sedimentology, and economic geology. While AI does not render geoscientists obsolete, it offers significant potential for accelerating scientific discovery, automating geoscience research, and assisting educators, students, and geotourists.

KNUST SEG Geophysics Field Camp: A project to study slavery-related archaeology in West Africa

In this article, we describe a field camp in West Africa that explored the use of geophysics in archaeological investigations related to the transatlantic slave trade. Africa's geodiversity and richness of natural resources makes geoscience education essential for social, academic, scientific, and professional growth and development. Field camps serve as a pivotal element of geoscience pedagogy, providing hands-on experiential learning opportunities for budding geoscientists. Due to a lack of funding, there is limited access to and availability of geoscience field camps for students and early-career professionals in Africa. The main goal of our field camp was to delineate archaeological relics related to the transatlantic slave trade in the southeastern part of Ghana. The field camp provided a way to conduct research and train geoscience students in planning and performing geophysical investigations. It also taught students how to process and interpret geophysical data and prepare and present scientific investigation reports. We investigated sites in Aflao, Denu, Hedzranawo, and Keta in the southeastern part of Ghana. Sites of interest included former slave markets, barracoons, and a fort. The project highlighted locations of potential slavery-related archaeological relics and shared the importance of the findings and their implications for understanding the history of transatlantic slavery in Ghana.

Deep learning in standard least-squares theory of linear models: Perspective, development and vision

Inspired by the attractive features of least-squares theory in many practical applications, this contribution introduces least-squares-based deep learning (LSBDL). Least-squares theory connects explanatory variables to predicted variables, called observations, through a linear(ized) model in which the unknown parameters of this relation are estimated using the principle of least-squares. Conversely, deep learning (DL) methods establish nonlinear relationships for applications where predicted variables are unknown (nonlinear) functions of explanatory variables. This contribution presents the DL formulation based on least-squares theory in linear models. As a data-driven method, a network is trained to construct an appropriate design matrix of which its entries are estimated using two descent optimization methods: steepest descent and Gauss–Newton. In conjunction with interpretable and explainable artificial intelligence, LSBDL leverages the well-established least-squares theory for DL applications through the following three-fold objectives: (i) Quality control measures such as covariance matrix of predicted outcome can directly be determined. (ii) Available least-squares reliability theory and hypothesis testing can be established to identify mis-specification and outlying observations. (iii) Observations’ covariance matrix can be exploited to train a network with inconsistent, heterogeneous and statistically correlated data. Three examples are presented to demonstrate the theory. The first example uses LSBDL to train coordinate basis functions for a surface fitting problem. The second example applies LSBDL to time series forecasting. The third example showcases a real-world application of LSBDL to downscale groundwater storage anomaly data. LSBDL offers opportunities in many fields of geoscience, aviation, time series analysis, data assimilation and data fusion of multiple sensors.

Understanding student motivations to attend and experience an optional geoscience field trip at a Hispanic-Serving Institution

This study aimed to use self-determination theory to examine the motivations of a sample of diverse undergraduate students attending a Hispanic-Serving Institution and the benefits they experienced on a field trip to southeastern New Mexico, USA, in the winter of 2023. We used a qualitative, single case study. The case comprised a field trip for undergraduate students—geology majors and non-majors. The field trip was a low-stakes opportunity for students interested in STEM and geoscience to experience fieldwork and explore new terrain at little to no cost to students. We recruited 17 undergraduate students attending the trip to participate in the study. We collected data via one-on-one online interviews prior to and after the field trip, and daily journaling during the trip. We used iterative cycles of coding to analyze the data as a team. We found that students had a mix of motivations to attend the field trip, including enjoyment, prior experiences, a desire to make new and deepen existing relationships, and an interest in learning more about geoscience and camping. We also found that their experiences mirrored their motivations—they benefited from the new knowledge about camping and geoscience, the new relationships they forged, and the enjoyment they derived from being outdoors with friends, looking at and for rocks. Our findings suggest that programs capitalize on students’ curiosity to motivate them to attend field trips, universities offer K-12 programming, and field trip organizers prepare students for the conditions they will encounter on field trips.

