Rock Physics Model Research Articles

Charakterystyka złoża piaskowca prekenomainskiego: Środkowy Synaj, Egipt

Fifty-one sandstone core samples obtained from wadi Saal area. They are belonging to the Pre-Cenomanian age. These samples were subjected to various laboratory measurements such as: density, porosity, permeability, electrical resistivity, grain size analysis and ultrasonic wave velocity. The parameters describing reservoir properties are outlined. Packing index, reservoir quality index, flow zone indicator and pore throat radius (R35 and R36) were calculated. The obtained interrelationships among these parameters allowing to improve petrophysical knowledge about the Pre-Cenomanian reservoir information. The obtained rock physics models could be employed with some precautions to the subsurface existences of the Pre-Cenomanian sandstone reservoirs especially in the surrounding areas.

A novel rock-physics model of organic-rich shale considering maturation influence

Geochemical parameters, e.g., maturity and total organic carbon (TOC) content, play a crucial role in the prediction of sweet spots and the exploration of oil and gas in organic-rich shales. Thermal maturity significantly affects the conversion of solid organic matter into hydrocarbons and the evolution of microstructures, thereby altering the overall elastic properties of shales. To clarify how maturity affects shale properties, we develop a novel rock-physics model (RPM) of organic-rich shale, in which we consider the continuous process of thermal maturity. First, we present how to estimate the maturity level, TOC content, and organic porosity using logging data. Second, different from only considering the discrete-stage maturity, we establish a novel RPM in which a continuous kerogen maturation process serves as a key control condition. Furthermore, we calibrate the volumetric proportion of each porosity type as a function of maturation. Finally, we apply the RPM to investigate how sweet spot parameters (thermal maturity, TOC content, and brittle mineral content), overpressure, and diagenesis affect the overall elastic properties and anisotropy of shale during thermal maturation. Results demonstrate that using our RPM, we may predict the acoustic velocity of shale formations reliably and that kerogen evolution has a noticeable impact on the elastic properties of shale rocks, particularly during the wet gas window stage of mid-to-high maturation. We conclude that thermal maturity emerges as a crucial sweet spot parameter in the case of the exploration of oil and gas in organic-rich shales.

Shear wave velocity prediction for fractured limestone reservoirs based on artificial neural network

AbstractShear wave velocity is an essential parameter in reservoir characterization and evaluation, fluid identification and prestack inversion. However, conventional data‐driven or model‐driven shear wave velocity prediction methods exhibit several limitations, such as lack of training data sets, poor model generalization and weak model robustness. In this study, a model‐ and data‐driven approach is presented to facilitate the solution of these problems. We develop a theoretical rock physics model for fractured limestone reservoirs and then use the model to generate synthetic data that incorporates geological and geophysical knowledge. The synthetic data with random noise is utilized as the training data set for the artificial neural network, and a well‐trained shear wave velocity prediction model, random noise shear wave velocity prediction neural network, is established by parameter tuning, which fits the synthetic data with noise well. The neural network is applied directly to the real field area. Compared with conventional shear wave prediction methods, such as empirical formulas and the improved Xu–White model, the prediction results show that the random noise shear wave velocity prediction neural network has better prediction performance and generalization. Furthermore, the prediction results demonstrate the efficacy of the proposed approach, and the approach has the potential to perform shear wave velocity prediction in real areas where training data sets are unavailable.

CO2 rock physics modeling for reliable monitoring of geologic carbon storage

Monitoring, verification, and accounting (MVA) are crucial to ensure safe and long-term geologic carbon storage. Seismic monitoring is a key MVA technique that utilizes seismic data to infer elastic properties of CO2-saturated rocks. Reliable accounting of CO2 in subsurface storage reservoirs and potential leakage zones requires an accurate rock physics model. However, the widely used CO2 rock physics model based on the conventional Biot-Gassmann equation can substantially underestimate the influence of CO2 saturation on seismic waves, leading to inaccurate accounting. We develop an accurate CO2 rock physics model by accounting for both effects of the stress dependence of seismic velocities in porous rocks and CO2 weakening on the rock framework. We validate our CO2 rock physics model using the Kimberlina-1.2 model (a previously proposed geologic carbon storage site in California) and create time-lapse elastic property models with our new rock physics method. We compare the results with those obtained using the conventional Biot-Gassmann equation. Our innovative approach produces larger changes in elastic properties than the Biot-Gassmann results. Using our CO2 rock physics model can replicate shear-wave speed reductions observed in the laboratory. Our rock physics model enhances the accuracy of time-lapse elastic-wave modeling and enables reliable CO2 accounting using seismic monitoring.

