Geophysical Models Research Articles

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

AbstractTwo major barriers hinder the holistic understanding of subsurface critical zone (CZ) evolution and its impacts: (a) an inability to measure, define, and share information and (b) a societal structure that inhibits inclusivity and creativity. In contrast to the aboveground portion of the CZ, which is visible and measurable, the bottom boundary is difficult to access and quantify. In the context of these barriers, we aim to expand the spatial reach of the CZ by highlighting existing and effective tools for research as well as the “human reach” of CZ science by expanding who performs such science and who it benefits. We do so by exploring the diversity of vocabularies and techniques used in relevant disciplines, defining terminology, and prioritizing research questions that can be addressed. Specifically, we explore geochemical, geomorphological, geophysical, and ecological measurements and modeling tools to estimate CZ base and thickness. We also outline the importance of and approaches to developing a diverse CZ workforce that looks like and harnesses the creativity of the society it serves, addressing historical legacies of exclusion. Looking forward, we suggest that to grow CZ science, we must broaden the physical spaces studied and their relationships with inhabitants, measure the “deep” CZ and make data accessible, and address the bottlenecks of scaling and data‐model integration. What is needed—and what we have tried to outline—are common and fundamental structures that can be applied anywhere and used by the diversity of researchers involved in investigating and recording CZ processes from a myriad of perspectives.

Seismically Informed Reference Models Enhance AI‐Based Earthquake Prediction Systems

AbstractGiven the robust nonlinear regression capabilities of Artificial Intelligence (AI) technology, its commendable performance in numerous geophysical tasks is expected. Yet, AI technology suffers from (a) its “black box” nature and (b) the fact that some complicated artificial neural networks (ANNs) claiming superior performance do not surpass some simple geophysical models that clearly describe the underlying physical processes. Numerous reports rely on standard machine learning metrics, often using a spatially uniform Poisson (SUP) distribution as their reference. A good performance just means that the artificial neural network (ANN) outperforms this basic reference, potentially offering little novelty to the scientific community. Worse, this can lead to spurious inference. We demonstrate this by using the monthly average human‐made Nighttime Light Map and the cumulative energy of earthquakes in various space‐time units as inputs for an Long short‐term memory model. The goal is to predict earthquakes with a magnitude of M ≥ 5.0 across the entire Chinese Mainland. With the SUP reference model, the ANN concludes that human‐made Nighttime Light possesses substantial earthquake prediction capability. This is evidently flawed reasoning. We show that this stems from the poor reference model and this spurious inference disappears when using a better benchmark consisting of a spatially varying Poisson (SVP) model informed from statistical seismology. This is implemented by weighting the punishments/rewards of our ANN associated with failed/successful predictions by prior probabilities provided by the stronger SVP model. Scores obtained with the time‐space Molchan diagram demonstrate the strong performance improvement obtained by training ANN with a better reference model.

Electrical Resistivity Inversion Based on Symmetric Polynomial Constraint

As we seek to make geophysical models more consistent with geological models, more and more a priori information is incorporated into geophysical inverse problems. How we make the most use of a priori information is still a key issue in geophysical inversion, particularly for effectively representing such information in inversion problems. To address this problem, we propose a symmetric polynomial constrained inversion algorithm that can represent multiple known physical properties for the lithology. The electrical resistivity synthetic model tests show that the inversion results constrained with symmetric polynomials can make the model resistivity be in line with a priori information and obtain more realistic sharp boundaries. Finally, we give a practical verification of our inversion algorithm with field data.

