Complex Geology Research Articles

Seismic performance and vibration damping analysis for end reinforcement tunnel working shaft in complex geology

Seismic performance and vibration damping analysis for end reinforcement tunnel working shaft in complex geology

From Shallow to Deep: Enhancing Seismic Resolution with Weak Supervision

High-resolution seismic data are crucial for accurately identifying subsurface geological formations and reservoir properties. However, as widely observed in both prestack and poststack data, seismic wave attenuation often leads to a gradual decrease in resolution with depth. This degradation leads to resolution differences between data from shallow and deep layers, characterized by high-frequency energy loss in deeper target layers. The latter could complicate the extraction of crucial deep-layer structure information, leading to increased uncertainty in reservoir modeling and significant economic losses due to our inability to image deep targets with sufficient resolution. Traditional methods with simplified assumptions about this nonlinear attenuation hinder modeling geological complexity and variability. Deep learning methods excel at capturing complex, nonlinear relationships but often rely heavily on scarce and costly paired labels for supervised learning. As a result, deep learning without requiring paired labels for seismic resolution enhancement has become a key research focus. This study explores a method to enhance 3D seismic data resolution using weakly supervised learning, leveraging the inherent similarity of sedimentary structures across depths. Specifically, our approach utilizes a relatively shallow, higher-resolution data window to transfer and extrapolate learned high-frequency information to a deeper window containing attenuated responses from deeper layers. Thereby, we achieve enhanced resolution in the target region. A tailored 3D convolutional neural network with a bidirectional cycle structure, custom-designed loss functions, and data preprocessing techniques specifically addresses the challenge of resolution differences caused by seismic wave attenuation. The effectiveness of the proposed method is validated with synthetic and real 3D poststack migration data, demonstrating its robustness for regions near the target layer. Compared to conventional spectral whitening, our approach leverages intrinsic data characteristics more adaptively and robustly, making geological structures more discernible.#xD;

Identification of Key Risk Factors in Mechanical Cross Passage Construction Based on the Decision-Making Test and Evaluation Test Method and the Interpretation Structure Model

In order to solve the problem that the key risk factors in the construction of mechanical cross passages are relatively vague, the decision-making test and evaluation test method (DEMATEL) and the interpretation structure model (ISM) are combined to analyze safety risks in the construction of mechanical cross passages scientifically and reasonably. Based on the ‘personnel’, ‘material’, ‘machine’, ‘method’, and ‘environment’ of 4M1E comprehensive management, the construction safety risk index system of mechanical method cross passage, including 18 risk factors, is extracted. DEMATEL was used to divide the risk factors into four factor sets: strong cause, weak cause, strong result, and weak result. Furthermore, ISM is used to construct a hierarchical structure diagram of risk factors, and deep risk factors and risk factors with a high node degree are divided. The results show that the safety awareness of construction personnel, the safety technology level of construction personnel, the establishment and implementation of the safety construction system, the level of construction management, and the degree of geological complexity are strong cause-type risk factors with both high centrality and high causality. Additionally, the effect of reinforcement of the internal support system, the setting of the digging parameter, the effect of grouting, and the effect of assembling the pipe sheet comprise the strong result-type risk factors with high centrality. The risk factors with higher node degree in ISM are consistent with the risk factors with higher centrality in the DEMATEL model, which are key factors that play an important role in risk control.

Geological Complexity: Bridging Cosmic Evolution and the Evolution of Life

Earth occupies a unique position in Big History as a bridge between cosmic evolution and the rise of complex life. While discussions often emphasize monumental events like the Big Bang or the rise of civilizations, the intermediate phase of planetary formation and life’s emergence reveals the intricate interplay of cosmic, geological, and biological processes. Earth’s development—from the chaotic dynamics of the early solar system, including the Moon-forming collision, to the establishment of a protective magnetic field and diverse energy gradients—created the conditions necessary for life to arise and co-evolve with the planet’s systems. This co-evolution is evident in feedback loops such as carbon sequestration, atmospheric oxygenation, and life-driven erosion processes, which shaped Earth’s environment over billions of years. Despite challenges like Snowball Earth events, asteroid impacts, and mass extinctions, the planet’s systems maintained a delicate balance of habitability, with life itself playing an active regulatory role as suggested by the Gaia hypothesis. Understanding Earth’s improbable trajectory highlights the rarity of life-bearing planets, the fragility of planetary systems, and the profound interconnectedness of cosmic and biological evolution. As human activity increasingly disrupts Earth’s delicate balance, this perspective provides valuable insights into planetary sustainability and informs the search for life on other worlds.

