Browsing by Author "Raghav Singh"

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A flowchart for porosity and acoustic impedance mapping using seismic inversion with semi hybrid optimization combining simulated annealing and pattern search techniques
(Springer Science and Business Media B.V., 2024) Raghav Singh; S.P. Maurya; Brijesh Kumar; Nitin Verma; Alok Kumar Tiwari; Ravikant Tiwari; G. Hema; Ajay P. Singh
Porosity and acoustic impedance are important in the study of subsurface properties of rocks and soil. Porosity is influenced by the type of minerals, and fluids, and their distribution within the subsurface material. Acoustic impedance is a key parameter in seismic inversion because it governs the reflection and transmission of seismic waves at interfaces between different rock layers. Mapping porosity and acoustic impedance using seismic inversion poses several challenges such as low resolution, longer convergence times compared to other optimization techniques, and handling large datasets. To address these challenges, our current study has employed a semi-hybrid optimization approach by incorporating a pattern search (PS) method into the globally recognized simulated annealing (SA) technique. In our devised methodology, seismic data is meticulously inverted, trace by trace, initially utilizing the simulated annealing process and subsequently integrating the pattern search which further reduces computational Complexity. The output from SA serves as the foundation for the PS optimization, preventing it from getting trapped in local minima or maxima. To evaluate the algorithm, we initiated a systematic analysis using synthetic data. The hybrid optimization method performed well, yielding highly accurate inversion results with a remarkable high resolution and correlation between original and inverted impedance. We then applied this approach to actual seismic reflection data from the Blackfoot field in Alberta, Canada. Notably, the inversion identified a sand channel between 1055 and 1070 ms two-way travel time, characterized by low impedance and high porosity, suggesting the potential presence of hydrocarbon reservoirs. The level of performance demonstrated in this context may not be anticipated when utilizing SA or PS optimization alone. Hence, the newly devised semi-hybrid optimization approach emerges as a highly recommended solution, offering the potential to address the constraints of individual optimization methods and deliver thorough subsurface insights. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Application of maximum likelihood and model-based seismic inversion techniques: a case study from K-G basin, India
(Springer Science and Business Media Deutschland GmbH, 2022) Richa; S.P. Maurya; Kumar H. Singh; Raghav Singh; Rohtash Kumar; Prabodh Kumar Kushwaha
Seismic inversion is a geophysical technique used to estimate subsurface rock properties from seismic reflection data. Seismic data has band-limited nature and contains generally 10–80 Hz frequency hence seismic inversion combines well log information along with seismic data to extract high-resolution subsurface acoustic impedance which contains low as well as high frequencies. This rock property is used to extract qualitative as well as quantitative information of subsurface that can be analyzed to enhance geological as well as geophysical interpretation. The interpretations of extracted properties are more meaningful and provide more detailed information of the subsurface as compared to the traditional seismic data interpretation. The present study focused on the analysis of well log data as well as seismic data of the KG basin to find the prospective zone. Petrophysical parameters such as effective porosity, water saturation, hydrocarbon saturation, and several other parameters were calculated using the available well log data. Low Gamma-ray value, high resistivity, and cross-over between neutron and density logs indicated the presence of gas-bearing zones in the KG basin. Three main hydrocarbon-bearing zones are identified with an average Gamma-ray value of 50 API units at the depth range of (1918–1960 m), 58 API units (2116–2136 m), and 66 API units (2221–2245 m). The average resistivity is found to be 17 Ohm-m, 10 Ohm-m, and 12 Ohm-m and average porosity is 15%, 15%, and 14% of zone 1, zone 2, and zone 3 respectively. The analysis of petrophysical parameters and different cross-plots showed that the reservoir rock is of sandstone with shale as a seal rock. On the other hand, two types of seismic inversion namely Maximum Likelihood and Model-based seismic inversion are used to estimate subsurface acoustic impedance. The inverted section is interpreted as two anomalous zones with very low impedance ranging from 1800 m/s*g/cc to 6000 m/s*g/cc which is quite low and indicates the presence of loose formation. © 2021, The Author(s).
