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Seismic inversion holds significant potential for providing crucial lithostratigraphic information in hydrocarbon reservoir characterisation and in identifying new traps. However, one of the major challenges in achieving reliable reservoir models in Algeria stems from the inherent uncertainties associated with seismic inversion algorithms and the non-linear relationship between petrophysical measurements. Due to their usefulness, several Artificial Neural Network algorithms have been developed and employed for seismic inversion and reservoir characterisation in the last few years. Nevertheless, only few researchers have addressed this issue in terms of optimisation of Multilayer Feed-Forward Neural Network (MLFN) architecture. In this case study, the use of an MLFN to address these challenges is proposed. The primary contribution of this research lies in the optimisation of the MLFN architecture based on trial and error procedures. The goal is to ensure that the computational demands are manageable within the constraints of available computing resources and that the process is time-efficient for geo-modellers. This practical approach is particularly valuable when applied at the reservoir scale. MLFN supervised training is conducted using logging data, where measured log curves serve as inputs, and core porosity, obtained from laboratory analysis, serves as target output. Moreover, coloured inversion is employed to generate a 3D seismic acoustic impedance cube, which, in turn, serves as input for a model-based inversion method designed to calculate porosity volume using the trained network. Furthermore, the usefulness of the resulting density cube is demonstrated through the correlation with density logs and core density values at wells 1, 2, and 3. Thence, the obtained correlation ranges validate the reliability of the obtained porosity volume in enhancing the characterisation of the targeted reservoir within the Algerian Saharan field. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute and a half later. The SFB is known for its blind thrust faults, typically not associated with surface ruptures. These earthquakes are usually linked to the middle and lower layers of the sedimentary cover. Identifying the faults that trigger earthquakes in the region remains a significant challenge and is subject to high uncertainty. This study aims to identify and determine the fault(s) that may have caused the doublet earthquake. To achieve this goal, we utilized the DInSAR method using Sentinel-1 to detect deformation, followed by finite-fault inversion and magnetic interpretation to determine the location, geometry, and slip distribution of the fault(s). Bayesian probabilistic joint inversion was used to model the earthquake sources and derive the geometric parameters of potential fault planes. The study presents two potential fault solutions—one dipping to the north and the other to the south. Both solutions showed no significant difference in strike and fault location, suggesting a single fault. Based on the results of the seismic inversion, it appears that a north-dipping fault with a strike, dip, and rake of 257°, 74°, and 77°, respectively, is more consistent with the geological setting of the area. The fault plane has a width of roughly 3.6 km, a length of 13.4 km, and a depth of 5.6 km. Our results revealed maximum displacements along the radar line of sight reaching values of up to −360 mm in the ascending orbit, indicating an unknown fault with horizontal displacements at the surface ranging from −144 to 170 mm and maximum vertical displacements between −204 and 415 mm. Aeromagnetic data for Iran were utilized with an average flight-line spacing of 7.5 km. The middle of the data observation period was considered to apply the RTP filter, and the DRTP method was used. We calculated the gradient of the residual anomaly in the N-S direction due to the direction of the existing faults and folds. The gradient map identified the fault and potential extension of the observed anomalies related to a fault with an ENE-WSW strike, which could extend to the ~ E-W. We suggest that earthquakes occur in the sedimentary cover of the SFB where subsurface faulting is involved, with Hormuz salt acting as an important barrier to rupture. The multidisciplinary approach used in this study, including InSAR and magnetic data, underscores the importance of accurate fault characterization. These findings provide valuable insights into the seismic hazard of the area. [ABSTRACT FROM AUTHOR]

The portrayal of sand cusp extinction is the key to evaluating the effectiveness of tectonic-lithological composite confinement, and it is difficult to accurately identify the location of sand cusp extinction due to the influence of seismic data band limitations. In order to effectively solve the problem of inaccurate identification of cusp extinction on seismic sections, this paper innovatively introduces the dual advantages of time-frequency resolution and energy focusing of synchronous compression match tracing to carry out the sand-cusp extinction identification research. Firstly, the time-frequency domain pleated-product operator is used to construct the redundancy dictionary of match tracing with the constraints of the initial model, and all the possible matching atoms are screened out to form the alternative atom bank in the time-frequency domain. The initial model constraints and the time-frequency domain joint inversion method are introduced into the inversion framework, which can obtain more accurate inversion results. Then, the dominant frequencies are extracted by channel-by-channel analysis using the simultaneous compressive match-tracking transform, and the time-frequency transient spectra are screened to obtain the single-frequency spectral profile with the optimal frequencies. The two-dimensional model and actual data testing of the proposed method show that the high-resolution seismic spectral decomposition sand-cusp identification method based on synchronous compression and matching tracking can obtain richer time-frequency domain information and get the single-frequency spectral profile with the optimal frequency, thus accurately identifying the location of cusps of subsurface geological bodies, which is of great significance for the depiction of cusps of overburden-type and erosion-type lithological confinement.

Abstract This study integrates seismic inversion and rock physics techniques to evaluate the hydrocarbon potential of an offshore field in the Niger Delta. Five wells revealed three reservoir sands with favourable reservoir properties, including gross thickness (49.2–81.4 m), porosity (0.18–0.2), permeability (565–1481 mD), and water saturation (0.16–0.54). A robust wavelet extraction process was implemented to guide seismic inversion, and a well log-centric approach was employed to validate the resulting acoustic impedance data. Rock physics analysis established correlations between acoustic impedance (Zp), porosity, fluid content, and lithology, enabling the identification of hydrocarbon-filled sands, brine-saturated sands, and shales. These relationships enabled the discrimination of hydrocarbon-filled sands [5000–8000 (m/s)(g/cc)], from brine-saturated sands [5600–8400 (m/s)(g/cc)], and shales [5000–9000 (m/s)(g/cc)] within the inverted seismic data. The inverted acoustic impedance section showed a general increase with depth, reflecting the typical compaction effects in the Niger Delta. Analysis of the impedance distribution across horizon time slices revealed prospective zones with low impedance values [below 6300 (m/s)(g/cc)], particularly in horizons 1 and 2. These newly identified zones exhibit the strongest potential for hydrocarbon accumulation and warrant further investigation. This study demonstrates the effectiveness of using well log and rock physics constrained seismic inversion for hydrocarbon exploration in an offshore field in the Niger Delta.