Forward Modeling Simulations to Validate Changes in Electrical Resistivity Tomography Monitoring Data for a Slope with Complex Geology

The electrical resistivity tomography (ERT) method has been increasingly integrated with hydrogeological risk mitigation strategies to monitor the internal conditions and the stability of natural and artificial slopes. In this paper, we discuss a case study in which numerical simulations were essential to validate the interpretation of the resistivity images obtained from an ERT monitoring system installed on a critical slope in Italy. An initial analysis of the monitoring data after rainfall events in the study site showed that the resistivity values were decreased only in the central zone along the ERT line, but they were increased in the two sides of the profile. Opposite behaviors were observed during the drying processes following the rainfall events. Core samples show complex geology at the study site, which might justify uneven responses of the different subsurface bodies to meteorological events. However, we decided to investigate the possible inversion artifacts resulting from the individual inversion of the tomographic sections. Forward modeling simulations on simplified time-lapse models of the study site were performed to explore this problem and to compare the individual and time-lapse inversions. Synthetic tests confirmed the nature of these unexpected behaviors and assessed the absolute necessity of a time-lapse approach for a correct inversion of monitoring data in the presence of a complex geological model such as the one of this case study. By applying the time-lapse inversion approach to the real data, the inversion artifact problem was substantially solved, arriving after the proper calibration of the inversion parameters, mainly the time-lapse damping factor and the spatial and temporal roughness constraints, to a reduction in the inversion artifacts to less than 5%.

CO2 Sequestration in a Carbonate Saline Aquifer: An Investigation into the Roles of Natural Fractures and Well Placement

CO2 sequestration is considered one of the main pillars in achieving the ongoing decarbonization efforts. A myriad of CO2 sequestration projects targeted sandstone reservoirs since carbonate reservoirs appeared to be unpropitious due to their geological complexity and unfavorable mineralogy and properties. This study investigates CO2 sequestration potential in a carbonate saline aquifer while considering various geological complexities by capitalizing on numerical simulation. A synthetic anticline reservoir model examined the optimum well location and landing zone for CO2 sequestration. Additionally, the model evaluated the role of natural fractures in the migration path of CO2 plume and geochemical reactions throughout the storage process. The study demonstrates that placing the injection well away from the top of the structure in a low-dip region while injecting in the bottom interval would yield the optimum design. After applying a plethora of analyses, geological complexity could impede the migration path of CO2 but eventually produce a similar path when injected in a similar region. The geochemical interactions between the injected CO2 and reservoir fluids and minerals reduce the free and trapped CO2 quantities by dissolving calcite and precipitating dolomite. Furthermore, natural fractures impact the CO2 quantities during early times only when the fractures cross the top layers. Similarly, the CO2 migration differs due to the higher permeability within the fractures, resulting in slightly different CO2 plumes. Consequently, the role of natural fractures should be limited in carbon storage projects, specifically if they do not cross the top of the reservoir. This study reflects a unique perspective on sequestering CO2 while capturing the roles of natural fractures and well placement in depicting the migration path of the CO2 plume. A similar systematic workflow and holistic approach can be utilized to optimize the subsurface storage process for potential formations.

A Comprehensive Implementation and Analysis: Decentralized Organ Giving using Blockchain Dapp for Organ Donation

Abstract—Current frameworks for organ donation and transplantation numerous difficulties with transplantation, organ retrieval ,transportation, benefactor-philanthropist matching, and enrollment. A complex geology of legal, medical, moral, and specialized restrictions must be explored by these procedures. A thorough organ blessing and transplantation plan is therefore essential to ensure a reasonable and successful preparation, idealizing endless experience and promoting acceptance. Existing systems for organ collection, preservation, and transplantation frequently require transparency, function slowly, and fall short of modern security requirements, leading to problems similar to illegal organ trade or therapeutic extortion. The proposed blockchain- based platform improves the fairness and profitability by automating the matching of donors and recipients through smart contracts. This system seamlessly integrates with healthcare databases, ensuring the secure exchange of essential medical data. By minimizing the risk of unauthorized access and data breaches, it safeguards sensitive information over the long term. Overall, this decentralized platform fosters trust, addresses patient concerns, and supports the handling of unauthorized activities within the organ donation sector. Keywords: Blockchain, Transperency, Traceability, Information Security,Transplantation,Decentralized system