A distinctive HPHT platform with different types of large-volume press subsystems at SECUF

Large-volume presses (LVPs) providing large volumes, liquid media, deformation capability, jump compression, and in situ measurements are in great demand for high-pressure research, particularly in the fields of geoscience, condensed matter physics, material science, chemistry, and biology. A high-pressure and high-temperature (HPHT) platform with different LVP subsystems, both solid-state and liquid environments, and nonequilibrium subsystems, has been constructed at the Synergetic Extreme Condition User Facility, Jilin University. This article describes the construction of the different subsystems and provides an overview of the capabilities and characteristics of the different HPHT subsystems. A large sample volume (1000 mm3) at 20 GPa is achieved through the use of a belt-type apparatus in the solid-state subsystem. HPHT conditions (1.8 GPa and 1000 K) are realized in the liquid subsystem through the use of a piston–cylinder-type LVP with optical diamond windows for in situ spectroscopic measurements. A maximum pressure jump to 10.2 GPa can be reached within 20 ms in the nonequilibrium subsystem with the use of an improved bladder-pressurization jump press. Some typical results obtained with different LVPs are briefly reviewed to illustrate the applications and advantages of these presses. In summary, the platform described here has the potential to contribute greatly to high-pressure research and to innovations in high-pressure technology.

Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management

Abstract Soil resource management is fundamentally integral to environmental sustainability and agricultural productivity. The digital elevation model (DEM) is the fundamental data for analyzing landform surfaces, which introduces an opportunity to obtain a broad spectrum of terrain factors to simplify interpreting the patterns and processes in the geoscience field. The accuracy and resolution of DEM are crucial for their effective use, and many algorithms have been developed to interpolate digital elevation data from a set of known points. Although primary topographic variables derived from grid datasets are important, secondary variables, such as the relief index (RFI), play a more critical role in understanding the complicated relationship between soil properties and landform attributes. The RFI is attained from a DEM by calculating the elevation range within a given neighborhood surrounding a central cell. It is an essential predictor of soil natural resource management that measures the degree of differentiation surface relief. In addition, it is beneficial for perceiving the landscape and its management. This study presents a comprehensive zonal analysis comparing the RFI values derived from multiple interpolation-based DEMs. It investigates deterministic and geostatistical interpolators, such as inverse distance weighted and natural neighbor across distinct zones with diverse topographical characteristics. The findings indicated a high correlation between the RFI and the reliability of the DEM, and the natural neighbor technique provided superior performance against others. The results revealed that the choice of spatial interpolation technique significantly affects the accuracy and reliability of RFI models.

Discovering Halite Traces on a Victim’s Clothing through a Forensic Geoscience Analytical Approach: A Suspicious Case in Italy

This suspect case focuses on investigating the presence of halite (NaCl) crystals on the clothing of a deceased individual to determine whether they resulted from immersion in seawater or residual absorption after immersion (i.e., the crystals were left on the clothing after contact with the victim’s wet body). Thirteen clothing samples were collected from various garments worn by the victim and were subjected to optical stereomicroscopy, Scanning Electron Microscopy (SEM), coupled with Energy Dispersive Spectroscopy (EDS), and Simultaneous Thermal Analysis (STA). Optical stereomicroscopy revealed numerous white-colored, vitreous, and greasy luster microcrystals dispersed between fabric fibers, with higher concentrations observed near the hem seams and metal rivets. These microcrystals exhibited predominantly cubic and irregular morphologies. Additionally, sandy particles and organic elements, such as plant fragments and micro seashells, were detected, indicative of coastal environment exposure. SEM-EDS analysis confirmed the presence mainly of sodium and chlorine in stoichiometric ratios consistent with halite, with crystals exhibiting amorphous, needle-shaped, or cubic morphologies. Furthermore, STA analysis identified weight loss events attributed to organic decomposition and halite decomposition at high temperatures, corroborating SEM-EDS findings. The distribution and characteristics of halite crystals, along with other trace elements, support the hypothesis of immersion in seawater while wearing clothing. Specifically, the higher concentrations of halite crystals near thicker fabric portions and metal rivets suggest slower drying rates and longer evaporation times, indicative of immersion rather than residual absorption after swimming. This finding not only helps in determining the victim’s exposure to seawater but also establishes a methodology for distinguishing between different sources of halite residue on clothing. Overall, the comprehensive mineralogical characterization of halite crystals on clothing samples, using best practices of forensic mineralogy, provides valuable forensic insights related to the circumstances that led to the victim’s death. This approach aided investigators in reconstructing the sequence of events, enhancing the accuracy of forensic reconstructions. Moreover, this study contributes to the broader field of forensic geoscience by demonstrating the practical applications of mineralogical analysis in criminal investigations, potentially guiding future research and improving investigative techniques in similar cases.