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

The absence of shear-wave (S-wave) velocity in well log data limits the effective seismic characterization of lithology, petrophysical properties, and fluid distribution of tight reservoirs in the Chang 7 shale oil formations of Ordos Basin, west China. However, conventional rock physics models (RPMs), e.g., the Xu-White model, struggle to accurately obtain the key parameters such as pore aspect ratio in the process of S-wave velocity prediction. This work performs a comparative study of S-wave velocity prediction based on deep neural network (DNN) and RPM for such reservoirs. By employing the poroelasticity equations which are capable of describing elastic wave propagation in the complex reservoirs saturated with viscous fluid, the relation between elastic and petrophysical properties is established with a DNN-based method. This approach quantifies the connection between P-/S-wave velocities and rock properties. On the other hand, we reformulate the traditional Xu-White model by incorporating the decoupled Kuster-Toksöz (K-T) model and the poroelasticity equations with viscoelastic fluid in the modeling process. The variation of pore aspect ratio with depth is also considered to characterize the complex pore structures, which is appropriate for improving the accuracy of prediction. Log data from the three wells are considered for verification, of which results show that the S-wave velocity prediction methods help improve the final result. Aided by the DNN-based method, the prediction exhibits a better performance if the training data is sufficient. In particular, provided a small amount of measured S-wave data (even ten data points), the trained DNN model can also provide reasonable predictions.

Prism-shaped rock-physics template

Despite the emergence of statistical and intelligent approaches for quantitative seismic interpretation in recent years, rock-physics templates (RPTs) are still preferred because of their simplicity and easy implementation. RPTs have been introduced as a fundamental tool for lithology and fluid discrimination based on well-log and seismic data, which have already been proven in terms of accuracy and reliability. Considering the demand for comprehensive RPTs and improving their efficiency, I develop a novel 3D prism-shaped template for lithology and fluid discrimination called a prism-shaped RPT (P-RPT). For this, a theoretical methodology for designing conventional RPT is introduced with the aim of bounds and hybrid rock-physics models. Next, some novel triangular and rectangular ternary and binary templates are designed and connected to one another to build a prism for fluid discrimination and lithology identification. Two ternary charts are used for fluid and lithology, incorporating the P- and S-wave velocity ratios and lambda-mu parameters. Furthermore, binary charts, including acoustic impedance, Lamé parameters, and porosity, are designed and modified from the literature for fluid and lithology identification. Next, the obtained templates are successfully validated on blind data sets (ultrasonic, well logging, and seismic data) in different reservoirs with various lithologies and fluid types. The results indicate that the P-RPT could integrate the available RPTs into a 3D diagram and give a reliable framework for applications in seismic interpretation. User friendliness and generalizability are the most prominent advantages of this template for detecting fluid type and lithology based on well logs and seismic inversion, the results of which can be interpreted on a single chart rather than analyzing the data with different templates. The methodology and framework for implementing and generating templates are thoroughly explained in theory and practice to localize P-RPT or update the new RPT for another region.

Gas prediction in tight sandstones based on the rock-physics-derived seismic amplitude variation versus offset method

Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs. However, the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic methods. Through rock physical modeling and reservoir parameter analyses conducted in this study, a more suitable indicator for estimating gas enrichment, termed the gas content indicator, has been proposed. This indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight sandstones. Moreover, a new seismic amplitude variation versus offset (AVO) equation is derived to directly extract reservoir properties, such as the gas content indicator and porosity, from prestack seismic data. The accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz equation. To ensure reliable estimations of reservoir properties from partial angle-stacked seismic data, the proposed AVO equation is reformulated within the elastic impedance inversion framework. The estimated gas content indicator and porosity exhibit favorable agreement with logging data, suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas enrichments. Furthermore, the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.

Automated lithofluid and facies classification in well logs: The rock-physics perspective

Although an accurate lithofluid and facies interpretation from wireline log data is critical for applications such as joint facies and impedance inversion of seismic data, extracting this information manually is challenging due to the logs’ complexity and the need to combine information from different logs. Traditional clustering methods also struggle with lithofluid type inference due to differing depth trends in petrophysical rock properties stemming from compaction and diagenesis. We introduce a rock-physics machine-learning workflow that automates lithofluid classification and property depth trend modeling to address these challenges. This workflow uses a maximum-likelihood approach, explicitly accounting for depth-related effects via rock-physics models (RPMs) to infer lithofluid types from borehole data. It uses a robust expectation-maximization algorithm to associate each lithofluid type with a specific RPM instance constrained within physically reasonable bounds. The workflow directly outputs lithofluid type proportions and type-specific RPMs with associated uncertainties, providing essential prior information for seismic inversion.