GIS-Driven mineral exploration: Enhancing geological insight through aeromagnetic integration

The Rehamna Variscan massif, located in the western Moroccan Meseta, is known for its geological complexity and mining potential, requiring detailed investigation to uncover its hidden resources. This study combines spatial analysis with aeromagnetic geophysical prospecting methods, using Geographic Information Systems (GIS), which are effective for integrating spatio-temporal data and organizing research phases. The goal is to demonstrate that incorporating aeromagnetic data into GIS enhances the detection and analysis of geological structures, thus facilitating mineral prospecting. This integration identified a significant bipolar magnetic anomaly, similar to that of the Hajjar mine, indicating favorable geological structures for mineralization. It also helped map faults and other geological discontinuities, revealing new target areas for in-depth mineral exploration, particularly for precious metals. The findings highlight the effectiveness of GIS in mineral prospecting in geologically complex regions. However, interpreting the data requires expertise in geophysics and geology to improve model accuracy. This combined approach allows for more precise analysis and better planning of mining exploration efforts.

Landslide Monitoring by Radar Permanent Scattered Remote Sensing Technique in Tangier

In northern Morocco, local authorities in Tanger commonly map landslide susceptibility to support effective land use management and urban planning. The area's complex geology leads to significant land surface displacement, resulting in instability phenomena such as landslides and block falls. This study utilizes Persistent Scatterer Interferometric Synthetic Aperture Radar (PSinSAR) techniques with Sentinel-1 data in Interferometric Wide (IW) mode to evaluate ground deformation in Tanger from 2021 to 2023. We focused on mapping surface displacements in the radar line-of-sight direction by identifying persistent scatterers. The findings enhance the understanding and monitoring of slow deformation phenomena in the region, providing essential insights for land management and infrastructure planning.

Assessing the potential of geothermal energy in Cambrian complexes for renewable energy transition in Lithuania

Several depleted Cambrian oil fields in Lithuania, with water-cuts reaching 99 %, present promising opportunities for geothermal energy utilization. This study focused on Lithuania’s Cambrian reservoir complex, which has the highest water production rates. The study aimed to explore the advantages of horizontal wells, a technology not previously employed for geothermal production in Lithuania, over traditional vertical wells. To optimize field development, various scenarios were evaluated using mechanistic models, with insights subsequently applied to real field conditions. The results from the mechanistic model demonstrated that horizontal wells outperform vertical wells in both water production and power generation. Furthermore, increasing fracture intensity was shown to enhance water output and power generation by operating the wells at pressures above fracture propagation, facilitating re-injection. However, the study also emphasized the importance of thorough reservoir characterization and modeling to account for geological complexities and improve production outcomes. In conclusion, the research underscores that horizontal wells offer several advantages over vertical wells for geothermal energy production. These include increased water production, higher power generation, reduced drilling costs, and enhanced operational efficiency. These benefits align with technological advancements observed in the hydrocarbon industry, making horizontal wells a viable solution for maximizing geothermal energy potential in Lithuania’s depleted oil and gas fields.

Facie classification of X-field in the Niger Delta using machine learning

The Niger Delta's complex geology presents challenges for accurate facies classification and reservoir characterization. This study applies machine learning techniques to well log data from four wells in the X-Field: Freeman-001, Freeman-003ST1, Freeman-004ST1, and Freeman-005. Key features analyzed include Gamma Ray (GR), Sonic Transient Time (DT), Bulk Density (RHOB), and Neutron Porosity (NPHI). Unsupervised learning methods (K-means, GMM, and Agglomerative Clustering) yielded moderate clustering performance, with K-means achieving the highest accuracy of 47.87%, followed by GMM (20.38%) and Agglomerative Clustering (13.94%). Supervised learning methods significantly outperformed unsupervised ones. Random Forest achieved 87.06% accuracy, 87.07% precision, and an F1-score of 87.06%. Gradient Boosting delivered the best results, with 87.53% accuracy, 87.55% precision, and an F1-score of 87.54%. SVM showed lower performance, with 77.86% accuracy and an F1-score of 77.82%. These findings highlight the superior performance of supervised learning, particularly tree-based models, in facies classification and underscore the potential of machine learning to enhance reservoir characterization in the Niger Delta.

