Identification Of Rock Types Research Articles

New and Highly Accurate Static Young's Modulus Model Using Machine Learning Techniques.

Static Young's modulus (E s) is a critical property required in numerous petroleum calculations. Various models to forecast E s have been proposed in the literature. However, existing models, by and large, lack precision and are confined to specific data set ranges. This study proposes an alternative approach for E s determination, utilizing different machine learning methods, such as an adaptive neuro-fuzzy inference system (ANFIS). In these proposed methods, the predictor variables include bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc). The models were trained on a data set comprising 1853 hydrocarbon reservoir rock samples from globally diverse locations. They were evaluated using trend, group error, and statistical error analyses. To test the efficacy of the proposed models, the optimally performing model was identified and used to detect the rock types along with the previously published models. Results indicated that ANFIS is the optimum model and can predict E s with an average absolute percentage relative error (AAPRE) of 5.1% and a correlation coefficient (R) of 0.9602. The ANFIS method has some benefits over other machine learning approaches insofar as its superiority in reaching a quicker decision about the mapped relationship between the inputs and outputs because it combines artificial neural networks and fuzzy logic in one tool. The ANFIS can perform a highly nonlinear mapping and displays a better learning ability. The proposed ANFIS model demonstrates its ability to capture accurate physical relationships between input rock properties and E s through trend analysis, which shows that increasing the RHOB increases the E s. Contrarily, increasing the DTc and DTs reduces the E s. Furthermore, the ANFIS model can accurately detect the rock types based on its E s determinations. This research demonstrates the importance of accurately predicting E s for the proper identification of rock types. Thus, this study offers potential advancements in geological assessments of hydrocarbon reservoirs and improvements in many petroleum engineering applications.

Advancing geological image segmentation: Deep learning approaches for rock type identification and classification

This study aims to tackle the obstacles linked with geological image segmentation by employing sophisticated deep learning techniques. Geological formations, characterized by diverse forms, sizes, textures, and colors, present a complex landscape for traditional image processing techniques. Drawing inspiration from recent advancements in image segmentation, particularly in medical imaging and object recognition, this research proposed a comprehensive methodology tailored to the specific requirements of geological image datasets. To establish the dataset, a minimum of 50 images per rock type was deemed essential, with the majority captured at the University of Las Palmas de Gran Canaria and during a field expedition to La Isla de La Palma, Spain. This dual-source approach ensures diversity in geological formations, enriching the dataset with a comprehensive range of visual characteristics. The study involves the identification of 19 distinct rock types, each documented with 50 samples, resulting in a comprehensive database containing 950 images. The methodology involves two crucial phases: initial preprocessing of the dataset, focusing on formatting and optimization, and subsequent application of deep learning models—ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large. Preparing the dataset is crucial for improving both the quality and relevance, thereby to ensure the optimal performance of deep learning models, the dataset was preprocessed. Following this, transfer learning or more specifically fine-tuning is applied in the subsequent phase with ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large, leveraging pre-trained models to enhance classification task performance. After fine-tuning eight deep learning models with optimal hyperparameters, including ResNet101, ResNet152, Inception-v3, DenseNet169, DenseNet201, MobileNet-v3-small, MobileNet-v3-large, and EfficientNet-v2-large, comprehensive evaluation revealed exceptional performance metrics. DenseNet201 and InceptionV3 attained the highest accuracy of 98.49% when tested on the original dataset, leading in precision, sensitivity, specificity, and F-score. Incorporating preprocessing steps further improved results, with all models exceeding 97.5% accuracy on the preprocessed dataset. In K-Fold cross-validation (k = 5), MobileNet V3 large excelled with the highest accuracy of 99.15%, followed by ResNet101 at 99.08%. Despite varying training times, models on the preprocessed dataset showed faster convergence without overfitting. Minimal misclassifications were observed, mainly among specific classes. Overall, the study's methodologies yielded remarkable results, surpassing 99% accuracy on the preprocessed dataset and in K-Fold cross-validation, affirming the efficacy in advancing rock type understanding.