A tight sandstone gas reservoir is characterized by low porosity and permeability. The existence of fractures can improve the permeability of the reservoir, and fractures are a vital storage space and migration channel of oil and gas. The development of fractures is also conducive to forming a fracture network during hydraulic fracturing. The prediction of fractures can provide an essential basis for developing tight sandstone gas reservoirs. The variation of seismic amplitude with azimuth can provide information on vertical fractures in the reservoir. This study proposed an improved inversion method of azimuth amplitude difference for the HTI (transversely isotropy with a horizontal axis) medium and combined with the rock physical theory, predicted fracture weakness parameter, characterizing the fracture properties. Conventional inversion methods invert elastic and fracture parameters simultaneously. The improved azimuth amplitude difference method introduces a reference azimuth, builds the track set of azimuth amplitude difference to eliminate the isotropic background, and inverts only the fracture weakness parameters related to the anisotropic terms. Using the only azimuth anisotropic response, the sensitivity of fracture identification and the accuracy of fracture parameter inversion are improved. The application of the field data shows that the sensitivity of the proposed inversion method to the prediction of fracture parameters is improved compared with the conventional inversion method, and the predicted fracture weakness is consistent with the permeability well logs and has a significant correlation with the gas production of tight sandstone gas reservoirs. Therefore, the prediction of fracture distribution and development degree based on the proposed method can provide a reliable indicator for the identification and development of gas-bearing favorable areas in tight sandstone reservoirs.

Nowadays, the most successful applications of full‐waveform inversion (FWI) involve marine seismic data under acoustic approximations. Elastic FWI of land seismic data is still challenging in theory and practice. Here, we propose a full dispersion spectrum inversion method and apply it to seismic data acquired in West Antarctica. Inspired by the conventional surface wave dispersion curve inversion method, we propose to invert the surface wave dispersion spectrum instead of the complicated waveforms. We compare the frequency‐velocity, frequency‐slowness, and frequency‐wavenumber spectra in terms of their ability to resolve dispersion modes and the feasibility of their adjoint updates and conclude that the frequency‐slowness spectrum is the best for our inversion objectives. We test four objective functions, subtraction, zero‐lag crosscorrelation, optimal transport, and the local‐crosscorrelation to quantify the spectrum mismatch and provide the corresponding adjoint source. We then theoretically analyze the convexity of the proposed objective functions and examine their convergence behavior using numerical examples. We also compare the proposed method with the classic FWI method and the traditional surface wave dispersion curve inversion method and discuss the strengths and weaknesses of each method. This technique is employed to evaluate the shallow velocity structures beneath a seismic array stationed in West Antarctica. Our proposed inversion scheme is also useful for more general applications such as imaging the shallow subsurface of the critical zones, like geothermal reservoirs, and CO2 storage sites. Plain Language Summary: Seismic full‐waveform inversion (FWI) is a cutting‐edge inversion method used for uncovering the Earth's subsurface structure. With the growing interest in clean energy and CO2 sequestration, exploring the subsurface in land is becoming crucial. However, there are only a few success stories of seismic FWI applied to land data mainly because of the complexity of the near‐surface and the increased nonlinearity of the problem. Here, we propose a full dispersion spectrum inversion method that seeks optimal velocity models in the subsurface by matching the seismic dispersion spectra. Dispersion spectra are the skeleton of seismic surface waves, which are simpler to quantify yet retain the key dispersion information of surface waves. It is generally easier for humans, as well as algorithms, to match simplified representations of the observed and simulated data, such as the dispersion spectrum, instead of the seismic waveforms themselves. The proposed method expands conventional 1D dispersion curve inversion to multiple dimensions and is accomplished under the framework of FWI. The proposed inversion method applies to seismic imaging applications in exploration and global seismology. Key Points: We propose a full dispersion spectrum inversion method for imaging the near‐surface and apply it to seismic array data collected in West AntarcticaThe surface wave frequency‐slowness spectrum is preferred over the frequency‐velocity and the frequency‐wavenumber spectra to measure the misfit in the proposed inversionThe optimal transport objective function is immune to cycle skipping but has a lower model resolution [ABSTRACT FROM AUTHOR]

Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998. Plain Language Summary: Understanding subsurface properties, such as porosity, is crucial for various applications, such as identifying energy resources and determining suitable locations for CO2 storage. Typically, obtaining the latter involves using the inversion of seismic records, which can be a challenging process with multiple possible outcomes. Using many seismic measures leads to data size expansion and challenges the processing, necessitating additional time and resources. Deep learning is highly effective at accurately measuring subsurface features when analyzing large amounts of random data. As a result, we performed calculations on seismic measurements and used horizons and borehole records to collect porosity samples. We have put forward a technique for identifying the most pertinent metrics. Afterward, we conducted training on multiple networks to make predictions about porosity. We developed several models and created a "weight‐averaging ensemble" to account for the random nature of the networks. This considers the power of baseline models when incorporating realizations, resulting in a strong, more accurate model. We used statistical analysis to determine the level of uncertainty in the approach. The proposed method for measuring porosity in the F3‐Netherlands offshore block is faster than traditional approaches and produces results similar to a reference model. Key Points: This study developed an approach to identify pertinent seismic attributes for porosity inversion with uncertainty estimatesThese attributes are used to train an array of distinct networks used to generate multiple realizationsA grid search is developed to find the best weights assigned to these realizations, which are combined to achieve the most precise result [ABSTRACT FROM AUTHOR]