Comparison of neural networks techniques to predict subsurface parameters based on seismic inversion: a machine learning approach
(Springer Science and Business Media Deutschland GmbH, 2024) Nitin Verma; S.P. Maurya; Ravi kant; K.H. Singh; Raghav Singh; A.P. Singh; G. Hema; M.K. Srivastava; Alok K. Tiwari; P.K. Kushwaha; Richa Singh
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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Coulomb Stress Change of the 2012 Indian Ocean Doublet Earthquake
(Springer International Publishing, 2024) Pankhudi Thakur; Rohtash Kumar; Ranjit Das; Amritansh Rai; Raghav Singh; Ankit Singh; S.P. Maurya
The 2012 Sumatra (Mw 8.6) earthquake, which falls into the largest and rarest group of the great intraplate earthquakes, continues to awe many brilliant minds. An enormous aftershock (Mw 8.2) was felt two hours after the Indian Ocean earthquake along the triple intersection of the Indian, Australian, and Sunda plates in the northwest. Over the past 20 years, there have been numerous earthquakes in the Sumatran subduction zone, including the 2004 earthquake (Mw 9.2) of Sumatra-Andaman, the 2005 earthquake (Mw 8.6) of Nias-Simeulue, the 2007 earthquake (Mw 8.4) of Bengkulu, the 2010 earthquake (Mw 7.8) of Mentawai tsunami, and a large number of other minor to moderate-sized events. It often takes a few seconds to a few minutes for the stress brought on by an earthquake to dissipate. This massive discharge disrupts the lithosphere and asthenosphere, which causes more earthquakes to occur nearby. A comprehensive comprehension of stress variations along a fault and its neighboring faults is essential for effectiv ly predicting and mitigating seismic risks. Drawing inspiration from the earthquake finite fault model pioneered by Guangfu Shao, Xiangyu Li, and Chen Ji from UCSB, we have formulated Coulomb stress models tailored to the Sumatran subduction zone and the Sumatran fault. It was discovered that the primary shock’s related coulomb stress change exceeded the stress-triggering threshold. The aftershock struck a place where there was a lot of stress from the mainshock. Therefore, the Coulomb failure stress change brought on by the mainshock is likely what caused the Sumatra aftershock to occur. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Data-Driven Spatiotemporal Assessment of Seismicity in the Philippine Region
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; S.P. Maurya; Ankit Singh; Pankhudi Thakur; Raghav Singh; Ranjit Das
The present study provides a retrospective analysis of the geographical and chronological fluctuations of three basic statistical characteristics of seismicity using a big dataset of events that occurred between 1940 and 2022 in the Philippine region. For determining the spatial-time changes in a-value (seismic activity), b-value (recurrence graph slope), and z-value, the contemporary expanded software package ZMAP with numerous sophisticated seismological functions for earthquake catalog analysis is employed (parameter of the relative seismic quiescence). For the various statistical interpretations, catalog data from the United States Geological Survey (USGS) occurred in spatial windows 0° N – 20° N and 118°E – 130oN are used. The overall conclusion is that unusually low b-values and high z-values, which define zones of comparatively seismic quiescence, may be a signal of the approaching release of more severe stress in areas near zones of relatively high a-values. Thus, the suggested joint interpretation of the spatial-time fluctuations of these three statistical characteristics of seismicity may be seen as a form of the predictor of the more powerful recent seismic occurrences in the region. Furthermore, the occurrence probability of a seven magnitude event is near about one with a return period of 2 years. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Determination and Identification of Focal Mechanism Solutions for the 2016 Kumamoto Earthquake from Waveform Inversion Using ISOLA Software
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; Shatrughan Singh; Raghav Singh; Satya Prakash; Pnkhudi Thakur
On April 14, 2016, at 21:26 (JST), a shallow earthquake of magnitude MJMA 6.5 occurred at Kumamoto prefecture in Kyushu region, Japan. It was one of the foreshocks before the main shock of magnitude MJMA 7.3 28 hours later, on April 16 hits the area. They began at the Hinagu fault zone, which intersects the main shock Futagawa fault zone, creating a complex tectonic context for this earthquake sequence. Here we find out the focal mechanism solution for the MJMA 6.5 earthquake. A software package ISOLA-GUI, a user-friendly MATLAB-based interface, developed by J. Zahradnik and E. Sokos, is used to find moment tensor solutions. It uses data from the three-component seismogram from a single station. The earthquake data is taken from strong motion records of the KiK-net networks. Full-wave seismogram data are used for inversion for single or multiple-point source earthquake models. This software is useful to find the moment tensor solutions where data at all azimuths are not available. The software uses a graphical interface that helps the user easily interact with the software and makes it easier to work. The software allows the result to be mapped using GMT. The focal mechanism solution from multi-station data has been shown along with a cross-plot of source depth and correlation. The power and SNR spectrum are also shown to assess the quality of the data. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Enhancement of CO2 monitoring in the sleipner field (north sea) using seismic inversion based on simulated annealing of time-lapse seismic data