A new method of rock type identification based on transformer by utilizing acoustic emission.

The characterization and analysis of rock types based on acoustic emission (AE) signals have long been focal points in earth science research. However, traditional analysis methods struggle to handle the influx of big data. While signal processing methods combined with deep learning have found widespread use in various process analyses and state identification, effective feature extraction using progressive fusion technology still faces challenges in the field of intelligent rock type identification. To address this issue, our study proposes a novel framework for rock type identification based on AE and introduces a new signal identification model called 3CTNet. This model integrates convolutional neural networks (CNNs) and Transformer encoder, intelligently identifying AE of different rock fractures by establishing dependencies between adjacent positions within the data and gradually extracting advanced features. Furthermore, we experimentally compare five oversampling methods, ultimately selecting the adaptive synthetic sampling method (ADASYN) to balance the dataset and enhance the model's robustness and generalization ability. Comparison of the internal structure of our model with a series of time series processing models demonstrates the effectiveness of the proposed model structure. Experimental results showcase the high identification accuracy of the intelligent rock type identification model based on 3CTNet, with an overall identification accuracy reaching 98.780%. Our proposed method lays a solid foundation for the efficient and accurate identification of formation rock types in geological exploration and oil and gas development endeavors.

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs.

To delineate a powerful reservoir model, rock type identification is an essential task. Recognizing intervals with promising reservoir quality in a heterogeneous reservoir, such as the Pab Formation, using well logs is critical for better exploration, because coring programs are always impractical due to time and cost constraints. Rock types are described by specific log responses, which are ultimately distinguished with the help of electrofacies. The current study uses a cluster analysis technique for the evaluation of reservoir rock types in the identified sand units. K-means cluster analysis is employed to define electrofacies, which are ultimately classified into four rock types on the basis of reservoir quality, from bad to excellent. Rock typing using cluster analysis has been done for four wells, and a correlation has been made to depict changes in electrofacies. From well-to-well correlation, it can be inferred that the reservoir quality of the Pab Formation at the lower portion of Zamzama-02 and 05 wells is excellent and is defined by rock type 4. The Zamzama-03 well in the southwestern region, on the other hand, has good to moderate reservoir quality, as demonstrated by dominating rock types 3 and 2, respectively. The applied prediction technique to the studied field provides continuous rock type identification for the entire reservoir. Using this methodology in defining rock type is cost-effective, requires less time in the demarcation of zones of interest, and is more accurate than manual analysis of the heterogeneous and thick Pab Formation. The studied approach is not only useful in the exploitation of the heterogeneous Pab Formation but also can be applied to other heterogeneous sandstone reservoirs elsewhere.

Geology and characteristics of petrographic rocks in the region of Trepça, Kosovo

Purpose. The purpose of this paper is to accomplish a thorough identification of rock types occurring within the Mitrovica region by describing in detail all encountered varieties. The authors aim to determine the discontinuity or continuity of all inter-formational boundaries, crucial for accurate delineation on the ground and complete reflection on a 1:25000 scale map. Additionally, the objective is to identify the nature of contact between rock types and provide a detailed their description. Methods. Field exploration in the Mitrovica region was conducted for several months. Rock identification involved detailed sampling and petrographic analysis, including thin section preparation of magmatic rocks. The method included sample preparation, polarized light source interactions with minerals and observation of optical properties. Key properties observed are birefringence, interference colors, extinction angles, identification and analysis. Findings. Based on the study of stratigraphic units and geological descriptions of mineral outcrop areas, various types of rocks through petrographic microscope preparation, as well as chemical and geochemical analyses, have been differentiated. The Mitrovica area encompasses the following lithostratigraphic units: harzburgite, gabbro, diabase, metasandstone, sandstone, quartzite and grainstone with calcium (Aeolisaccus sp.; Bioclastic grainstone with dacycladal algae and small milliolides; Salpin-goporella sp.). Originality. The originality of the research lies in employing an optical microscope for the precise identification of rocks. Through the re-search carried out in the study area, we obtained a comprehensive petrographic description of mineral composition, texture, and mineralization, facilitating the assessment of the area exploitation potential. Practical implications. The petrographic exploration yielded the conclusion that the Pb-Zn mineralization is present in the study area, which is a sig-nificant finding for the advancement of the mining sector and the local community, provided that environmental preservation measures are upheld and responsible methods of area exploitation are implemented.