SpectralGPT: Spectral Remote Sensing Foundation Model.

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS Big Data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; and 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS Big Data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.

Immersive and Interactive 3D Visualization of Large-Scale Geoscientific Data

Abstract Virtual reality (VR) technology has great potential in supporting planetary scientists and the field of geosciences by offering immersive and interactive experiences for data exploration and analysis. This paper shows the opportunities presented by the use of VR technology in the geosciences domain while also identifying the open challenges associated with this integration. Our focus is on highlighting the several benefits that VR brings, including stereo vision, head tracking, and collaborative capabilities. These features can foster knowledge exchange and interdisciplinary research. Nevertheless, the adoption of VR presents certain challenges that need to be addressed. These include maintaining high refresh rates, handling large heterogeneous datasets, and striking a balance between visualization fidelity and performance. Fortunately, significant advancements have been made in high-performance data analysis, progressive data streaming, and real-time visualization, enabling interactive exploration of large-scale datasets within VR environments. To support a broader adoption among domain experts, we propose a visualization approach that scales both with display and with data size. As such, the system can be used to interactively explore large-scale datasets in immersive environments like CAVEs or powerwalls, on HMDs, or on traditional desktop setups. This approach allows for seamless transitions between desktop and VR experiences, leveraging immersive environments for collaboration and outreach activities. In this paper, we present an architectural framework, hardware environments, use cases, lessons learned, and the future potential of VR in this field. Through this scalable system, we anticipate transformative advancements in scientific exploration, collaboration, and knowledge dissemination.

Heterogeneous feature learning network for multimodal remote sensing image collaborative classification

ABSTRACT While multimodal remote sensing data processing has garnered growing interests in the geoscience field, how to effectively extract, represent, and fuse heterogeneous features remains challenging. In this article, a novel heterogeneous feature learning network (HFLN) architecture is investigated for multimodal remote sensing collaborative classification, which takes both advantages of long-range feature modelling capability of transformer as well as local feature extraction of convolutional neural network (CNN). First, the global spectral dependencies are extracted from hyperspectral images using the spectral transformer structure. Then, local spatial invariant characteristics are extracted from multimodal remote sensing images by convolutional operation. Next, heterogeneous spectral and spatial characteristics, having significant structural and semantic differences, are dynamically integrated through the feature coupling module in an interactive manner. Finally, the multi-stage network architecture is utilized for extracting hierarchical characteristics, where the numbers of feature maps in CNN and heads in transformer are gradually increased to represent more complex features. Four benchmark remote sensing datasets (i.e. hyperspectral images and multimodal remote sensing datasets) are utilized for experimental analysis, and extensive experimental results have certified the progressiveness of the investigated approach.

CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification.

In recent years, the classification and identification of surface materials on earth have emerged as fundamental yet challenging research topics in the fields of geoscience and remote sensing (RS). The classification of multi-modality RS data still poses certain challenges, despite the notable advancements achieved by deep learning technology in RS image classification. In this work, a deep learning architecture based on convolutional neural network (CNN) is proposed for the classification of multimodal RS image data. The network structure introduces a cross modality reconstruction (CMR) module in the multi-modality feature fusion stage, called CMR-Net. In other words, CMR-Net is based on CNN network structure. In the feature fusion stage, a plug-and-play module for cross-modal fusion reconstruction is designed to compactly integrate features extracted from multiple modalities of remote sensing data, enabling effective information exchange and feature integration. In addition, to validate the proposed scheme, extensive experiments were conducted on two multi-modality RS datasets, namely the Houston2013 dataset consisting of hyperspectral (HS) and light detection and ranging (LiDAR) data, as well as the Berlin dataset comprising HS and synthetic aperture radar (SAR) data. The results demonstrate the effectiveness and superiority of our proposed CMR-Net compared to several state-of-the-art methods for multi-modality RS data classification.