The assessment of hydrocarbon flow through seismic and well-log data presents a persistent challenge in determining porosity. The acoustic impedance section provides a visual representation of the layers, while the raw seismic data showcase the subsurface reflectors that exist within the rock layers. The accuracy of acoustic impedance is widely acknowledged to surpass that of seismic data as a representation of reality. The primary objective of this study is to convert seismic reflector data into acoustic impedance values, which provide insights into the layer properties based on lithology. This approach enhances the accuracy of seismic inversion results by aligning them more closely with actual geological conditions. Seismic inversion is employed to ascertain the physical characteristics of the rock, including acoustic impedance and porosity. Carbonate reservoirs are recognised for their complex pore structures and heterogeneity, which present difficulties in their characterisation. The objective of this research is to predict the porosity and identify the reservoir within the dense carbonate reservoirs in Central Luconia, Sarawak. These objectives are achieved by employing a porosity and acoustic impedance cross-plot and improved precision and predictability through the integration of seismic attribute interpretation and deterministic seismic inversions. The uniqueness of our approach stems from the incorporation of various geophysical techniques to detect reservoirs that have hydrocarbon deposits. A correlation is observed between seismic inversion acoustic impedance and porosity within the zone of interest, indicating an estimated porosity range of 10–35%. The analysed area demonstrates the possibility of containing a hydrocarbon based on the observed relationship between porosity and impedance, as well as the outcomes of the inversion analysis. [ABSTRACT FROM AUTHOR]

Copyright of Reviews of Geophysics & Planetary Physics is the property of Editorial Office of Reviews of Geophysics & Planetary Physics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Seismic inversion, complemented by machine learning algorithms, significantly improves the accuracy and efficiency of subsurface parameter estimation from seismic data. In this comprehensive study, a comparative analysis of machine learning techniques is conducted to predict subsurface parameters within the inter-well region. The objective involves employing three separate machine learning algorithms namely Probabilistic Neural Network (PNN), multilayer feedforward neural network (MLFNN), and Radial Basis Function Neural Network (RBFNN). The study commences by generating synthetic data, which is then subjected to machine learning techniques for inversion into subsurface parameters. The results unveil exceptionally detailed subsurface information across various methods. Subsequently, these algorithms are applied to real data from the Blackfoot field in Canada to predict porosity, density, and P-wave velocity within the inter-well region. The inverted results exhibit a remarkable alignment with well-log parameters, achieving an average correlation of 0.75, 0.77, and 0.86 for MLFNN, RBFNN, and PNN algorithms, respectively. The inverted volumes portray a consistent pattern of impedance variations spanning 7000–18000 m/s*g/cc, porosity ranging from 5 to 20%, and density within the range of 1.9–2.9 g/cc across the region. Importantly, all these methods yield mutually corroborative results, with PNN displaying a slight edge in estimation precision. Additionally, the interpretation of the inverted findings highlights anomalous zones characterized by low impedance, low density, and high porosity, seamlessly aligning with well-log data and being identified as sand channel. This study underscores the potential for seismic inversion, driven by machine learning techniques, to swiftly and cost-effectively determine critical subsurface parameters like acoustic impedance and porosity. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to investigate the effect of sedimentary environments and diagenesis processes on the reservoir quality of the Asmari Formation in Kupal field by combining the results of seismic sequence stratigraphy, petrophysical evaluation, seismic attributes and the inversion of threedimensional seismic data to determine more precisely the process of changes in the reservoir properties of zones 1 to 5 of the sequences of the carbonate part of the Asmari Formation. In this study, the inversion method was used to extract acoustic impedance from 3D seismic data by Hampson Russell software. There are different methods of inversion of seismic data, according to the available data and the conditions of the Kupal field, in this study, model-based inversion is chosen due to its higher accuracy and separation power. The special aim of this article is to determine the changes in the reservoir properties of the Asmari formation by combining the results of the inversion of seismic data and seismic indicators with the results of sedimentological studies, sequence stratigraphy. According to sequence of stratigraphic studies, Asmari Formation has three sequences. The lower sequence is in the sandstone section and the middle and upper sequences are in the carbonate section. According to petrophysical studies, Asmari reservoir has 7 main zones. Zones 1 to 5 are in the upper and middle sequences of the carbonate section, zones 6 and 7 are in the lower sequence of the sandstone section. After matching the results of inversion with the results of sedimentology, the stratigraphic sequence was identified as the middle sequence, which includes zones 3, 4, and 5. It settled in a carbonate platform sedimentary environment. This sedimentary sequence is influenced by digenesis factors after deposition and the conditions of the sedimentary environment during deposition. Zones 3, 4, and 5 have sporadic changes in acoustic impedance indicators, layer thickness, and porosity. The most important factor of these changes can be due to the presence of mounds and reefs in these sediments. According to the dissolution or cementation of reefs, these zones may have high porosity and low impedance (dissolution) or low porosity and high impedance (cementation) in reefs. The final sequence of the Asmari formation, which includes zones 1 and 2, was deposited in the Sabkhai environment. This condition has caused primary or secondary porosity between the dolomite crystals. The changes in porosity and acoustic impedance and the amplitude of zones 1 and 2 are very small. Due to the low depth of the sedimentary basin, we see the activity of river systems in some places, which have caused erosion and deposition. The sediments of these rivers can have high porosity and low acoustic impedance. These sediments have high reservoir quality. The implications of this study suggest that in Maron and Aghajari fields are adjacent to the Kupal field and they have three-dimensional seismic data. Coincidentally, they can indicate a interpretation of threedimensional seismic data and stratigraphic seismic sequence in these fields. [ABSTRACT FROM AUTHOR]