(Elsevier Ltd, 2024) G. Hema; S.P. Maurya; Ravi Kant; Ajay P. Singh; Nitin Verma; Raghav Singh; K.H. Singh
The primary aim of this research is to enhance seismic data interpretation and CO2 monitoring by utilizing seismic inversion techniques based on the simulated annealing method. Simulated annealing is a global optimization technique employed for inverting seismic data and provides better results as compared with local optimization-based inversion. This methodology is implemented in the Utsira Formation, located at a depth of 1000 m within the Sleipner Field, Norway. The study encompasses the analysis of three sets of time-lapse seismic data, first from 1994 (pre-injection), followed by surveys in 1999 and 2001, corresponding to the injection of 2.35 million tonnes and 4.26 million tonnes of CO2, respectively. Firstly, synthetic data is used to check the reliability of the algorithm followed by real data application. This process starts by performing the inversion analysis on the synthetic data which shows a decrease in the impedance values observed at the injection site whereas the seismic amplitude increases. The qualitative as well as quantitative analysis depicts that the algorithm works satisfactorily. The same process is applied to the real data from the Sleipner field. Acoustic impedances are calculated using a simulated annealing-based inversion scheme for the pre-injection case in 1994 and post-injection scenarios in 1999 and 2001. Because of the presence of injected CO2 in the years 1999 and 2001, a low impedance zone that ranged from 2000 m/s*g/cc to 2400 m/s*g/cc appeared at the time interval of 0.85–1.10sec. The interpretation of the inverted impedance section and seismic attribute analysis show no signature of CO2 leakage. The results indicated that the inverted section which is derived from the SA optimization technique shows very clear CO2 information offering a more realistic representation with enhanced resolution of the CO2 plume and its migratory paths. © 2024 Elsevier Ltd
Estimation of Petrophysical Properties Using Linear Programming Sparse Spike Inversion and Deep Feed-Forward Neural Network Techniques Over F3 Block, Netherlands: A Case Study
(Birkhauser, 2024) Raghav Singh; Prabodh Kumar Kushwaha; S.P. Maurya; Piyush Rai
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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Exploring GRACE and GPS and Absolute Gravity Data on the Relationship Between Hydrological Changes and Vertical Crustal Deformation in South India
(Springer International Publishing, 2024) Ankit Singh; Rohtash Kumar; Amritansh Rai; Raghav Singh; S.P. Singh
Rainfall during the monsoon across the Indian subcontinent leads to significant hydrological changes that alter the ground over a range of timescales. In the present study, both GPS and Grace data have been analysed to assess the impact of groundwater variability on ground deformation study region. The amplitudes seasonal vertical deformation phases estimated from Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) are reliable, demonstrating that hydrological factors are the main contributors of periodic deformation in the region. We compare the deformation computed from GRACE mass signal with that of altitude variations from non-stop GPS data from three locations, viz. Bangalore, Hyderabad and Lucknow. For the specified period of time, the patterns of the GRACE and GPS plots match well for Hyderabad and Bangalore but not so well for Lucknow. This shows that hydrological loading is mostly to blame for the crustal deformation seen by GPS in Hyderabad and Lucknow. The crustal d formation as determined by the GPS in Lucknow is consistent with that derived from GRACE data over Lucknow to some extent. This shows that GRACE may be used to adjust the deformation detected by GPS for the hydrological effect. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Exploring the Concept of Self-Similarity and High-Frequency Decay Kappa-Model and fmax-Model Using Strong-Motion Surface and Borehole Data of Japan: A Statistical Approach
(Springer International Publishing, 2024) Rohtash Kumar; Raghav Singh; Amritansh Rai; Sandeep; S.P. Singh; S.P. Maurya; Prashant Kumar Singh