Shear-wave attenuation anisotropy: a new constraint on mantle melt near the Main Ethiopian Rift

The behaviour of fluids in preferentially aligned fractures plays an important role in a range of dynamic processes within the Earth. In the near-surface, understanding systems of fluid-filled fractures is crucial for applications such as geothermal energy production, monitoring CO2 storage sites, and exploration for metalliferous sub-volcanic brines. Mantle melting is a key geodynamic process, exerting control over its composition and dynamic processes. Upper mantle melting weakens the lithosphere, facilitating rifting and other surface expressions of tectonic processes.Aligned fluid-filled fractures are an efficient mechanism for seismic velocity anisotropy, requiring very low volume fractions, but such rock physics models also predict significant shear-wave attenuation anisotropy. In comparison, the attenuation anisotropy expected for crystal preferred orietation mechanisms is negligible or would only operate outside of the seismic frequency band.Here we demonstrate a new method for measuring shear-wave attenuation anisotropy, apply it to synthetic examples, and make the first measurements of SKS attenuation anisotropy using data recorded at the station FURI, in Ethiopia. At FURI we measure attenuation anisotropy where the fast shear-wave has been more attenuated than the slow shear-wave. This can be explained by the presence of aligned fluids, most probably melts, in the upper mantle using a poroelastic squirt flow model. Modelling of this result suggests that a 1% melt fraction, hosted in aligned fractures dipping ca. 40° that strike perpendicular to the Main Ethiopian Rift, is required to explain the observed attenuation anisotropy. This agrees with previous SKS shear-wave splitting analysis which suggested a 1% melt fraction beneath FURI. The interpreted fracture strike and dip, however, disagrees with previous work in the region which interprets sub-vertical melt inclusions aligned parallel to the Main Ethiopian Rift which only produce attenuation anisotropy where the slow shear-wave is more attenuated. These results show that attenuation anisotropy could be a useful tool for detecting mantle melt, and may offer strong constraints on the extent and orientation of melt inclusions which cannot be achieved from seismic velocity anisotropy alone.

A new rock physics model for fractured oil shale reservoirs in Mahu oil field

The exploration of unconventional reservoirs such as oil shale has become a focus of research in the oil/gas industry, but due to the diversity of lithology and structural complexity of shale reservoirs, the study of their rock physics laws is a challenge. By analyzing the physical, lithologic, and fluid characteristics of oil shale reservoirs in the Mahu area of Xinjiang, China, we adopted a variety of effective-medium theories to carry out rock physics modeling. We analyzed the differences in the calculation results of various theoretical models, and finally constructed a set of rock physics modeling processes suitable for oil shale reservoirs. The analysis shows that the calculation results of Voigt-Reuss-Hill (VRH) and Hashin-Shtrikman (HS) average for mixed mineral matrix are very similar, but there are certain differences between the upper and lower bounds calculated by them. The upper and lower bounds of HS average are closer than VRH average. We used the Kuster-Toksoz effective-medium, differential effective-medium (DEM), and self-consistent approximation models to construct the rock skeleton and analyzed the differences of the models. The results of the three models are almost identical, but their assumptions and limitations differ. We used DEM in the final shale model, considering the large porosity and the order of inclusion filling. According to the fracture distribution characteristics of oil shale reservoirs, we used an inclined fracture model to describe the fractures with different structural characteristics. Finally, the established shale rock physics model was used to calculate the actual logging data, and the results of elastic parameters are consistent with the measured data. Through the inversion of the fracture parameters, the inversion results are consistent with the measured results as a whole, indicating that the model has certain applicability to the simulation of oil shale reservoirs.

Velocity Measurements of Powdered Rock at Low Confining Pressures and Comparison to Lunar Shallow Seismic Velocity

AbstractSeismic methods will be useful for future lunar near‐surface characterization, and high‐fidelity elastic models will be required to aid interpretation of seismic observations. To develop an elastic lunar near‐surface model, we performed ultrasonic velocity measurements of lunar regolith simulant at low confining pressure and developed a rock physics model calibrated to these measurements. Grain contact models based on Hertz‐Mindlin theory produce accurate results at high confining pressure (i.e., several hundred meters or more burial depth) but historically fail to predict observed velocities in unconsolidated media at low pressure. Therefore, we heuristically modified existing models to fit our measured data over a range of porosities and confining pressures. To compare with Apollo 14 and 16 active seismic experiments, we used our new heuristic rock physics model to produce lunar subsurface velocity profiles. We performed ray tracing through our velocity profiles to calculate seismic traveltime, which results in good agreement with first arrivals interpreted from the Apollo experiments. Our model suggests a slightly higher velocity‐pressure dependence than inferred from in situ measurements, which may be due to porosity reduction in the lunar regolith from impact‐induced and natural vibrations.