Abstract The assessment of hydrocarbon flow through seismic and well-log data presents a persistent challenge in determining porosity. The acoustic impedance section provides a visual representation of the layers, while the raw seismic data showcase the subsurface reflectors that exist within the rock layers. The accuracy of acoustic impedance is widely acknowledged to surpass that of seismic data as a representation of reality. The primary objective of this study is to convert seismic reflector data into acoustic impedance values, which provide insights into the layer properties based on lithology. This approach enhances the accuracy of seismic inversion results by aligning them more closely with actual geological conditions. Seismic inversion is employed to ascertain the physical characteristics of the rock, including acoustic impedance and porosity. Carbonate reservoirs are recognised for their complex pore structures and heterogeneity, which present difficulties in their characterisation. The objective of this research is to predict the porosity and identify the reservoir within the dense carbonate reservoirs in Central Luconia, Sarawak. These objectives are achieved by employing a porosity and acoustic impedance cross-plot and improved precision and predictability through the integration of seismic attribute interpretation and deterministic seismic inversions. The uniqueness of our approach stems from the incorporation of various geophysical techniques to detect reservoirs that have hydrocarbon deposits. A correlation is observed between seismic inversion acoustic impedance and porosity within the zone of interest, indicating an estimated porosity range of 10–35%. The analysed area demonstrates the possibility of containing a hydrocarbon based on the observed relationship between porosity and impedance, as well as the outcomes of the inversion analysis.

Understanding subterranean reservoirs, geological characteristics, fluid composition, and hydrocarbon potential strongly relies on precise reservoir characterization. Seismic inversion is a key method in reservoir characterization to approximate the acoustic impedance and porosity of underlying rock formations using seismic and well-log data. A sparse layer reflectivity (SLR) post-stack inversion method approach is used in this study to make thin layers more visible. To generate an impedance volume, it uses a predetermined wavelet library, an objective function, and a regularization parameter, the regularization parameter is a tunable parameter used to control the balance between fitting the data closely (minimizing the misfit) and ensuring a smooth and stable model for and sparseness computed coefficients. This study uses Blackfoot data to estimate the density, velocity, impedance, and porosity of a particular region using the SLR and Radial Basis Function Neural Network (RBFNN). According to the interpretation of the impedance section, a low impedance anomaly zone with an impedance range of (8500–9000) m/s*g/cc is present at a time of (1040–1065) ms. The low impedance zone is classified as a clastic glauconitic sand channel (reservoir zone) based on the correlation between seismic and borehole data. Further, a Radial Basis Function Neural Network (RBFNN) has been applied to the data to estimate porosity volume and to conduct a more thorough examination of the reservoir zone and cross-validate inverted results. The research demonstrates that the high porosity zone, low velocity, and density zone are discovered by the RBFNN technique, and the low impedance zone interpreted in inversion findings are correlating, which confirms the existence of the glauconitic sand channel. This research is crucial for understanding how well SLR, RBFNN, and multi-attribute analysis work to define sand channels. [ABSTRACT FROM AUTHOR]

Reservoir prediction and prospectivity are performance indices of the oil and gas field development planning and reserves estimation which depicts the behaviour of the reservoir in the future; its success is dependent on accurate description of the reservoir rock properties, fluid properties, rock-fluid properties and flow. Hence, the objective of this paper was to investigate and predict the reservoir and prospectivity of Omos Field, Onshore Niger Delta Basin, by integrating appropriate standard methods. Results obtained from rock physics attribute map analysis revealed very high Mu-Rho values between 13.0-16.2 Gpa, that is expected for sand presence, and this agrees with the good quality reservoir sands predicted from sequence stratigraphy study.Lambda-Rho values obtained ranges between - 1 to 9.5 GPa, with a much lower value between -1 to 4.3 in the prospective zone that indicates the presence of hydrocarbon bearing sand. These results agree with the results of structural and amplitude maps analysis. Hence the prospective reservoir has a very good hydrocarbon potential in this zone and should be a target for further appraisal for development and production purposes [ABSTRACT FROM AUTHOR]

In this study, acoustic impedance (P-impedance) distribution in the subsurface of the F3 block, Netherlands is determined using the linear programming (l1-norm) sparse spike inversion (LPSSI) method. The objectives of the study are to characterize the sand channel and extract high-resolution subsurface rock features from the low-resolution seismic data. To estimate rock properties from seismic data, a variety of conventional post-stack seismic inversion techniques are available. However, the LPSSI technique is a reasonably quick and easy-to-compute subsurface model that can be employed for both quantitative and qualitative interpretation. The method is employed in two steps: first, composite traces close to well locations are retrieved and inverted for acoustic P-impedance, and then optimization of the LPSSI parameters is done using comparison with well log impedance. According to the analysis of the composite traces, the algorithm performs well and has a high average correlation (0.98). The F3 block seismic data are utilized in the second stage to estimate the distribution of acoustic impedance in the subsurface by using the LPSSI method. A sand channel-like low impedance anomaly with a range of 3800–7400 m/s g/cc is evident in the inverted acoustic impedance analysis at the 1380–1400 ms time interval. Then, using a deep feed-forward neural network (DFNN), many other crucial rock parameters, including porosity, density, and P-wave velocity, were estimated in the inter-well region to corroborate the sand channel. Following the analysis of these petrophysical properties, a high porosity zone (24–40%), low-density zone (1.9–2.02 g/cc), and low P-wave velocity zone (1700–2300 m/s) are present in the 1380–1400 ms time interval, which aligns with the low impedance zone and validates the presence of the sand channel. [ABSTRACT FROM AUTHOR]

Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hundreds of kilometers apart. Laterally interpolating these sparse measurements provides smooth temperature and salinity distributions within the oceans, although they may not be very accurate. Marine seismic data, on the other hand, show visible reflections within the water-column which are primarily controlled by subtle sound-speed variations. Because these variations are functions of the temperature, salinity, and pressure, estimating sound-speed from marine seismic data and relating them to temperature and salinity have been attempted in the past. These seismically derived properties are of much higher lateral resolution (less than 25 m) than the sparse measurements and can be potentially used for climate and marine biology research. Estimating sound-speeds from seismic data, however, requires running iterative seismic inversions, which need a good initial model. Currently practiced ways to generate this initial model are computationally challenging, labor-intensive, and subject to human error and bias. In this research, we outline an automated method to generate the initial model which is neither computational and labor-intensive nor prone to human errors and biases. We also use a two-step process of, first, estimating the sound-speed from seismic inversion data and then estimating the salinity and temperature. Furthermore, by applying this method to real seismic data, we demonstrate the feasibility of our approach and discuss how the use of machine learning can further improve the computational efficiency of the method and make an impact on the future of climate modeling, weather prediction, and marine biology research. [ABSTRACT FROM AUTHOR]

The Hikurangi Margin east of New Zealand's North Island hosts an extensive gas hydrate province with numerous gas hydrate accumulations related to the faulted structure of the accretionary wedge. One such hydrate feature occurs in a small perched upper‐slope basin known as Urutī Basin. We investigated this hydrate accumulation by combining a long‐offset seismic line (10‐km‐long receiver array) with a grid of high‐resolution seismic lines acquired with a 600‐m‐long hydrophone streamer. The long‐offset data enable quantitative velocity analysis, while the high‐resolution data constrain the three‐dimensional geometry of the hydrate accumulation. The sediments in Urutī Basin dip landward due to ongoing deformation of the accretionary wedge. These strata are clearly imaged in seismic data where they cross a distinct bottom simulating reflection (BSR) that dips counterintuitively in the opposite direction to the regional dip of the seafloor. BSR‐derived heat flow estimates reveal a distinct heat flow anomaly that coincides spatially with the upper extent of a landward‐verging thrust fault. We present a conceptual model of this gas hydrate system that highlights the roles of fault‐controlled fluid flow at depth merging into strata‐controlled fluid flow into the hydrate stability zone. The result is a layer‐constrained accumulation of concentrated gas hydrate in the dipping strata. Our study provides new insight into the interplay between deep faulting, fluid flow and gas hydrate formation within an active accretionary margin. Plain Language Summary: Gas hydrates are ice‐like substances in which natural gas molecules are trapped in a cage of water molecules. They exist where the pressure is high, temperature is cold, and enough methane is present. These conditions exist in the marine environment at water depths greater than 300–500 m near sediment‐rich continental margins and in polar regions. It is important to study gas hydrates because they represent a significant part of the Earth's carbon budget and influence the flow of methane into the oceans and atmosphere. In this study, we use the seismic reflection method to generate images of gas‐hydrate‐bearing marine sediments east of New Zealand. Our data reveal an intriguing relationship between deep‐sourced fluid flow upward along a tectonic fault, and shallower flow through dipping sediments. This complex fluid flow pattern has led to disruption of the gas hydrate system and the formation of concentrated gas hydrate deposits within the dipping sediments. Our study highlights the relationships between relatively deep tectonic processes (faulting and fluid flow) and the shallow process of gas hydrate formation in an active subduction zone. Key Points: A distinct gas‐hydrate to free‐gas transition is mapped using high‐ and low‐frequency seismic dataGas and hydrate accumulations in the Urutī Basin are controlled by the structural setting, ongoing deep‐sourced fluid flow, and near surface stratigraphyRegions of high modeled heat flow can be directly related to accumulations of gas and gas hydrates [ABSTRACT FROM AUTHOR]

The exploration and development potential of shale gas reservoirs in the Sichuan Basin is enormous; however, it also faces difficulties such as complex structures, strong heterogeneity, and unclear geophysical response characteristics. Fine prediction of geostress is an important part of shale gas exploration and development, which directly affects the implementation effect of reservoir evaluation, well trajectory design, and fracture reconstruction. The existing geostress prediction techniques lack high-precision seismic data constraints, making it difficult to accurately reflect the planar distribution characteristics of geostress in the block with rapid changes in complex tectonic zones. At the same time, the geophysical response characteristics of geostress in the Sichuan Basin are unknown, and the geostress seismic prediction technology lacks theoretical basis. This paper combines numerical simulation and physical experiments and defines the characteristics of the geophysical response of shale gas reservoirs in the Sichuan Basin changing with the stress field, and technical countermeasures for geostress seismic prediction have been established to provide technical means for accurate prediction of the geostress field in the shale gas block. Based on the geostress sensitive parameters obtained from prestack seismic inversion, the geostress field prediction of a shale gas work area in the Sichuan Basin is realized. [ABSTRACT FROM AUTHOR]

Reservoir characterization entails mapping properties such as lithology, fluid type, porosity and permeability, to describe reservoir quality and identify areas for the location of new wells for optimum production. In this study, we test the reliability of rock-physics modelling and model-based post-stack seismic inversion for characterizing reservoir rocks located within the 'Rhoda' field, onshore Niger Delta Basin, Nigeria. The aim is to accurately discriminate lithology and estimate reservoir properties for accurate prediction of new prospective areas within the field of interest. Unlike some conventional inversion and geomodelling algorithms, which are probabilistic, model-based post-stack seismic inversion is favoured for its sensitivity in resolving thin-bed tuning for increased stratigraphic resolution and reservoir property prediction. More so, it gives satisfactory results, even with limited well control and fair to poor seismic quality, as will be shown in this study. On the basis of gamma-ray and resistivity log signatures, two reservoir sands, dubbed D6000 and D7000, were identified in the field of study. Along the eastern direction, well correlation revealed a significant net sand reduction. Within the reservoirs of interest, petrophysical parameters such as water saturation (Sw), porosity (Ø), and shale volume (Vsh) were evaluated. With high Ø and low Sw and Vsh values, these reservoirs were classified as good quality reservoirs. Crossplots of well-log properties such as density vs acoustic impedance, velocity ratio vs acoustic impedance, gamma-ray vs density, and gamma-ray vs acoustic impedance showed lithologies and pore fluids such as hydrocarbon sand, brine-sand, and shale. The model-based post-stack seismic inversion results revealed a lateral distribution of hydrocarbon and brine sands characterized by low and moderate acoustic impedance values, respectively. The low values of acoustic impedance slice and seismic horizon attributes (instantaneous phase and frequency) identified away from the drilled locations imply the presence of hydrocarbon-charged sands, which are potential prospects for future exploitation. This study therefore, shows the effectiveness of rockphysics-based modelling and model-based post-stack seismic inversion in the characterization of reservoir sands within the Niger Delta Basin and can be applied in other provinces around the world. [ABSTRACT FROM AUTHOR]