We statistically analyzed the fmax-model, κ-model and stress drop (Δσ) using surface and borehole data of the KIK-NET Japan seismological network. The statistical tests show no contribution of source in the fmax-model and κ-model. The ‘fmax’ values obtained in the present study are 4.2–11.0 Hz and 5–11.0 Hz for surface and borehole data, respectively. The impact of local heterogeneities and wave propagation path is clearly visible on both surface and borehole fmax-models. The same is confirmed by the p-value ‘t-test’. The multivariate linear regression (MVLR) has been applied for the analysis of dependent variables ‘κ(s)’ and ‘κ(w)’ w.r.t. independent variables epicentral distance and magnitude. The p-value calculated by t-test indicates the strong dependence of κ(s) and κ(w) on near-surface geology and the physical state of the wave travelling media but almost no contribution of magnitude. The contribution of near-surface geology in kappa values is also confirmed by the ‘κ0’ (kappa at epicentral distance = 0). The relationships between the fmax-model and the κ-model have been developed for the study region. The stress drop (Δσ(s)) assessed from surface data is 44.16-65.86 bars with an average value of 53.19 bars and borehole derived stress drop (Δσ(w)) is 46.38-68.13 bars with an average value of 54.16 bars. This study discards the effect of depth; type of earthquake, i.e. normal, reverse and strike-slip; and signal to noise ratio (SNR) on stress drop as there is no huge variation in both Δσ(s) and Δσ(w) with the seismic moment and source radius. Therefore, the study supports the concept of self-similarity. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis
(Elsevier Ltd, 2024) Ravi Kant; Brijesh Kumar; S.P. Maurya; Raghav Singh; Anoop Kumar Tiwari
The present study integrates various local and global optimization techniques together to estimate subsurface properties from post-stack seismic data and compare their efficacy qualitatively and quantitatively. Specifically, a local gradient-based optimization method, the quasi-newton method (QNM), is combined with global techniques such as simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO). These are well-established methods in geophysics. The research compares three global optimization methods (SA, GA, and PSO), their hybrid variants, and QNM for estimating subsurface acoustic impedance. The goal is to assess the trade-offs between solution accuracy and convergence efficiency, offering insights into the strengths and weaknesses of each approach. The objective is to guide the selection of the most effective optimization technique for seismic inversion, balancing quality and computational performance. Both synthetic and real seismic datasets are used to validate the proposed methodology, demonstrating its robust performance across various geological scenarios. Comparative analyses with single global inversion approaches reveal that hybrid optimization methods offer greater accuracy and reliability, positioning them as versatile tools for subsurface characterization. The results indicate that while the hybrid PSO method does not provide significant improvements over single PSO, it extends the convergence time. On the other hand, SA and GA perform adequately, but their hybrid versions considerably enhance solution quality at the cost of longer convergence times. Among the methods, SA shows the fastest convergence to the global solution, followed by GA and PSO. Hybrid SA stands out, delivering superior resolution and faster convergence compared to hybrid PSO and GA. © 2024
Identification of the reservoir using seismic inversion based on particle swarm optimization method: A case study
(Springer, 2024) Ravi Kant; Brijesh Kumar; S.P. Maurya; Nitin Verma; Ajay P Singh; G. Hema; Raghav Singh; K.H. Singh; Piyush Sarkar
Accurate reservoir characterization is a crucial step for developing, managing, and optimizing hydrocarbon production. In this current study, we employ particle swarm optimization techniques (PSO) to perform an inversion of post-stack seismic data, extracting information about subsurface acoustic impedance and porosity. Conventional seismic inversion methods predominantly employ local optimization strategies, which often rely on the availability of initial models, particularly in unfamiliar geological settings. In contrast, our approach is based on global search principles, consistently striving to converge towards a global optimal solution, independent of the initial model. To validate the developed technique, we initially subjected it to synthetic data and a wedge model, followed by its application to real data from the Blackfoot field in Canada. The investigation reveals that the inverted results, both for the synthetic and real data, closely align with the observed data. Statistical analysis indicates a high correlation of 0.99 for the synthetic data. For the real data, the correlation remains strong at 0.89. Finally, the PSO-based inversion algorithm is applied across the entire seismic volume, successfully yielding high-resolution subsurface information. This inversion reveals impedance variations ranging from 6500 to 13000 m/s*g/cc, with porosity levels spanning 5–24%, within the Blackfoot region. As per the findings of the investigation, it is evident that the upper section of the subsurface mainly comprises non-solid rock materials. The examination of inverted sections has disclosed an atypical region characterized by low impedance (<9000 m/s*g/cc) and remarkably high porosity (>18%) within the time interval of 1040–1060 ms two-way travel time. This distinctive zone is corroborated by well-log analysis at the same depth and is categorized as a reservoir. © Indian Academy of Sciences 2024.