Numerical Simulation of Geophysical Models to Detect Mining Tailings’ Leachates within Tailing Storage Facilities

The effective detection and monitoring of mining tailings’ leachates (MTLs) plays a pivotal role in environmental protection and remediation efforts. Electrical resistivity tomography (ERT) is a non-invasive technique widely employed for mapping subsurface contaminant plumes. However, the efficacy of ERT depends on selecting the optimal electrode array for each specific case. This study addresses this challenge by conducting a comprehensive review of published case studies utilizing ERT to characterize mining tailings. Through numerical simulations, we compare the imaging capabilities of commonly used electrode configurations, six ERT arrays, aiming to identify the optimal array for MTLs’ detection and monitoring. In addition, field surveys employing ERT were conducted at the El Mochito mine tailings site to detect zones saturated with leachates within the tailing storage facilities (TSFs). The findings indicate that the “Wenner-Schlumberger” array exhibits superior data resolution for MTL detection. However, the choice of the optimal electrode array is contingent on factors such as survey location, geological considerations, research objectives, data processing time and cost, and logistical constraints. This study serves as a practical guide for selecting the most effective electrode array in the context of pollutant penetration from mining tailings, employing the ERT technique. Furthermore, it contributes valuable insights into characterizing zones saturated with mining tailing leachates within the TSFs, providing a solid foundation for informed environmental management and remediation strategies.

Multiscale modeling of reservoir systems using geostatistical methods

Identifying factors and quantifying the processes, influencing the extent of reservoir rocks and the distribution of their heterogeneities are fundamental prerequisites to improve the characterization of reservoir connectivity and its dynamic functioning. In this paper, we propose a multiscale approach based on combined geological–geophysical investigations, geostatistics, and 3D geological modeling to build a structural model of faulted-reservoir systems, at regional and local scales within the Jeffara basin, southeast Tunisia.

Impact of the combination and replacement of SLR-based low-degree gravity field coefficients in GRACE solutions

GRACE and GRACE Follow-On (FO) missions provide time-variable gravity field models of unprecedented quality that allow for the hydrological, oceanic, and ice mass change studies on a global scale. However, the very low-degree coefficients derived from GRACE and GRACE-FO are of inferior quality due to thermal effects acting on satellites and malfunctioning of the onboard accelerometers. Therefore, C20 and C30 coefficients describing the Earth’s oblateness and the pear shape of the Earth, respectively, are being replaced by values derived from satellite laser ranging (SLR) in the standard GRACE solutions. This study assesses the impact of the replacement of low-degree gravity field coefficients in GRACE/GRACE-FO solutions by SLR data on the trend and seasonal signals of ice mass changes in Greenland and Antarctica. We found that the replacement of the low-degree gravity field coefficients changes the estimates of trends by 4, 8, and 22 Gt/year in Greenland, West, and East Antarctica, respectively, depending on the source of SLR coefficients and period for which the coefficients are replaced. In SLR and GRACE solutions, all coefficients of the same order and the same parity of degrees are strongly correlated. Therefore, replacing only two selected coefficients may lead to a biased solution. Thus, we propose to combine GRACE with SLR solutions up to a degree and order 10 × 10 to properly consider the sensitivity of each of the techniques to gravity field coefficients, instead of replacing two coefficients from SLR in GRACE solutions. The combined solution reduces the residual trend of post-glacial rebound from 1.2 to 0.9 Gt/year and from − 57.8 to − 57.0 Gt/year in Scandinavia and South Canada, respectively, when compared to GRACE/GRACE-FO solutions with the replacement of coefficients. The SLR-GRACE combination reduces the noise in the GRACE/GRACE-FO solutions by 8%, from 38 to 35 Gt, in the Fennoscandia region. In the periods when GRACE is at the end of its mission and observations are disrupted, the weights adjust the contribution from SLR and GRACE based on relative ratio of variances from each techniques. Thus, the combined solutions are more consistent with independent geophysical models of glacial isostatic adjustment, and the combinations are affected by smaller noise than the standard GRACE solutions and properly account for different sensitivities of SLR and GRACE techniques to low-degree time-variable gravity field coefficients.