EMA beamline at Sirius: A versatile platform to probe glass and glass ceramics under extreme thermodynamic conditions

AbstractGlass and glass ceramics are very functional materials, albeit their structural complexity. Their relevance ranges from fundamental science problems in the fields of physics, chemistry, and geoscience, to applications in health areas, engineering, or technological matters that require high performance. Enhancing our understanding of these materials' performance and refining sample preparation methods remains paramount in this field. Synchrotron facilities offer a suite of powerful techniques for the detailed characterization of glasses and glass ceramics. These methods provide valuable insights into their atomic and molecular structure, phase transformations, mechanical behavior, and thermal properties, ultimately contributing to the development of improved materials for a wide range of applications. In‐depth investigations conducted under extreme conditions of pressure and temperature have yielded pivotal insights into densification mechanisms, phase transitions, crystallization kinetics, and their consequential macroscopic properties. The emergence of fourth‐generation synchrotrons brings in a wave of novel experimental possibilities that may exert a profound influence on this field in the coming decade. In this study, we unveil a selection of the remarkable capabilities now accessible to researchers at the Brazilian Synchrotron Light Source—Sirius, within the realm of extreme methods of analysis (EMA) beamline for investigating vitreous systems under extreme conditions.

Data-enhanced revealing of trends in Geoscience

Abstract Purpose This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023. By integrating bibliometric analysis with expert insights from the Deeptime Digital Earth (DDE) initiative, this article identifies key emerging themes shaping the landscape of Earth Sciences①. Design/methodology/approach The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database. To map relationships between articles, citation networks were constructed, and spectral clustering algorithms were then employed to identify groups of related research, resulting in 407 clusters. Relevant research terms were extracted using the Log-Likelihood Ratio (LLR) algorithm, followed by statistical analyses on the volume of papers, average publication year, and average citation count within each cluster. Additionally, expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation, relevance, and impact within Geosciences, and finalize naming of these top trends with consideration of the content and implications of the associated research. This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists. Findings Thirty significant trends were identified in the field of Geosciences, spanning five domains: deep space, deep time, deep Earth, habitable Earth, and big data. These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society, science, and technology. Research limitations The analyzed data of this study only contain those were included in the Web of Science. Practical implications This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science, especially on solid earth. The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study. Originality/value This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.

GeoKnowledgeFusion: A Platform for Multimodal Data Compilation from Geoscience Literature

With the advent of big data science, the field of geoscience has undergone a paradigm shift toward data-driven scientific discovery. However, the abundance of geoscience data distributed across multiple sources poses significant challenges to researchers in terms of data compilation, which includes data collection, collation, and database construction. To streamline the data compilation process, we present GeoKnowledgeFusion, a publicly accessible platform for the fusion of text, visual, and tabular knowledge extracted from the geoscience literature. GeoKnowledgeFusion leverages a powerful network of models that provide a joint multimodal understanding of text, image, and tabular data, enabling researchers to efficiently curate and continuously update their databases. To demonstrate the practical applications of GeoKnowledgeFusion, we present two scenarios: the compilation of Sm-Nd isotope data for constructing a domain-specific database and geographic analysis, and the data extraction process for debris flow disasters. The data compilation process for these use cases encompasses various tasks, including PDF pre-processing, target element recognition, human-in-the-loop annotation, and joint multimodal knowledge understanding. The findings consistently reveal patterns that align with manually compiled data, thus affirming the credibility and dependability of our automated data processing tool. To date, GeoKnowledgeFusion has supported forty geoscience research teams within the program by processing over 40,000 documents uploaded by geoscientists.