Implementing 4D seismic inversion based on Linear Programming techniques for CO2 monitoring at the Sleipner field CCS site in the North Sea, Norway
(Springer Science and Business Media Deutschland GmbH, 2024) Ajay Pratap Singh; Satya Prakash Maurya; Ravi Kant; Kumar Hemant Singh; Raghav Singh; Manoj Kumar Srivastava; Gopal Hema; Nitin Verma
This article provides a comprehensive analysis of CO2 injection monitoring in the Sleipner Field. Ensuring the safe storage and containment of CO2 in geological formations or assigned storage sites, especially in the carbon capture and storage (CCS) projects. In this study, a seismic inversion method incorporating linear programming sparse spike inversion was employed to observe and analyze the CO2 plume in the Sleipner field, Norway. This approach enhances the understanding of the dynamics and behavior of the CO2 injection, providing valuable insights into the monitoring and assessment of CCS operations in the Sleipner field. The foundational dataset includes 3D post-stack seismic data from the year 1994, with special emphasis on the monitoring data collected in 1999, following four years of CO2 sequestration. The analysis utilized synthetic data to investigate alterations in seismic amplitude, highlighting that amplitude variations were more prominent compared to variations in velocity and density. The findings highlight noticeable shifts in P-wave velocity, signifying a significant 29% reduction, with the most substantial decrease occurring within the 0 to 30% CO2 saturation range. Correspondingly, density changes align with trace variations, demonstrating only a 2–3% reduction in density as gas saturation increases from 0 to 30%. Beyond 30% saturation, density exhibits a further decrease of 30%. The traces collectively reveal a consistent trend, showcasing a 32% reduction in impedance as CO2 saturation levels rise. Through the cross-equalization process, it was observed that the initial data repeatability was low, indicated by a normalized root mean square (NRMS) value of 0.6508. However, significant improvement was achieved, bringing the NRMS value to a more satisfactory level of 0.5581. This improvement underscored the alignment of features both above and below the reservoir, underscoring the efficacy of the cross-equalization technique. The outcomes of the 4D inversion provided insights into the distribution of CO2 within the reservoir, revealing upward migration. Importantly, the results confirmed the secure storage of CO2 within the reservoir, affirming the integrity of the overlying cap layer. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2024.
Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study
(Springer Science and Business Media B.V., 2024) Raghav Singh; Aditya Srivastava; Ravi Kant; S.P. Maurya; P. Mahadasu; Nitin Verma; G. Hema; P.K. Kushwaha; Richa; K.H. Singh; Ajay P. Singh; M.K. Srivastava; Piyush Sarkar
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. © 2024, The Author(s), under exclusive licence to Springer Nature B.V.
Lapse-Time Dependence of Coda Quality Factor Within the Lithosphere of Northern Ecuador
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; Ankit Singh; Raghav Singh; Indrajit Das; S.P. Maurya
In the current study, a frequency-dependent attenuation relationship based on coda waves is developed for the Ecuador region. The single backscattering model developed by Aki and Chouet (J Geophys Res 80:3322–3342, 1975) is used to investigate the dependence of Coda-Q on lapse time frames. The waveforms of 49 local earthquakes recorded by a one-station local seismological network are used for the analysis. The frequency-dependent Coda-Q relations estimated for the region are: Qc = (65.75) f0.6302 (10 sec lapse time), Qc = (92.48) f0.6868 (20 sec lapse time), Qc = (18.17) f1.3337 (30 sec lapse time), Qc = (30.95) f1.2303 (40 sec lapse time), Qc = (88.22) f0.9117, and Qc = (125.19) f0.9342. Given that greater areas would be sampled by longer lapse time windows, the rise in Qc values with lapse time demonstrates the depth dependency of Qc. The increase in value of Q0 (QC at 1 Hz) with time depicts that the heterogeneities decrease with depth. The observed quality factor is highly variable with the lapse time and frequency. The more significant value of n shows that the area is seismically active. The observed Qc relation is found equivalent to other similar seismically active regions. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Moho Mapping of Northern Chile Region Using Receiver Function Analysis and HK Stacking
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; Dipankan Srivastava; Raghav Singh; Ankit Singh; S.P. Maurya
In this study, Rfpy software is utilized to compute the receiver functions to map Moho in the Northern Chile region. To obtain the teleseismic waveforms within an epicentral distance of 30° to 90°, the Package makes use of the IRIS station database. The receiver functions over stations AC04 and AC05 indicate a low-velocity layer possible area at a shallower depth. Additionally, the delay periods of the Moho Ps phase at various different back azimuths are used to infer the anisotropy or dipping Moho. With the aid of H-K Stacking the receiver functions for Poisson’s ratio and Moho depth were also inverted. Under the AC07 station, which is situated in Caldera, Atacama, a higher Moho depth of 46 km is discovered. This results in a Poisson’s ratio of 0.24. Below the CO10 station, which is in Coquimbo, an extraordinary poisson ratio is found. Accordingly, a Moho depth of 25 km is discovered. Inverting the acquired P-wave receiver functions may effectively determine the S-wave velocity structure below each station. This would aid in enhancing the region’s crustal imaging. Additionally, harmonic decompositions of the receiver functions might be performed to examine the behavior of anisotropy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Moment tensor solutions of some regional events using 3-component single station waveform data