Integration of automatic implicit geological modelling in deterministic geophysical inversion

Abstract. We propose and evaluate methods for the integration of automatic implicit geological modelling into the geophysical (potential field) inversion process. The objective is to enforce structural geological realism and to consider geological observations in a level set inversion, which inverts for the location of the boundaries between rock units. We propose two approaches. In the first approach, a geological correction term is applied at each iteration of the inversion to reduce geological inconsistencies. This is achieved by integrating an automatic implicit geological modelling scheme within the geophysical inversion process. In the second approach, we use automatic geological modelling to derive a dynamic prior model term at each iteration of the inversion to limit departures from geologically feasible outcomes. We introduce the main theoretical aspects of the inversion algorithm and perform the proof of concept using two synthetic studies. The analysis of the results using indicators measuring geophysical, petrophysical, and structural geological misfits demonstrates that our approach effectively steers the inversion towards geologically consistent models and reduces the risk of geologically unrealistic outcomes. Results suggest that the geological correction may be effectively applied to pre-existing geophysical models to increase their geological realism and that it can also be used to explore geophysically equivalent models.

DInSAR analysis and geophysical modeling of 2022 November 23 Gölyaka-Düzce Earthquake

DInSAR analysis and geophysical modeling of 2022 November 23 Gölyaka-Düzce Earthquake

Trakya Havzasının Güneybatısında Fay Parametrelerinin Gravite Gradyent Verilerinden Kestirimi

Characterization of fault zones plays an important role for earthquake studies, geothermal and mineral resource exploration. This problem can be achieved by optimizing parameters of a defined synthetic model iteratively using gravity, gravity gradients or magnetic data. Curvature gradients indicate deviations of an equipotential surface from a spherical surface, therefore, reflecting deviations of mass distributions from a source point. In this study, differential curvature gradient is used as it identifies linear structures such as dip-slip faults and geological contacts. Curvature gradients are computed from gravity gradient observations which are derived from the high-resolution Earth Gravitational Model 2008. Estimating the fault parameters from gravity gradients is a complex nonlinear geophysical inverse problem that requires finding the minimum of a multi-variable cost function. A global optimization technique called simulated annealing (SA) is used to estimate location, dip angle and some depth parameters of the faults located in southwest side of the Thrace basin in Türkiye. In the optimization technique, a straight solution is inevitably applied to calculate the theoretical anomaly to be compared with the observed anomaly in each search step. In this case, the problem requires constructing a mathematical or geophysical interpretation model. Such a model is designed as a dip-slip fault model. The results show that dip angle estimates indicate high-angle faults which are found to be consistent with the seismic studies.

Constraining Earth's mantle rheology with Love and Shida numbers at the [formula omitted] tidal frequency

We use measurements of Earth's tidal response at the M2 frequency to constrain the rheology of its mantle. The viscoelasticity and anelasticity of the planet are modeled with an Andrade rheology that depends on two parameters: α and ζ. In this paper, we propose an improved algorithm to compute Earth's tidal deformation. Its Love and Shida numbers k2, h2, k3 and l2 as well as the tidal lag ϵ were calculated for two viscosity profiles and for a wide range of values of α and ζ. By comparing our results with geodetic measurements we obtain the range of values of α and ζ that successfully describes Earth's viscoelastic behavior. Values of ζ as high as 105 can not be excluded. For ζ=1, α should be in the range from 0.19 to 0.33, while for a ζ=105, α is most likely between 0.11 and 0.17. We believe that a similar rheology should be used in geophysical models of other rocky planets and satellites. The obtained results are mostly representative of the lower mantle.We have also shown that for several combinations of the two parameters α and ζ we could obtain nearly identical values of Earth's ℜk2, ℜh2, ℜl2 and ℜk3 with considerably different values of the associated tidal lag. This shows that the approach of always setting ζ=1 might be too simplistic and an Andrade rheology with two free parameters is needed to constrain both the real and imaginary parts of Love and Shida numbers.