Porosity is one of the most important petrophysical parameters, studied in the subject of reservoir characterization. Determining porosity and how it changes in hydrocarbon reservoirs is an important issue that has been addressed in various researches. In this research, Poro-Acoustic Impedance (PAI) is introduced as an extended form of Acoustic Impedance (AI). The difference between PAI and AI is related porosity that is directly involved in the PAI. The inclusion of porosity data in the PAI formula made porosity effective in forward modeling and inversion of seismic data. The use of PAI in the forward modeling of synthetic models increases the contrast between the subsurface layers, and the contrast increases twice as compared to the AI. Band Limited Recursive Inversion (BLRI) algorithm is used for inversion of synthetic seismograms and model-based algorithm is used for real seismic data inversion. For real data, due to the existence of well data, seismic horizons and geological information, using the basic model method for inversion is more accurate. The main difference between inversion using PAI and AI is that changes in porosity can be seen directly in the results of PAI inversion. The correlation of porosity with PAI and AI is -0.93 and -0.85, respectively, which shows that porosity has a stronger relationship with PAI. The use of PAI can be a quick and simple solution to understand porosity changes in hydrocarbon reservoirs and increase the accuracy of porosity determination in reservoirs to a great extent. [ABSTRACT FROM AUTHOR]

Understanding the near-surface structure of the Earth requires accurate prediction of physical properties of the subsurface, such as velocity estimated from tomographic inversion of seismic refraction data. The predicted velocity values are often uncertain due to epistemic uncertainty in the inversion process (i.e., imperfectly known underlying physics) and aleatoric variability in the data (i.e., inherent noise in observations). Although seismic refraction is widely used in near-surface applications, the associated uncertainty is rarely quantified and presented alongside the inverted velocity tomograms. In this study, the effect of epistemic uncertainty due to local variability in the initial model and aleatoric variability due to first-arrival picking error on the velocity prediction uncertainty are investigated. A stochastic framework is implemented based on a statistical approach where multiple realizations of stochastically perturbed initial models and travel time picks are generated and the uncertainty in the predicted velocity models is quantified. The two sources of uncertainty are first studied independently and then the combined effect is investigated. The results show that both sources affect the posterior uncertainty, but the uncertainty in the initial model has a greater effect than picking error on the uncertainty of the posterior velocity model. In addition, joint analysis of both sources of uncertainty shows that the uncertainty in the inverted model depends on predicted velocity values, depths, velocity gradients and ray coverages. [ABSTRACT FROM AUTHOR]

Abstract Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach, such as differential evolution (DE) and particle swarm optimization (PSO). Based on the well logs, the sparse probability distribution (PD) of the reflectivity distribution is spatial stationarity. Therefore, we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity, which helps to provide more accurate potential solutions for the seismic inversion. In the proposed scheme, as two key operations, the creating of probability density function library and probability transformation are inserted into standard SI algorithms. In particular, two targeted DE-PD and PSO-PD algorithms are implemented. Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO. In particular, the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion. Overall, the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

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Estimation of reservoir properties from seismic data suffers from non-unique solutions. A workflow based on the numerical reformulation of a laboratory-based rock physics model may reduce the non-uniqueness. This study attempts to integrate seismic and well log data of relatively unexplored parts of Upper Assam (UA) basin using inversion driven by laboratory based rock physics model. The laboratory-based rock physics model was developed based on experimental measurements conducted on rock cores from Tipam and Barail formations of the basin. Seismic inversion analysis was performed in OpendTect, an open-source software on post-stack seismic data to derive the acoustic impedance (AI) using coloured inversion. A multilayered feed-forward neural network was developed to spatially populate different petrophysical properties. Laboratory-based correlations between AI, density, porosity were utilised for the AI model from which velocity was computed using multivariate rock physics equation. This derived velocity value was transformed to AI and subsequently trained with well log to populate density (2.23-2.73 gm/cc) and porosity (7-28%) for the entire survey area. A reasonable to high correlation is obtained between bulk density and porosity derived by NN using well log and that derived by laboratory-based model (r = 0.78, 0.91 for Barail and 0.95, 0.94 for Sylhet formation). Thus, integrating datasets of different scale from seismic to core with well log data using neural network helps to derive more realistic models that helps in quantitative decision analysis. [ABSTRACT FROM AUTHOR]

Seismic inversion is one of the fundamental techniques for solving geophysics problems. To obtain the elastic parameters or petrophysical parameters, it is necessary to establish a direct or indirect mapping that is usually nonlinear between the observed data and the inversion parameters in seismic inversion. The traditional model-based inversion method, which is calculating complex and expensive, relies on strict physics theories. Neural networks are an excellent artificial intelligence method for establishing nonlinear mapping. Theoretically, any linear and nonlinear functions can be fitted with neural networks. Compared with model-based inversion methods, the inversion efficiency and accuracy can be improved remarkably using neural networks with their powerful ability to discover and extract features from big data. By reviewing the application of fully-connected neural networks, probabilistic neural networks, convolutional neural networks, recurrent neural networks, generative neural networks, and physics-based neural networks in seismic inversion, we provide a comprehensive overview of neural network methods to seismic inversion, including the basic principles of different neural networks, types of seismic inversion, seismic datasets, and the general framework of neural networks for seismic inversion. In addition, the future trends of seismic inversion based on neural networks are also discussed, including the application of image segmentation networks and generative adversarial networks in seismic inversion. [ABSTRACT FROM AUTHOR]