Spatial Variations of Deformation along a Strike-Slip Fault: A Case Study of Xianshuihe Fault Zone, Southwest China

The distribution of damage zones around a fault has long been regarded as a frontier and hot spot in the field of geoscience but is still not fully understood. In this study, we conducted field investigations and tests around the Xianshuihe fault zone (XSHF), a left-lateral strike-slip fault with a length of about 400 km located in the eastern margin of the Tibetan Plateau. The results reveal that the fracture frequency and rock strength parameters present a spatially asymmetric distribution along the fault and have a negative power-law correlation with the distance from the fault. The widths of the damage zones are approximately 20.8 km and 17.1 km in the southwest and northeast directions, respectively. Combined with the previous studies, we presented a negative power-law function to depict the correlation between slip displacement and the width of the damage zone and found that the growth rate of damage zone in faults with low displacement is greater than that in those with large displacement. The study demonstrates that the asymmetric distribution of the damage zone surrounding the XSHF is mainly due to the stress redistribution in different damage zones stemming from the left echelon and different activity rates of the blocks on both sides of the XSHF.

Application of the dynamic transformer model with well logging data for formation porosity prediction

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, the Dynamic Transformer model in machine learning was introduced in this study, aiming to improve the accuracy and generalization ability of logging porosity prediction. Dynamic Transformer is a deep learning model based on the self-attention mechanism. Compared with traditional sequence models, Dynamic Transformer has a better ability to process time series data and is able to focus on different parts of the input sequence in different locations, so as to better capture global information and long-term dependencies. This is a significant advantage for logging tasks with complex geological structures and time series data. In addition, the model introduces Dynamic Convolution Kernels to increase the model coupling, so that the model can better understand the dependencies between different positions in the input sequence. The introduction of this module aims to enhance the model's ability to model long-distance dependence in sequences, thereby improving its performance. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the Dynamic Transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the Dynamic Transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, this study introduces a CNN (convolutional neural network)-transformer model, which aims to improve the accuracy and generalization ability of logging porosity prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation of CNNs can effectively learn the mapping relationship of local features, so as to better capture the local correlation in the well log. Transformer models are able to effectively capture complex sequence relationships between different depths or time points. This enables the model to better integrate information from different depths or times, and improve the porosity prediction accuracy. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the CNN-transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the CNN-transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

A novel few-shot learning framework for rock images dually driven by data and knowledge

In the field of geosciences, the integration of artificial intelligence is transitioning from perceptual intelligence to cognitive intelligence. The simultaneous utilization of knowledge and data in the geoscience domain is a universally addressed concern. In this paper, based on the interpretability of deep learning models for rock images, rock features such as structure, texture, mineral and macroscopic identification characteristics were selected to extract a rock identification subgraph from the petrographic knowledge graph and carry out rock type similarity reasoning. Comparative experiments were conducted on few-shot learning of rock images under the supervision of rock type similarity knowledge. The results of the few-shot learning comparisons demonstrate that the supervision of rock type similarity knowledge significantly enhances performance. Additionally, rock type similarity knowledge exhibits a marginal effect on improving few-shot learning performance. Given the absence of Chinese word embedding and large-scale Chinese pre-trained language models in the geological domain, graph embedding based on domain-specific knowledge graphs in geosciences can offer computable geoscience knowledge for research dually propelled by data and knowledge.

Application of geophysical well logs in solving geologic issues: Past, present and future prospect

Geophysical well logs are widely used in geological fields, however, there are considerable incompatibilities existing in solving geological issues using well log data. This review critically fills the gaps between geology and geophysical well logs, as assessed from peer reviewed papers and from the authors’ personal experiences, in the particular goal of solving geological issues using geophysical well logs. The origin and history of geophysical logging are summarized. Next follows a review of the state of knowledge for geophysical well logs in terms of type of specifications, vertical resolution, depth of investigations and demonstrated applications. Then the current status and advances in applications of geophysical well logs in fields of structural geology, sedimentary geology and petroleum geology are discussed. Well logs are used in structural and sedimentary geology in terms of structure detection, in situ stress evaluation, sedimentary characterization, sequence stratigraphy division and fracture prediction. Well logs can also be applied in petroleum geology fields of optimizing sweet spots for hydraulic fracturing in unconventional oil and gas resource. Geophysical well logs are extending their application in other fields of geosciences, and geological issues will be efficiently solved via well logs with the improvements of advanced well log suits. Further work is required in order to improve accuracy and diminish uncertainties by introducing artificial intelligence. This review provides a systematic and clear descriptions of the applications of geophysical well log data along with examples of how the data is displayed and processed for solving geologic problems.