Data-driven assessment of well stimulation in unconventional gas reservoirs

Unconventional gas reservoirs, characterized by their complex geologies and challenging extraction conditions, demand innovative approaches to enhance gas production and ensure economic viability. Well stimulation techniques, such as hydraulic fracturing and acidizing, have become indispensable tools in unlocking the potential of these tight formations. However, the effectiveness of these techniques can vary widely depending on the specific characteristics of the reservoir. In this context, a data-driven approach to assess well stimulation practices offers a promising avenue to optimize recovery processes and reduce uncertainties. This paper presents a comprehensive study that leverages the power of big data analytics and machine learning to analyze and improve well stimulation strategies in unconventional gas reservoirs. By systematically gathering and processing vast arrays of geological, operational, and production data, this study aims to identify patterns and correlations that can predict stimulation outcomes more accurately. The ultimate goal is to develop a robust framework that allows for tailored stimulation designs based on the unique properties of each reservoir, thereby maximizing efficiency and minimizing environmental impacts. This study introduces a new procedure for assessing well stimulation performance, which involves analyzing the EUR through Duong’s model, calculating the key performance indicator of the treatment, and establishing a data-driven model to predict the treatment KPI.

Research on Realization of Coal Mine Impact Ground Pressure Signal Early Warning Based on BP Neural Network and LSTM

With the reduction of coal resources makes deep coal seam mining important, but also increases the ground stress and geological complexity, especially impact ground pressure.AE signal monitoring technology plays an important role in preventing impact ground pressure by real-time monitoring of cracks and stress changes in coal rock. In this paper, wavelet analysis and RFE techniques are used to extract key features from signals, and BP neural network is chosen as the classification model after comparing multiple machine learning models. Random Forest is accurate but has low recall, and LSTM suffers from slow classification and poor generalization, neither of which is applicable. Compared with prophet and ARIMA models, LSTM performs better in high-frequency data prediction and dealing with fluctuating data, so it constructs a coal mine impact ground pressure signal early warning system based on BP neural network and LSTM to improve the accuracy of coal mine safety monitoring. The innovation of this paper is to use different network frameworks for classifiers and predictors, and by using feature extraction techniques, data transformation is performed to maintain the accuracy of the results while reducing the model complexity, improving the response speed, and enhancing the model generalization ability.

Development of a new composite surface tensiometry parameter for quality evaluation of the maturation process of peloids by contact angle method

With the introduction of the rheological and surface tensiometry approaches, peloids started to be viewed as a geological complex system that needed to be characterized in an integrated way. The development of the Rossi factor revealed variations in the surface free energy of Euganean thermal muds as a function of their quality, conformity, and matura-tion degree. The Rossi factor consents to measuring the contact angle of Fomblin HC/25PFPE Polyperfluoromethylisopropyl Ether by static sessile drop method. The de-velopment of the Kinetic Contact Angle sessile drop methodology, applied to the meas-urement of Fomblin HC/25 PFPE contact angles over time, led to the determination of an integrated surface tensiometry parameter for the evaluation of the maturation process of Euganean thermal peloids, here called the Maturation Mud Index. The Mud Maturation Index is based on the contact angle measurements performed onto peloids surfaces col-lected by Osservatorio Termale Permanente of University of Padova from 2005 to 2010. The Mud Maturation Index has shown a connection to the activity of thermal spas during their pelotherapy season. As a result, this index can serve as a valuable tool for surface tensiometry in assessing the maturation process quality of a peloid based on its level of maturation within the thermal activity of the Euganean Thermal Area in Padova, Italy.