Digital twin of earth: A novel information framework for managing a sustainable earth

<p>As the progress of human society rapidly enters a new stage, sustainable development has become an essential target. It is necessary to have an effective approach to managing the resources and ecosystems of Earth, with the target of promoting sustainable development while meeting the needs and requirements of both present and future generations. Digital twin technology provides a fresh perspective on sustainable management with a better understanding of the interconnectedness of Earth's systems and accurate simulation and prediction of future changes. By integrating various types of data and models, including climate models, geophysical models, and social media data, a digital twin of Earth can simulate and analyze various aspects of the planet. The digital twin of Earth leverages real-time multi-source data integration through advanced IoT and wireless sensor networks, feeding into a data lake architecture that supports the seamless integration and processing of heterogeneous data. The integrated data then informs dynamic models, combining numerical simulation models with intelligent algorithms for enhanced prediction and analysis. This approach emphasizes the integration of environmental, social, and economic considerations into decision-making that balances economic growth, social well-being, and environmental protection to achieve sustainable development. This paper introduces the definition and information framework of the new concept of a digital twin of Earth to promote the current research on Earth management based on digital transformation techniques.</p>

Diagnostic of the Lévy area for geophysical flow models in view of defining high order stochastic discrete-time schemes

Diagnostic of the Lévy area for geophysical flow models in view of defining high order stochastic discrete-time schemes

ResNet Deep Learning‐Based Inversion Method for Sea Surface Wind Field

This paper presents a method to invert the sea surface wind field from Sentinel‐1 synthetic aperture radar (SAR) interferometric wide swath (IW) band mode ground range detected (GRD) vertical polarization (VV) data based on the deep residual network (ResNet). Compared with the traditional algorithm (i.e., using geophysical model functions (GMFs)), the wind field inversion based on the ResNet deep learning method solves the problem of separate wind direction and wind speed inversion. The ResNet model is developed based on the results of 212 VV polarized IW images and cross calibrated multiplatform (CCMP) data collected in 2021 in the offshore waters of China. The root mean square error, standard deviation, and bias of the inverse wind direction of the ResNet model are 12.5°, 9.8°, and 7.4°, respectively, and the root mean square error, standard deviation, and bias of the inverse wind speed are 2.33, 1.495, and −1.375. The ResNet is better than the conventional local gradient and C‐band model models, further confirming the advantages shown by the deep learning algorithm in nonlinear fitting and its potential application in sea surface wind field inversion.

ГЛУБИННАЯ МОДЕЛЬ ЛИТОСФЕРЫ ВДОЛЬ ГЕОТРАНСЕКТА Г. СПАССК-ДАЛЬНИЙ – БУХ. ЗЕРКАЛЬНАЯ

The possibility is shown of joint interpretation of seismic, density, magnetic and geoelectric models of the lithosphere deep structure along the Spassk-Dalniy – Zerkalnaya Bay geotraverse. Seismic and density boundaries form the main structural framework of the integrated model of the lithosphere. Magnetic heterogeneities map the elements associated with magmatism. The electrical model reflects the most recent processes of matter redistribution. The resulting integrated geophysical model demonstrated that for the terranes identified on the surface and most of the faults separating them the structural style is well expressed only within the upper and middle parts of the earth’s crust to a depth of 10-20 km. The lithosphere of the Khanka superterrane retains its structure both in the earth’s crust and in the mantle. Structures of the crust and upper mantle of the Sikhote-Alin orogenic belt are different and associated with each other through highly conductive areas and zones.