Deep learning-based rock type identification using drill vibration frequency spectrum images

ABSTRACT Rock identification is crucial in the mining industry. It provides useful information regarding the geological characteristics of an area, which can be applied to drill-bit selection, optimisation of drilling parameters, and selection of blasting materials. Common methods for this purpose are core drilling and core analysis. Although these methods are dependable, they lag and are expensive. Recent studies have highlighted a need for an automatic and reliable system for rock identification during the drilling process. Hence, this study establishes a new and reliable method for automatically identifying rocks using drill vibrations and deep-learning algorithms. The sample rocks were drilled using a rotary percussion rock drifter with accelerometers mounted on a guide cell. The fast Fourier transform algorithm was used to convert the drill vibration signals into frequency spectrum images, which were subsequently used as inputs to the deep-learning algorithms. Study rock-identification models were constructed using three convolutional neural networks: ResNet-50, Inception-v3, and DenseNet-201. The classification accuracy was used as a metric to assess the performance of the models. Subsequently, a Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm was used to identify noteworthy frequencies responsible for rock predictions. With the help of deep-learning algorithms, drill vibrations could be used to identify different rocks during the drilling process. The Inception-v3 model exhibited optimum performance, with a classification accuracy of 99.0%. Grad-CAM indicated that frequencies from 0 to 8000 Hz are important for rock classification. This approach offers an automatic, low-latency, and dependable rock-identification system.

Propagating image-based rock classes from cored to non-cored depth intervals using supervised machine learning

High-resolution image data is instrumental in quantifying the variation of rock fabric in formation evaluation that conventional well logs fail to capture. However, the acquisition of image data for all wells in a reservoir is restricted due to technology limitations and the high cost involved. The main objective of this paper is to propose a workflow to extrapolate rock fabric information from imaged depth intervals/wells to nearby un-imaged depth intervals/wells for enhanced formation evaluation in un-imaged wells. To propagate rock fabric information, we trained a supervised learning algorithm in a well with core photos, CT-scan images, and conventional well logs. Subsequently, the trained model is used to identify fabric-influenced well-log-based rock classes using only conventional well logs in un-imaged depth intervals/well (referred to as fabric-based rock classes). We applied the proposed workflow to two wells in a siliciclastic formation with spatial variation in rock fabric. Comparison of the detected fabric-based rock classes in the nearby depth intervals/well using the trained model with image-based rock classes resulted in an average accuracy of 94%. The outcomes of this paper contribute to accelerated identification of rock types honouring rock fabric while minimizing extensive imaging and coring efforts. Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows

Classifying rock types by geostatistics and random forests in tandem

Rock type classification is crucial for evaluating mineral resources in ore deposits and for rock mechanics. Mineral deposits are formed in a variety of rock bodies and rock types. However, the rock type identification in drill core samples is often complicated by overprinting and weathering processes. An approach to classifying rock types from drill core data relies on whole-rock geochemical assays as features. There are few studies on rock type classification from a limited number of metal grades and dry bulk density as features. The novelty in our approach is the introduction of two sets of feature variables (proxies) at sampled data points, generated by geostatistical leave-one-out cross-validation and by kriging for removing short-scale spatial variation of the measured features. We applied our proposal to a dataset from a porphyry Cu–Au deposit in Mongolia. The model performances on a testing data subset indicate that, when the training dataset is not large, the performance of the classifier (a random forest) substantially improves by incorporating the proxy features as a complement to the original measured features. At each training data point, these proxy features throw light based on the underlying spatial data correlation structure, scales of variations, sampling design, and values of features observed at neighboring points, and show the benefits of combining geostatistics with machine learning.

Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery.

Earth observation by remote sensing plays a crucial role in granite extraction, and many current studies use thermal infrared data from sensors such as ASTER. The challenge lies in the low spatial resolution of these satellites, hindering precise rock type identification. A breakthrough emerges with the Thermal Infrared Spectrometer (TIS) on the Sustainable Development Science Satellite 1 (SDGSAT-1) launched by the Chinese Academy of Sciences. With an exceptional 30 m spatial resolution, SDGSAT-1 TIS opens avenues for accurate granite extraction using remote sensing. This study, exemplified in Xinjiang's Karamay region, introduces the BR-ISauvola method, leveraging SDGSAT-1 TIS data. The approach combines band ratio with adaptive k-value selection using local grayscale statistical features for Sauvola thresholding. Focused on large-scale granite extraction, results show F1 scores above 70% for Otsu, Sauvola, and BR-ISauvola. Notably, BR-ISauvola achieves the highest accuracy at 82.11%, surpassing Otsu and Sauvola by 9.62% and 0.34%, respectively. This underscores the potential of SDGSAT-1 TIS data as a valuable resource for granite extraction. The proposed method efficiently utilizes spectral information, presenting a novel approach for rapid granite extraction using remote sensing TIS imagery, even in scenarios with low spectral resolution and a single data source.

Robust reservoir identification by multi-well cluster analysis of wireline logging data

A novel clustering method is applied to well logs for improved rock type identification in hydrocarbon formations. For grouping the objects in the multi-dimensional data space, we propose a Most Frequent Value (MFV) based clustering technique applied to natural gamma ray, bulk density, sonic, photoelectric index, and resistivity logs. The MFV method is a robust estimator, which assists in finding the cluster centers more reliably than a more noise sensitive K-means clustering approach. The result of K-means cluster analysis highly depends on the choose of the initial centroids. To reduce the risk of inappropriately chosen starting values, we apply a histogram-based selection method to give the best position of the initial cluster centers. We assure the robustness of the solution by calculating the centroid as the MFV of the cluster elements and defining the overall deviation of cluster elements from the center by a weighted Euclidean (Steiner-) distance. The proposed workflow relies on a fully automated weighting of the cluster elements, which does not require a constraint on the statistical distribution of the observed variables. The processing of synthetic data shows high noise rejection capability and efficient cluster recognition even beside considerable amount of outlying and missing data; the accuracy is measured by the difference between the estimated and the exactly known distribution of cluster numbers. The clustering tool is first applied to single borehole data, then the procedure is extended to multi-well logging datasets to reconstruct the multi-dimensional spatial distributions of clusters revealing the lithological and petrophysical characteristics of the studied formations. A large in situ dataset acquired from several boreholes traversing Hungarian gas-bearing clastic reservoirs of Miocene age is analyzed. The accuracy of the field results is confirmed by core permeability measurements, independent well log analysis and a gradient metrics characterizing the noise rejection capability of the clustering method.

Reservoir Characterization and Rock Typing of Carbonate Reservoir in the Southeast of Iraq

Flow unit and reservoir rock type identification in carbonates are difficult due to the intricacy of pore networks caused by facies changes and diagenetic processes. On the other hand, these classifications of rock type are necessary for understanding a reservoir and predicting its production performance in the face of any activity. The current study focuses on rock type and flow unit classification for the Mishrif reservoir in Iraq's southeast and the study is based on data from five wells that penetrate it. Integration of several methods was used to determine the flow unit based on well log interpretation and petrophysical properties. The flow units were identified using the Quality Index of Rock and the Indicator of Flow Zone. The Winland correlation was used to determine the pore throat size. The Lucia classification was based on fabric rock number, and cluster analysis detects rock types using well log data within the Mishrif Formation. Four rock types have been specified by the combination of these approaches grainstone-packstone, packstone-wackestone, Wackestone-Mudstone and Mudstone.