Seismic attribute analysis of the Lokbatan-Puta-Gushkhana field

This study presents a detailed seismic attribute – mainly Root Mean Square (RMS) amplitude and Sweetness attribute analysis, aiming to improve the understanding of subsurface structures. We apply both these seismic attributes on a specific petroleum field called Lokbatan-Puta-Gushkhana. Additionally, the study investigates the use of RMS amplitude analysis in the characterization of reservoirs, focusing on the estimation of fluid volume and porosity. The results offer useful guidance for reservoir management techniques and aid in the optimization of reservoir modeling. Seismic attributes listed above (RMS Amplitude, Sweetness) make it possible to distinguish zones by variable properties of the wave field. If we observe that the calculated seismic attributes differ significantly, then it can be assumed that the elastic properties of this area (velocity, density, etc.) have also changed. Differentiation of elastic properties and, accordingly, facies compositions allows for predicting the distribution of reservoirs. The outcomes of this study have a wide range of implications for the petroleum industry and provide a solid methodology for using RMS amplitude analysis in reservoir characterization and basin-scale exploration. The insights derived from this study can help with decision-making in the oil and gas exploration industry, enhancing resource evaluation and promote the development of sustainable energy sources. In order to study the distribution of reservoirs in the Lokbatan-Puta-Gushkhana area of the Absheron oil and gas region in the Productive Series (PS) and the underlying sediments (Diatom and Maykop), the results of the complex studies conducted according to the geophysical survey of the wells and 2D seismic data were analyzed. Additionally, we combine RMS attribute analysis with geological interpretations to improve reservoir models and drilling strategies. Our findings not only contribute to the understanding of the geological complexities of the Lokbatan-Puta-Gushkhana field but also offer practical insights for hydrocarbon exploration and production in this prolific region.

Analysis of the Distribution and Management of Tubular Wells in the Urban Area of Cuiabá, Mato Grosso

Roundwater exploitation in urban areas, particularly in fractured rocks, faces significant challenges due to geological complexity impacting water resource management. This study aims to analyze the concentration and distribution of tubular wells in Cuiabá, identifying areas of overexploitation. Technical data on wells, including coordinates, flow rates, and pumping regimes, were organized in a database and spatially analyzed using density and interpolation techniques. The results reveal a high concentration of illegal wells, highlighting uncontrolled exploitation of groundwater resources with implications for sustainability. The analysis showed that 81.4% of wells are used for supply and sanitation, reflecting the increasing pressure on resources. The spatial distribution of illegal wells indicates higher vulnerability in specific areas, suggesting the need for stricter control. It is concluded that effective mapping and regulation of wells are crucial for sustainable groundwater resource management in Cuiabá.

Unveiling Differences in Seismic Response: Comparative Study of Equivalent Linear and Nonlinear Analyses in the Central Coastal Region of Bengkulu, Indonesia

Seismic response analysis is a key aspect in earthquake geotechnical engineering, as it provides important insights into the behavior of soils when exposedtoseismic forces. This research compares equivalent linear and non-linear models in the central coastal region of Bengkulu, which is known for its complex geology and high seismicity. By evaluating the accuracy and reliability of each model in predicting ground motion amplification,this research aims to provide useful recommendations for seismic design. The research method uses one-dimensional equivalent linear and nonlinear propagation modeling, namely Pressure Dependent Hyperbolic (PDH). The analysis resulted in the parameters of Peak Ground Acceleration (PGA), time history acceleration, spectral response acceleration, and amplification factor. The equivalent linear method consistently produced higher values for peak ground acceleration (PGA), spectral response acceleration, time history acceleration, and amplification factor compared to the nonlinear method. The analysis results show that the equivalent linear PGA values are in the range of 0.32g to 0.63g, while the nonlinear values range from 0.20g to 0.52g. The resulting spectral responses are averaged over the design spectrum within 0.2 s to 0.9 s, which can affect low- to high-ceilinged buildings. The equivalent linear amplification factor has a range of 1.59 to 1.91, while the nonlinear has a range of 0.80 to 1.59. Both methods have their advantages, with the nonlinear approach offering greater accuracy for large seismic events, while the equivalent linear model remains useful for preliminary analysis. Hopefully, these findings will improve the understanding of ground response in coastal areas and provide valuable data for improving infrastructure resilience in earthquake-prone areas around the world.