(Springer, 2022) Rohtash Kumar; Arjun Kumar; S.C. Gupta; S.P. Singh; Rajeev Saran Ahluwalia; Raghav Singh
The information about the ongoing tectonic faulting process causing earthquakes in an area having single or sparse seismological waveform data available remains a mystery for seismologists. The usual P-wave polarity inversion is unable to find the solution to the earthquake mechanism if the event is recorded with a lower azimuthal coverage network. Recently some seismologists seek towards the moment tensor solution and tried to find the focal mechanisms of earthquakes. The present work is a step in the same direction. Twelve regional earthquakes recorded by a distant seismological network in the Siang region of Arunachal Himalaya have been analyzed using ISOLA codes developed by Sokos and Zahradnik (2008). The solutions obtained by CMT Harvard by inversion of a large number of available waveform data have been considered standards. In the present study, moment tensor solutions have been estimated using the hypocentre locations given by the CMT catalog. The obtained solutions are comparable with the CMT solutions reported. High variance reduction has been obtained for the analyzed earthquakes that agree with the observations by Delouis and Legrand (1999), Kim and Kraeva (1999), Kim et al. (2000), Dragger (2003), and Maercklin et al. (2011) that moment tensor solutions can be obtained by using single station waveform data. The present study infers that the moment tensor inversion would be useful for obtaining information about the ongoing faulting process for which limited waveform data is available. For most of the Himalayan earthquakes which occur northern side of the Main Central Thrust (MCT), the seismological networks in those areas are either very sparse or not instrumented at all. The knowledge of undergone tectonics of this region was established with various faults visible on the surface by geologists and lacks the knowledge of the present situation of ongoing tectonics of the region. Hence, the moment tensor solutions obtained using available data will help in understanding the ongoing tectonic processes of the regions lacking well coverage of seismological networks. © 2022, Indian Academy of Sciences.
Prediction of acoustic impedance and density porosity using seismic inversion, and geostatistical methods on Krishna Godavari basin, India: A case study
(Elsevier B.V., 2023) S.P. Maurya; Richa; K.H. Singh; P. Mahadasu; Ajay P. Singh; G. Hema; P.K. Kushwaha; Raghav Singh
Seismic inversion is a geophysical technique used to estimate subsurface acoustic impedance models from seismic reflection data. The basic concept behind seismic inversion is to establish mathematical assumptions that can relate seismic responses to geological formation properties. This study addresses two types of seismic inversion methods, namely Band-limited inversion (BLI) and Linear programming inversion (LPI) techniques for the delineation of gas reservoirs. The study presents the successful use of the inversion in a real example from a gas sand reservoir in KG-Basin, India. Inversion has led to the removal of ambiguity and revealing clear information about the target area. This work aims to use and compare the results of these two generic seismic post-stack inversion methods for characterizing the reservoir. The inversion reveals the presence of low acoustic impedance around 2320-2440 ms and is inferred as a sandstone layer with a potential hydrocarbon reservoir. One can improve the analysis of the elastic properties for reservoir characterization by using rock physics knowledge to understand better reservoir properties such as water saturation, porosity, and shale volume. This study also uses the Rock Physics Template (RPT) and plots it with elastic properties to aid the efficient interpretation. RPT helps to identify the lithology and fluid content in the area. Results from the seismic study suggest a comparatively better analysis done through the LPI technique over BLI. In the last, the Multi-attribute transform as a part of the Geostatistical method is used to predict porosity away from the borehole. The method shows a very high porosity section and clearly shows the distribution of porosity in and around the region. The interpretation shows that a very high porosity zone (15–25%) is present that is in cross-correlation with a low impedance zone (4000-8000 m/s*g/cc) and confirms the presence of a hydrocarbon zone in-between 2320 and 2440 ms two-way travel time. All seismic inversion and geostatistical methods can retrieve subsurface information with very high-resolution images but the analysis based on linear programming is found slightly better in each step and depicts good prediction results. The study finally gives a flowchart that combines seismic inversion and geostatistical methods to extract petrophysical parameters from seismic data and can help to interpret hydrocarbon-bearing formation in any virgin area. © 2023 Elsevier B.V.