Despite the extensive application of artificial neural networks in seismic inversion, their effectiveness is often hampered by the limited availability of labeled data. To address this challenge, we introduce a novel method for seismic impedance inversion. Our approach integrates a physics-driven cycle network with a conditional generative adversarial network (CGAN) and a convolutional model. Employing seismic data as the input, the CGAN capitalizes on inherent information to minimize non-uniqueness during inversion. Furthermore, the convolutional model, acting as a physics-informed operator, reverts the derived impedance data back to seismic form, enabling simultaneous training of neural networks with labeled and unlabeled data, fulfilling the seismic-to-seismic cycle. The proposed method is demonstrated to be effective on tests using both theoretical models and field data. [ABSTRACT FROM AUTHOR]

Environmental problems associated with depleted oil and gas reservoirs upon long-term production from them are likely to become important challenges in future decades. With the increasing trend of production from hydrocarbon reservoirs, more and more reservoirs across the world are reaching the second half of their life—a fact that places an emphasis on the necessity of investigating what is known as reservoir subsidence. Different analytical and numerical approaches have been introduced for analyzing the subsidence on the basis of the elasticity theory but in the form of case studies, leaving a comprehensive model yet to be proposed. In this work, a formulation was introduced for estimating reservoir subsidence by integrating the rock physics, rock mechanics, and thermo-poroelasticity theories. Then, a modified version of this formulation was developed to calculate compaction in an actively producing reservoir that is suspect of subsidence, as a case study. For this purpose, triaxial hydrostatic tests were carried out on core plugs obtained from the considered reservoir, and then, compaction parameters (i.e., compression index and coefficient of deformation) were obtained at a laboratory scale. In order to evaluate the subsidence at a reservoir scale, the laboratory-scale results and in situ reservoir properties were integrated with well-logging and 3D seismic data at well location to come up with 3D cubes of compaction information. Continuing with the research, time-dependent inelastic deformation was modeled considering continued production for different future periods. The field observations showed that the estimated compaction is not visible at the surface in the form of subsidence due to the high depth and stiffness of the studied reservoir. However, collapse of casing at some of wells drilled into the studied reservoir could be attributed to the reservoir subsidence. Finally, variations of compaction with pore pressure were investigated to propose a model for predicting the subsidence in future periods. Findings of this research can be used to forecast subsidence at well location to take the required measures for avoiding possible casing collapse and/or relevant environmental issues. [ABSTRACT FROM AUTHOR]

Tight sandstones produce an increasing amount of natural gas worldwide. Apart from identifying the gas enrichment, the predictions of lithology and permeable zones are crucial for the prediction of tight gas sandstones. In the present study, a seismic inversion method is developed based on rock physical modeling, by which it is possible to directly predict the lithology and pore structure in tight formations. The double-porosity model is used as a modeling tool in considering complex pore structures. Based on the model, the microfracture porosity is then predicted using logging data, which are used as a factor to estimate microfractures. Parameters representing the lithology and pore structure are proposed and estimated using logging data analyses and rock physical modeling based on the framework of the Poisson impedance. Thereafter, a new AVO equation is established and extended to the form of an elastic impedance for a direct prediction of the lithology and pore structure parameters. Real data applications show that the indicators of lithology and permeable zones are consistent with the production status. They agree with the petrophysical properties measured in wellbores, thereby proving the applicability of the proposed method for the effective characterization of tight gas sandstones. [ABSTRACT FROM AUTHOR]

The rock mechanical properties influence the selection of drilling parameters, optimized drilling trajectory, and appropriate hydraulic fracturing intervals. Estimating the 3-D spatial distribution of these geomechanical properties at the reservoir scale is a challenging task, especially in the case of limited data only at the well locations. Advanced geostatistical techniques can be utilized to represent a reservoir's inherent spatial variation more realistically. In this study, we investigate the spatial variability of rock mechanical properties, including Young's modulus, shear modulus, bulk modulus, and Poisson's ratio, as major constituents of the reservoir geomechanical model. The data are extracted from a hydrocarbon field located southwest of Iran and consist of forty wells. We first build a 1-D model of rock elastic moduli at each well by integrating petrophysical and core-based laboratory measurements and then develop a corresponding 3-D model using geostatistical simulation techniques. Thereafter, 3-D seismic data are employed to optimize the geomechanical model. Results show that the integration of well logs with seismic data increases the accuracy of field-wise 3-D elastic moduli models. Furthermore, we used various co-simulation techniques to demonstrate the improving effect of complementary data in constructing a more realistic reservoir geomechanical model. [ABSTRACT FROM AUTHOR]

Reservoir prediction and prospectivity are performance indices of the oil and gas field development planning and reserves estimation which depicts the behaviour of the reservoir in the future; its success is dependent on accurate description of the reservoir rock properties, fluid properties, rock-fluid properties and flow. Hence, the objective of this paper was to investigate and predict the reservoir and prospectivity of Omos Field, Onshore Niger Delta Basin, by integrating appropriate standard methods. Results obtained from rock physics attribute map analysis revealed very high Mu-Rho values between 13.0-16.2 Gpa, that is expected for sand presence, and this agrees with the good quality reservoir sands predicted from sequence stratigraphy study.Lambda-Rho values obtained ranges between - 1 to 9.5 GPa, with a much lower value between -1 to 4.3 in the prospective zone that indicates the presence of hydrocarbon bearing sand. These results agree with the results of structural and amplitude maps analysis. Hence the prospective reservoir has a very good hydrocarbon potential in this zone and should be a target for further appraisal for development and production purposes.