Geoscientist-driven machine learning-assisted fault interpretation: A case study from Atlanta Field to demonstrate the impact of fault labeling on the fault prediction cube

The energy industry is undergoing a digital transformation, leveraging cloud capabilities and artificial intelligence technology to overcome the challenges of conventional geoscience workflows. Machine learning (ML) methodologies have been developed and applied to various geophysical and geologic workflows, such as seismic processing, imaging, seismic interpretation, and petrophysical analysis. The traditional seismic interpretation approach requires manual interpretation of faults line by line at a fixed increment. Consequently, depending on the geologic complexity and size of the area of interest, the traditional approach can be a time-consuming and repetitive task. We focus on applying ML to seismic data for fault interpretation using a 2D convolutional neural network methodology to accelerate the fault interpretation process. This approach has the potential to significantly reduce the time and effort required to interpret faults, depending on the seismic data quality and geologic complexity, which is demonstrated by our study in the Atlanta Field, a postsalt asset in Brazil's northern Santos Basin. The basin has high structural complexity due to the salt tectonics, which required additional labeling interpretation to capture different fault patterns and train the algorithm to predict faults within the seismic volume. Additionally, the study provides the impact of the initial fault interpretation label on the ML result, such as in the fault extension and fault dip. Overall, the ML approach reduced the total effort from two weeks to four days, representing a time improvement of 60% from interpretation to structural framework workflow.

Heterogeneity Modeling and Heterogeneity-Based Upscaling for Reservoir Characterization and Simulation

Summary Reservoir heterogeneity is a key factor in modeling reservoir performance. Heterogeneity measures can be calculated for a given permeability field but are not sufficiently straightforward to reverse the process. Detailed heterogeneity can be built into a fine-scale model but can be lost during upscaling to a coarse scale, no matter which method is chosen from simple averaging to flow-based (FB). In this paper, we propose a method of heterogeneity modeling and heterogeneity-based upscaling with the aim of solving these problems. Unlike the traditional geostatistical method used to generate a permeability field that is not directly linked to a desired heterogeneity coefficient, the proposed method creates a heterogeneous permeability field directly using a Lorenz coefficient. Using a given Lorenz coefficient as an input, the expected heterogeneous permeability field can be generated via the proposed steps and equations. Using the proposed heterogeneity-based upscaling method, the Lorenz coefficient and curve, defined from the fine-scale model, can be preserved with minimum heterogeneity loss after upscaling such that gas/oil ratio (GOR) can be matched between the fine- and coarse-scale models without using pseudofunctions. The proposed method has been applied successfully in modeling a giant carbonate oil field in the Caspian Sea consisting of a matrix-dominated platform and a fracture/karst-dominated rim. Due to the field’s complex geology and high H2S content, a dual-porosity/dual-permeability compositional model has been created to model compositional flow within/between matrix and fracture/karst initialized with an abnormally high reservoir pressure. The field surveillance data show that reservoir heterogeneity is a key component for the field reservoir characterization and simulation. The Lorenz coefficient and curves can be estimated from the cores and logs, but the challenge is how to preserve the characteristics of the Lorenz coefficient and curves during modeling, upscaling, history matching, and uncertainty analysis (UA). Application of the new method has demonstrated its ability to overcome this challenge and has significantly improved the quality of the field’s reservoir modeling, upscaling, history matching, and UA. The fine-scale model Lorenz coefficient and curves were directly applied to calculate the coarse-scale permeability. The range of the Lorenz coefficient and curves estimated from cores and logs were used to generate a range of heterogeneous permeability fields for UA. Regional Lorenz coefficients were adjusted based on the mismatches of GOR, and new heterogeneous permeability fields were generated to improve the history-matching quality.

Application of Source Independent Full Waveform Inversion to Land Seismic Data: A Case Study of Donggi Field, Indonesia

Abstract Full-waveform inversion (FWI) is a data-fitting process based on seismic full-wavefield modeling that provides high-resolution images of subsurface geological structures from observed field data. However, its applicability to real-world scenarios, particularly in land data, is limited due to several factors. These factors include the lack of modeling of strong surface waves, converted waves, phase dispersion, near-surface weathering layer distortion, and wave phenomena such as attenuation energy and distortion because of complex geology. Additionally, the source wavelet information is often missing for land surveys, and recorded waveforms can vary significantly due to spatially variant shot characteristics such as source charge size, shot depth, and hole pattern, as well as the distortion caused by the near-surface weathering layer. In this study, we address the main challenges of seismic data with a low signal-to-noise ratio and an unknown correct source wavelet. To solve for the unknown source wavelet, we use a source-independent objective function and perform several preprocessing techniques on the seismic input to improve the signal-to-noise ratio without significantly changing the amplitude. We use velocity analysis data as a prior model to reduce the non-linear problem. We demonstrate the effectiveness of our technique by applying it to land seismic data from the Donggi Basin in Indonesia, and our results show that the velocity model improves and has a better correlation with well-log data.