Numerical analysis of the geophysical parameter sensitivity on the multimode Love wave dispersion curves

The characteristics of multimode Love wave dispersion curves are dependent on various geophysical parameters, namely shear wave velocity (SWV), layer thickness and density. This paper presents a novel numerical analysis of these parameters to determine which ones have a significant effect on the dispersion curves of multimode Love waves. Identifying parameter sensitivity is a crucial step in the inversion procedure of geophysical modeling. Our numerical analysis focuses on perturbing three geophysical parameters that influence the dispersion curves. The results confirm that SWV parameter is the most sensitive. By employing the same perturbation procedure, our numerical analysis reveals that the SWV parameter has a highly significant impact on the multimode Love wave dispersion curves. The average relative error (RE) values are found to be 27.33% for alpha = −0.2 and 25.51% for alpha = +0.2. Conversely, perturbing the layer thickness parameter demonstrates no significant influence on the dispersion curves, resulting in average RE values of 5.69% for alpha = −0.2 and 7.96% for alpha = +0.2. Furthermore, the perturbation of the density parameter exhibits an extremely negligible impact on the multimode Love wave dispersion curves, with average RE values on the order of 10−14%.

The crustal structure of the Longmenshan fault zone and its implications for seismogenesis: new insight from aeromagnetic and gravity data

Abstract. Although many geophysical models have been proposed in the Longmenshan fault zone (LFZ) and its surrounding areas, the deep structure of the seismic gap and its constraint of the Wenchuan and Lushan earthquakes remain uncertain. Based on the compiled aeromagnetic data and Bouguer gravity data, we have tried to create a more detailed and reasonable magnetic and density model using 2D forward modeling and 3D inversion and made the deep structure of the LFZ visible. The research shows that structure is heterogenous across the LFZ. The earthquake epicenters are located in regions with high-magnetic anomalies and gravity gradients that are associated with rigid blocks that were likely to accumulate stress. However, the seismic gap shows low-magnetic anomalies and transition of gravity anomalies related to a weak zone. The Sichuan Basin has two NE-trending banded high-magnetic blocks extending beneath the LFZ that firmly support the idea that the crust of the Sichuan Basin has subducted downward the LFZ. More importantly, the basement subducts to approximately 33 km west of the Wenchuan–Maoxian fault, with a low dip angle beneath the middle segment of the LFZ, whereas the distance decreases to approximately 17 and 19 km under the southern segment. Thus, the crust of the Sichuan Basin beneath the middle segment extends farther than that beneath the southern segment, with the seismic gap as the transition zone. Therefore, we propose that the structural heterogeneity of the basement on the western margin of the Sichuan Basin may be the main reason for the different focal mechanisms and geodynamics of the Wenchuan and Lushan earthquakes.

3D Geophysical Modeling Based on Multi-Scale Edge Detection, Magnetic Susceptibility Inversion, and Magnetization Vector Inversion in Panjshir, Afghanistan to Detect Probabilistic Fe-Polymetallic Bearing Zone

The Panjshir Fe-Polymetallic ore deposit is a valuable geological resource in Afghanistan, rich in iron and multiple essential metallic minerals, with substantial potential for industrial development. The exploration phase faces challenges related to the complex geological settings, high variability of mineral compositions, and the need for advanced geophysical techniques to accurately locate and assess valuable metallic resources. Considering the strong magnetic characteristics exhibited by Fe-Polymetallic elements, geomagnetic data were employed to analyze and map the likely prospectivity of Fe-Polymetallic deposits within the study area. Multi-scale edge detection techniques were employed to accurately map the boundaries of magnetic bodies by utilizing the upward continued analytical signal amplitude. The presence of a fault system on the geological map confirmed the structural information derived from our edge detection techniques. Advanced magnetic data inversion techniques were employed to create a three-dimensional representation of the distribution of magnetic bodies linked to Fe-Polymetallic deposits. In our efforts to reduce the impact of remnant magnetization in the study area, we adopted a comprehensive strategy by employing both magnetic susceptibility and magnetization vector inversion techniques. The use of a sparse and blocky norm regularization [0,1,1,1] is well-suited for magnetic susceptibility inversion, while a blocky norm [0000,0000,0000] is the appropriate choice for magnetization vector inversion in our study. Ultimately, the zones characterized by a high magnetic susceptibility and a high magnetization amplitude are considered promising areas for potential Fe-polymetallic occurrences.