Identification of Rock Types from Iron Sand at Pasia Jambak Beach, Padang, West Sumatra

West Sumatera is a region that has an abundance of natural resources in the form of iron sand, one of which is in the area of Pasia Jambak Beach, Padang. Pasia Jambak Beach is one type of beach in rumps and sandy beaches, which has grayish-brown sand and rough grains on its surface. Iron sand could be formed due to weathering of rocks and materials found around the coast, such as old ships and others. This study aims to see the type rock of origin and the composition of the magnetic mineral elements found in the iron sand of Pasia Jambak Beach. The composition of iron sand-forming elements can be seen using XRF. The analysis results using XRF showed that the most dominant element in the samples was Si (Silica) and Fe (Ferrum). Based on the SiO2 vs K2O diagram, it can be seen that the iron sand in the Pasia jambak Beach area comes from weathering of Basalt, Dacite, and Latite rocks. So, it could be concluded that the original rock group from iron sand at Pasia Jambak Beach is the Calc-Alkaline group (medium Potassium content) and High K Calc-Alkaline (high Potassium content).

Petrographic and geochemical characteristics of rocks in the Drenas region, Kosovo

Purpose. The paper purpose is to provide complete identification of rock types and the geochemical characteristics in the Drenas region. The authors seek to determine the discontinuity or continuity of all inter-formational boundaries to accurately delineate them in the field and fully reflect on a 1:25000 scale map. In addition, they reveal the nature of the contact between rock types, giving its detailed description and geochemical characteristics. Methods. Geological study is focused on the following facts: complete identification of all rock types on the basis of studying their samples, preparation of thin sections for petrographic study, chemical and geochemical analysis. The methods applied for geochemical analysis use the MinPet software. This software, used for mineral chemistry, is programmed to process and recalculate the results of major-element analysis according to the most common normative calculation schemes. This software is used in scientific works for constructing diagrams of rock calcification and geochemical interpretations according to the components of Na2O + K2O/SiO2, as well as rare earth elements according to the component SiO2/Zr/TiO2. The exploration field trips have been conducted to identify and describe areas of mineral outcrop. Findings. Based on the study of geochemical analysis and the petrographic description of mineral outcrops, as well as examination under a microscope, different types of rocks have been identified. Based on these types, the origin, age and spread of these rocks in this region have been determined. Originality. The originality of the study is in the use of the analysis results obtained in the AcmelLabs Laboratory, Vancouver, Canada, for major elements, rare earth elements and trace elements, which are shown in the diagrams presented in this paper. Practical implications. The geochemical analysis leads to the conclusion that the studied area has Ni-mineralization, which is important for the development of mining sector, as well as for the community while preserving the environment and applying an adequate method of exploiting the area.

Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type

Nowadays, there are significant issues in the classification of lithofacies and the identification of rock types in particular. Zamzama gas field demonstrates the complex nature of lithofacies due to the heterogeneous nature of the reservoir formation, while it is quite challenging to identify the lithofacies. Using our machine learning approach and cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects and provide an accurate result even when the user is inexperienced. To constrain accurate reservoir models, rock type identification is a critical step in reservoir characterization. Many empirical and statistical methodologies have been established based on the effect of rock type on reservoir performance. Only well-logged data are provided, and no cores are sampled. Given these circumstances, and the fact that traditional methods such as regression are intractable, we have chosen to apply three strategies: (1) using a self-organizing map (SOM) to arrange depth intervals with similar facies into clusters; (2) clustering to split various facies into specific zones; and (3) the cluster analysis technique is used to identify rock type. In the Zamzama gas field, SOM and cluster analysis techniques discovered four group of facies, each of which was internally comparable in petrophysical properties but distinct from the others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) and Water Saturation (Sw) are used to generate these results. The findings and behavior of four facies shows that facies-01 and facies-02 have good characteristics for acting as gas-bearing sediments, whereas facies-03 and facies-04 are non-reservoir sediments. The outcomes of this study stated that facies-01 is an excellent rock-type zone in the reservoir of the Zamzama gas field.

Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis

Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.

Investigation of the Causes and Factors Generating Land Instability in the Berbeşti Mining Basin

Abstract Romania’s new commitments to give up coal-based energy production entail the gradual closure of the last four open-pits in operation in Berbești Mining Basin, thus, raising the issue of primarily ensuring the stability of the lands in these perimeters in order to reintroduce them, too, into the economic circuit as soon as possible. Considering the numerous negative geo-mining phenomena that have taken place over time in the perimeters of Berbești Mining Basin, respectively landslides and other types of geotechnical phenomena, it is necessary to investigate the causes and factors that favor their manifestation so that their influence can be reduced, removed or simply taken into resizing calculations. The paper presents the results of a study whose purpose is to investigate the factors and causes that generate phenomena of instability of natural and artificial slopes in the area of Berbești Mining Basin. The main research methods applied in this paper were in situ observations, analyzes and laboratory tests, identification of rock types and their behaviors in various weather and humidity conditions, determination of physical and mechanical characteristics, and interpretation of results.

Surface roughness estimation and rock type identification by ultrasonic and optical techniques

Surface roughness estimation and rock type identification by ultrasonic and optical techniques

Petrographic characteristics in the central part of Kosovo

Purpose. This paper aims to provide complete identification of rock types in the Drenas region by detailed description of all types of the rocks found. The authors intended to determine interruption or continuity of all inter-formational boundaries to accurately delineate them on the ground and fully reflect on the 1: 25000 scale map, as well as to identify the nature of contact between rock types and give its detailed description. Methods. During August, September, October of 2019, the exploration field trips were carried out. Geological survey works focused on the following areas: complete identification of all rock types on the basis of studying their samples, preparation of thin sections for petrographic (only the magmatic rock), chemical and geochemical analysis. Systematic measurement of structural elements was conducted alongside with identification and description of mineral outcrops areas. Findings. Based on the study of stratigraphic units and geological description of mineral outcrop areas, we identified different types of rocks using petrography microscope preparation and chemical and geochemical analysis. The area of Drenas has the following lithostratigraphic units: gabbro diabase, harzburgite, metasandstone. Originality. The originality of the study consists in the use of optical microscope for precise identification of rocks. As a result of the research conducted in the exploration area, we have obtained a clear petrographic description of minerals composition, their texture and mineralization, which allows assessing the possibility of the area exploitation. The analyses were completed at the certified laboratory of Geology-Mining Faculty (Polytechnic University of Tirana) and Geosciences Institute. Practical implications. Petrographic study and chemical analysis led to the conclusion that the research area has Ni mineralization, which is important for the development of mining sector and the community given the environment is preserved and the adequate way of the area exploitation is applied.

Analysis of situ elemental concentration log data for lithology and mineralogy exploration— A case study

Metamorphic rocks are diverse with more compositions, structures, and textures that are complex. Rock type identification and prediction from metamorphic rocks using well log data are difficult tasks. This study shows the use of cross plot technique, Pearson correlation, and factor analysis in metamorphic rocks interpretation using borehole geochemical data from the 4390–5089 m interval depth of the Chinese Continental Scientific Drilling Main hole. Lithological identification abilities, correlation between geochemical and geophysical logs, and build a factor model which link in situ chemical element to minerals were studied. The results show that Potassium and Thorium logs are the most discriminating logs in metamorphic rocks. Pearson correlation shows that Potassium and Thorium are the largest contributors to the gamma ray responses. Factor analysis results show a 2 factor model-where factor 1 (amphibole mineral) and factor 2 (K-feldspar mineral) described 76.261% of the variation in log responses. These statistical methods can be a very helpful tool in helping the task of geoscientists in the context of research drillings.

Rock Type Identification Using Analysis of the Acoustic Signal Frequency Contents Propagated While Drilling Operation

Rock Type Identification Using Analysis of the Acoustic Signal Frequency Contents Propagated While Drilling Operation