Rock physics-based anisotropy parameters estimation of Marcellus Shale using log data acquired in a vertical well

The elastic properties of shale rock exhibit anisotropy due to the presence of highly anisotropic clay minerals and their alignment. This anisotropy has a significant impact on geophysical and geomechanical responses. However, the elastic anisotropy of shale is often overlooked due to the difficulty in measuring sufficient parameters in the field, resulting in a gap between theoretical understanding and practical implementation. To address this issue, a rock physics-based approach was tested on log data obtained from a vertical well in the Marcellus Shale to estimate anisotropy parameters. The approach utilized a rock physics model based on the Sayers-den Boer method with model parameters optimized using measured stiffness parameters C33, C55, and C66. Anisotropy parameters δ and ε were then calculated using these optimized model parameters. The optimized model parameters and estimated anisotropy parameters are consistent with existing studies, indicating the correctness of the model. Additionally, the rock physics model was used to investigate the impact of gas saturation on the vertical VP/VS ratio and ε/γ ratio. It was found that gas saturation has a significant impact on these ratios. This carries important implications for acoustic log data interpretation and seismic characterization of shales.

Study of rock fracture patterns for obtaining the basis for energy-efficient ore ball milling

The study is conducted using methods of analysis (in proving the possibility of assessing the disintegration of rocks by energy consumption), methods of free vibration theory (to determine the motion of a falling ball over an elastic connection), methods of energy balance, methods of theoretical mechanics and material resistance (to study amplitude characteristics of mechanical systems), methods of rock fracture theory (to determine fracture work), physical modeling (to design test benches) and experimental methods (to find relationships between parameters). The strength of bulk crushed material in the first stages of ore preparation is practically independent of its coarseness and solid form, which allows the estimated volume of disintegrated ore (particle concentration) to be determined by the energy expended, translated into other physical quantities. Results of experimental studies support the theoretical dependences. The proposed approach of automatic stabilisation of solid concentration in the ball mill pulp at the optimum level allows to increase the productivity of ore preparation on the finished grade in the first stages by 10% at reduction of specific power consumption for ore grinding.

An anisotropic rock physics modeling for the coalbed methane reservoirs and its applications in anisotropy parameter prediction

The elastic properties and anisotropy of coalbed methane reservoirs are still poorly understood due to the complicated pore structure and the existing state of the coalbed methane. Therefore, an anisotropic rock physics model for the coalbed methane reservoirs is proposed based on the related effective medium theories, which focuses on the equivalent calculation of the elastic properties for adsorbed coalbed methane and the characterization of the multiple pore structure. Many factors related to the elastic properties of coal are considered, including the composition, pore structure, adsorbed gas content and pore fluid. The measured ultrasonic and well-logging data demonstrate that the proposed physics model is effective in predicting the elastic constants and velocity anisotropy parameters. The results suggest that the elastic constants increase with the increase of adsorbed gas content to some extent, and the effect of porosity on the elastic constants is much more obvious than that of adsorbed gas content. The velocity anisotropy parameters increase with the aligned fracture porosity and the fracture aspect ratio, and the P-wave anisotropy is more sensitive to these two factors. With increasing organic matter and clay content and porosity, Young's modulus and brittleness index decrease while the Poisson's ratio increases, and the impact of the porosity on the brittleness index is more significant than that of organic matter and clay content. The results are helpful in establishing the relationship between reservoir physics parameters and seismic response and can provide a basis for coalbed methane reservoir prediction and hydraulic fracturing.