Forecast of oil and gas reservoirs of the Sea of Okhotsk based on interpretation processing of seismic material

Background. This article analyses the results of geological and geophysical studies conducted in the Okhotsk Sea area with the purpose of developing methods for processing seismic information in combination with other data to solve the problems of forecasting hydrocarbon deposits.Aim. Solving problems of forecasting hydrocarbon deposits through the development of a structural and technological scheme for prospecting and evaluation of a geological and geophysical model to identify images of oil and gas deposits.Materials and methods. The research methodology included selection of the location of points for simulating “vertical wells” in order to analyze the geodynamics of the medium; multiplex parametric transformations of wave fields taking into account diagnostic significant attributes and building a multiparametric cluster; selection of oil and gas deposits; formation of images of oil and gas deposits; local forecast of improved reservoir properties and saturation; refinement of the geometry of target surfaces with forecast data in actual points (vertical wells); forecast of the area of target surfaces; spatial mapping of the largest hydrocarbon deposits.Results. Forecasting of oil and gas prospects in the Okhotsk Sea in areas where there is no deep drilling can be carried out using the following technologies: detailing the structural and tectonic structure of hydrocarbon traps; multi-feature prediction of hydrocarbon deposits; decomposition of the dispersion of the wave field; low-frequency resonance of seismic emission geodynamic noise; selection of oil and gas-containing objects.

Estimating geodynamic model parameters from geodetic observations using a particle method

SUMMARY Bayesian-based data assimilation methods integrate observational data into geophysical forward models to obtain the temporal evolution of an improved state vector, including its uncertainties. We explore the potential of a variant, a particle method, to estimate mechanical parameters of the overriding plate during the interseismic period. Here we assimilate vertical surface displacements into an elementary flexural model to estimate the elastic thickness of the overriding plate, and the locations and magnitudes of line loads acting on the overriding plate to produce flexure. Assimilation of synthetic observations sampled from a different forward model than is used in the particle method, reveal that synthetic seafloor data within 150 km from the trench are required to properly constrain parameters for long wavelength solutions of the upper plate (i.e. wavelength ∼500 km). Assimilation of synthetic observations sampled from the same flexural model used in the particle method shows remarkable convergence towards the true parameters with synthetic on-land data only for short to intermediate wavelength solutions (i.e. wavelengths between ∼100 and 300 km). In real-data assimilation experiments we assign representation errors due to discrepancies between our incorrect or incomplete physical model and the data. When assimilating continental data prior to the 2011 Mw Tohoku-Oki earthquake (1997–2000), an unrealistically low effective elastic plate thickness for Tohoku of ∼5–7 km is estimated. Our synthetic experiments suggest that improvements to the physical forward model, such as the inclusion of a slab, a megathrust interface and viscoelasticity of the mantle, including accurate seafloor data, and additional geodetic observations, may refine our estimates of the effective elastic plate thickness. Overall, we demonstrate the potential of using the particle method to constrain geodynamic parameters by providing constraints on parameters and corresponding uncertainty values. Using the particle method, we provide insights into the data network sensitivity and identify parameter trade-offs.

Depth-to-basement Estimation of Basin Relief Using the BP Network

Nonlinear gravity inversion is a popular method for determining basin bottom relief and delineating basin configuration. However, traditional gravity inversion presents certain challenges, including the complexity and time demand of calculating and transforming large matrices, as well as instability and non-uniqueness caused by the inherently ill-posed nature of inversion problems. Over the past decade, deep learning, a subset of machine learning, has seen successful applications in geophysical interpretation and exploration. In this study, we propose an innovative method for estimating two-dimensional (2D) depth-to-basement using a BP network structure. This structure leverages the Leaky Rectified Linear Unit (ReLU) as an activation function, yielding more realistic geophysical models.