Abstract The Hikurangi Margin east of New Zealand's North Island hosts an extensive gas hydrate province with numerous gas hydrate accumulations related to the faulted structure of the accretionary wedge. One such hydrate feature occurs in a small perched upper‐slope basin known as Urutī Basin. We investigated this hydrate accumulation by combining a long‐offset seismic line (10‐km‐long receiver array) with a grid of high‐resolution seismic lines acquired with a 600‐m‐long hydrophone streamer. The long‐offset data enable quantitative velocity analysis, while the high‐resolution data constrain the three‐dimensional geometry of the hydrate accumulation. The sediments in Urutī Basin dip landward due to ongoing deformation of the accretionary wedge. These strata are clearly imaged in seismic data where they cross a distinct bottom simulating reflection (BSR) that dips counterintuitively in the opposite direction to the regional dip of the seafloor. BSR‐derived heat flow estimates reveal a distinct heat flow anomaly that coincides spatially with the upper extent of a landward‐verging thrust fault. We present a conceptual model of this gas hydrate system that highlights the roles of fault‐controlled fluid flow at depth merging into strata‐controlled fluid flow into the hydrate stability zone. The result is a layer‐constrained accumulation of concentrated gas hydrate in the dipping strata. Our study provides new insight into the interplay between deep faulting, fluid flow and gas hydrate formation within an active accretionary margin.

The exploration and development potential of shale gas reservoirs in the Sichuan Basin is enormous; however, it also faces difficulties such as complex structures, strong heterogeneity, and unclear geophysical response characteristics. Fine prediction of geostress is an important part of shale gas exploration and development, which directly affects the implementation effect of reservoir evaluation, well trajectory design, and fracture reconstruction. The existing geostress prediction techniques lack high-precision seismic data constraints, making it difficult to accurately reflect the planar distribution characteristics of geostress in the block with rapid changes in complex tectonic zones. At the same time, the geophysical response characteristics of geostress in the Sichuan Basin are unknown, and the geostress seismic prediction technology lacks theoretical basis. This paper combines numerical simulation and physical experiments and defines the characteristics of the geophysical response of shale gas reservoirs in the Sichuan Basin changing with the stress field, and technical countermeasures for geostress seismic prediction have been established to provide technical means for accurate prediction of the geostress field in the shale gas block. Based on the geostress sensitive parameters obtained from prestack seismic inversion, the geostress field prediction of a shale gas work area in the Sichuan Basin is realized.

Abstract Environmental problems associated with depleted oil and gas reservoirs upon long-term production from them are likely to become important challenges in future decades. With the increasing trend of production from hydrocarbon reservoirs, more and more reservoirs across the world are reaching the second half of their life—a fact that places an emphasis on the necessity of investigating what is known as reservoir subsidence. Different analytical and numerical approaches have been introduced for analyzing the subsidence on the basis of the elasticity theory but in the form of case studies, leaving a comprehensive model yet to be proposed. In this work, a formulation was introduced for estimating reservoir subsidence by integrating the rock physics, rock mechanics, and thermo-poroelasticity theories. Then, a modified version of this formulation was developed to calculate compaction in an actively producing reservoir that is suspect of subsidence, as a case study. For this purpose, triaxial hydrostatic tests were carried out on core plugs obtained from the considered reservoir, and then, compaction parameters (i.e., compression index and coefficient of deformation) were obtained at a laboratory scale. In order to evaluate the subsidence at a reservoir scale, the laboratory-scale results and in situ reservoir properties were integrated with well-logging and 3D seismic data at well location to come up with 3D cubes of compaction information. Continuing with the research, time-dependent inelastic deformation was modeled considering continued production for different future periods. The field observations showed that the estimated compaction is not visible at the surface in the form of subsidence due to the high depth and stiffness of the studied reservoir. However, collapse of casing at some of wells drilled into the studied reservoir could be attributed to the reservoir subsidence. Finally, variations of compaction with pore pressure were investigated to propose a model for predicting the subsidence in future periods. Findings of this research can be used to forecast subsidence at well location to take the required measures for avoiding possible casing collapse and/or relevant environmental issues.

On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute and a half later. The SFB is known for its blind thrust faults, typically not associated with surface ruptures. These earthquakes are usually linked to the middle and lower layers of the sedimentary cover. Identifying the faults that trigger earthquakes in the region remains a significant challenge and is subject to high uncertainty. This study aims to identify and determine the fault(s) that may have caused the doublet earthquake. To achieve this goal, we utilized the DInSAR method using Sentinel-1 to detect deformation, followed by finite-fault inversion and magnetic interpretation to determine the location, geometry, and slip distribution of the fault(s). Bayesian probabilistic joint inversion was used to model the earthquake sources and derive the geometric parameters of potential fault planes. The study presents two potential fault solutions—one dipping to the north and the other to the south. Both solutions showed no significant difference in strike and fault location, suggesting a single fault. Based on the results of the seismic inversion, it appears that a north-dipping fault with a strike, dip, and rake of 257°, 74°, and 77°, respectively, is more consistent with the geological setting of the area. The fault plane has a width of roughly 3.6 km, a length of 13.4 km, and a depth of 5.6 km. Our results revealed maximum displacements along the radar line of sight reaching values of up to −360 mm in the ascending orbit, indicating an unknown fault with horizontal displacements at the surface ranging from −144 to 170 mm and maximum vertical displacements between −204 and 415 mm. Aeromagnetic data for Iran were utilized with an average flight-line spacing of 7.5 km. The middle of the data observation period was considered to apply the RTP filter, and the DRTP method was used. We calculated the gradient of the residual anomaly in the N-S direction due to the direction of the existing faults and folds. The gradient map identified the fault and potential extension of the observed anomalies related to a fault with an ENE-WSW strike, which could extend to the ~ E-W. We suggest that earthquakes occur in the sedimentary cover of the SFB where subsurface faulting is involved, with Hormuz salt acting as an important barrier to rupture. The multidisciplinary approach used in this study, including InSAR and magnetic data, underscores the importance of accurate fault characterization. These findings provide valuable insights into the seismic hazard of the area.