Experimental seismic crosshole setup to investigate the application of rock-physical models at the field scale

Seismic crosshole techniques are powerful tools to characterize the properties of near-surface aquifers. Knowledge of rock-physical relations at the field scale is essential for interpreting geophysical measurements. However, it remains difficult to extend the results of existing laboratory studies to the field scale due to the usage of different frequency ranges. To address this, we develop an experimental layout that successfully determines the dependency of gas saturation on seismic properties. Integrating geophysical measurements into a hydrogeologic research question allows us to prove the applicability of theoretical rock-physical concepts at the field scale, filling a gap in the discipline of hydrogeophysics. We use crosshole seismics to perform a time-lapse study on a gas injection experiment at the TestUM test site. With a controlled two-day gaseous [Formula: see text] injection at a depth of 17.5 m, we monitor the alteration of water saturation in the sediments over a period of 12 months, encompassing an observational depth of 8–13 m. The investigation contains an initial P-wave simulation followed by a data-based P-wave velocity analysis. Subsequently, we discuss different approaches to quantifying gas content changes by comparing Gassmann’s equation and the time-average relation. With the idea of patchy saturation, we discover that analyzing P-wave velocities in the subsurface is a suitable method for our experiment, resulting in a measurement accuracy of 0.2 vol%. We determine that our seismic crosshole setup is able to describe the relation of the rock’s elastic parameter on modified fluid properties at the field scale. With this method, we are able to quantify the relative water content changes in the subsurface.

Basin‐scale prediction of S‐wave Sonic Logs using Machine Learning techniques from conventional logs

AbstractS‐wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short‐term memory algorithm for estimating S‐wave sonic logs from conventional logs, including P‐wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short‐term memory model with self‐attention yields the highest performance, achieving an R‐squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S‐wave velocity values enable the creation of a basin‐scale S‐wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short‐term memory model excels not only in predicting S‐wave velocity but also in estimating S‐wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. In conclusion, these S‐wave velocity estimates facilitate the prediction of further elastic properties, aiding in the comprehension of petrophysical and geomechanical property variations within the basin and enhancing earthquake hypocentral depth estimation.

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.

Sub-Bottom Sediment Classification Employing a Multi-Attribute Temporal Convolutional Network

Sub-bottom profile data have the potential to characterize sediment properties but are seldom used for offshore site investigations because of uncertainties in rock-physics models. Deep-learning techniques appear to be poised to play very important roles in our processing flows for the interpretation of geophysical data. In this paper, a novel deep learning-based method for this task is proposed in which a nonlinear mapping between the observed data and sediment types is learned using a multi-attribute temporal convolution network (MATCN). Firstly, empirical mode decomposition (EMD) is employed for the original data, and intrinsic mode functions (IMFs) with multiple time scales are generated. Based on different IMFs, instantaneous frequency (IF) data under different IMFs can be obtained, while instantaneous phase (IP) and instantaneous amplitude (IA) data are obtained based on the original data. IF, IA and IP data are called attribute data, and are highly related to the attenuation, reflection, and interior structure of the sediment. Thus, IA, IF, and IP are used as the inputs, and a 1D convolutional neural network (CNN) and a time convolution network (TCN) are used to extract sequential features. Different feature representations are then fused. Combining cross-entropy loss function and class-edge loss function, the network is encouraged to produce classified results with more continuous sediment distributions compared with the traditional loss function. The real-data experiments demonstrate that the proposed MATCN has achieved good performance with an F measure greater than 70% in all cases, and greater than 80% in most cases.

Correlation between induced polarization and sulfide content of rock samples obtained from seafloor hydrothermal mounds in the Okinawa Trough, Japan

AbstractThe physical properties of seafloor massive sulfides are crucial for interpreting sub-seafloor images from geophysical surveys, shedding light on the evolution of seafloor mineral deposits. While some studies have explored the relationship between electrical properties and the volume of conductive minerals in rocks from seafloor massive sulfide deposits, they primarily focused on artificial samples, leaving the characteristics of natural samples less understood. Moreover, there has been no comprehensive study detailing the general characteristics of electrical properties, particularly chargeability and relaxation time, in relation to the volumetric fraction of sulfides in rocks from massive sulfide mounds in typical hydrothermal areas. In this study, we employed complex conductivity measurements, elemental concentration analysis, and mineral content identification on to rock samples from the active hydrothermal zones of the Okinawa Trough in Japan. The complex conductivity observed was remarkably high, with a pronounced imaginary component and a broad frequency range. This is attributed to induced polarization extending beyond our measurement range. The rock samples were rich in conductive sulfide minerals such as pyrite, chalcopyrite, and galena. Using the Cole–Cole rock physics model, we established a correlation between rock chargeability and relaxation time coefficient with the volume fraction of conductive sulfide minerals, which deviated from previous findings. The intensity of induced polarization was notably higher than anticipated in earlier studies using artificial samples. Furthermore, we observed a distinct positive correlation between the coefficient of relaxation time and the increase in sulfide volume, likely due to the geometric characteristics of the sulfide minerals. Our findings suggest that rocks in massive sulfide mounds may generally construct sulfide clusters that lengthen the conductive path of the electrical carrier. Graphical Abstract