Recent Developments in Earthquake Seismology Present and Future of Seismological Analysis
(Springer International Publishing, 2024) Rohtash Kumar; Raghav Singh; Shyam Kanhaiya; Satya Prakash Maurya
The book presents earthquake source, wave propagation, site amplification, and other seismological studies including earthquake simulation, application of Artificial Neural Network (ANN) in seismology, earthquake early warning system, waveform inversion, moment tensor analysis, receiver function analysis, earthquake prediction, and earthquake early warning system applications. To minimize the losses due to an earthquake, it is better to understand the source properties, medium characteristics, site condition, and amplitude of a probable earthquake at a particular site. The evolutions of earthquake source models make it possible to understand the source dynamics. However, analysis of the source using a single-domain method does not provide a better understanding of the source dynamics. Therefore, this book combines methods from the earthquake spectrum to waveform inversion and joint inversion. The book also discusses earthquake prediction methods and their reliability around the globe, and techniques of simulation viz. stochastic, empirical, semi-empirical, and hybrid, along with their limitations and application. Seismology is an interdisciplinary subject. Therefore, the information presented in the book will appeal to a wider readership from students, teachers, researchers, planners engaged in developmental work, and people concerned with earthquake awareness. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study
(Springer, 2024) Nitin Verma; S.P. Maurya; Ravi Kant; K.H. Singh; Raghav Singh; A.P. Singh; G. Hema; M.K. Srivastava; Alok K Tiwari; P.K. Kushwaha; Richa
In the current study, a seismic inversion based on a hybrid optimisation of genetic algorithm (GA) and pattern search (PS) is carried out. The GA is an approach to global optimisation technique that always converges to the global optimum solution but takes much time to converge. On the other hand, the PS is a local optimisation technique and can converge at local or global optimum solution depending on the starting model. If these two techniques are used together (here termed hybrid optimisation), they can enhance one's benefit and reduce the drawbacks of others. The present study developed a methodology to combine GA and PS in a single flowchart and utilise seismic reflection data exclusively to predict porosity and impedance volume in inter-well regions. The algorithms are initially tested on synthetically created data based on the wedge model, the coal coking model, and the 1D convolution model. The performance of the algorithm is remarkably acceptable, according to the error analysis and statistical analysis between the inverted and the anticipated results. After that, the field post-stack seismic data from the Blackfoot field, Canada, is transformed into impedance and porosity using a developed hybrid optimisation technique. The inverted/predicted sections show very high-resolution subsurface information with impedance varying from 6000 to 14000 m/s×g/cc and porosity varying from 5 to 40% in the region. The error decreases from 1.0 to 0.5 for impedance inversion, whereas it varies from 1.4 to 0.5 for porosity inversion within 3000 iterations, which cannot be achieved by a single optimisation technique. The findings also demonstrated a sand channel (reservoir) anomaly with low impedance (6000–9000 m/s×g/cc) and high porosity (12–20%) in between 1040 and 1060 ms time intervals. This study provides evidence that subsurface parameters like acoustic impedance or porosity may be promptly and affordably determined using seismic inversion based on hybrid optimisation. The developed methodology is very helpful in finding subsurface parameters in a limited time and cost, which cannot be achieved only by global or local optimisation. © Indian Academy of Sciences 2024.