Seismic Data Processing From a Client's Perspective

Seismic data processing serves as a fundamental and intricate phase in the realm of geophysics, playing a pivotal role in unraveling the mysteries concealed beneath the Earth's surface. This methodological process is indispensable not only in the pursuit of locating and characterizing subsurface structures for oil and gas exploration but also in the broader context of environmental studies and diverse geophysical applications.

The role of the client or interpreter in data processing Quality Control (QC) is integral to ensuring the reliability and accuracy of the seismic data for subsequent geological interpretation. Clients and interpreters play a vital role as they possess valuable insights into the geological context and objectives of the project. Their involvement helps tailor the QC process to specific exploration or reservoir characterization goals.

Firstly, the client/interpreter is responsible for providing clear and comprehensive specifications regarding the desired outcomes and geological features of interest. This includes communicating the specific challenges posed by the geological setting and the expected characteristics of the subsurface structures. This information guides the data processing team in setting appropriate parameters and criteria for QC checks.

Secondly, the client's expertise is crucial in the interpretation of seismic images and the identification of anomalies or artifacts that may arise during data processing. Their understanding of the geological features allows them to recognize whether the processed data aligns with expected patterns. If discrepancies or uncertainties arise, the client can communicate these observations to the processing team for further investigation and refinement.

Additionally, the client's involvement ensures that the QC process is aligned with the project's overarching goals. By actively participating in QC reviews, the client can validate the accuracy of the processed data in relation to the geological context. This collaborative approach facilitates the identification and resolution of issues, leading to more reliable seismic images for interpretation.

Ultimately, the client/interpreter acts as a bridge between the geological objectives of the project and the technical aspects of data processing. Their active engagement in the QC process ensures that the final seismic data meets the requirements for accurate geological interpretation, contributing to informed decision-making in exploration and reservoir characterization projects.

At its core, seismic data processing commences with the acquisition of raw seismic data through the deployment of specialized instruments, such as seismic sensors, geophones or hydrophones, which record the reflections of artificially induced seismic waves. These waves penetrate the subsurface, bouncing off various geological formations, and their echoes are meticulously captured by the instruments. The subsequent steps in seismic data processing are a sophisticated amalgamation of mathematical algorithms, signal processing techniques, and computational methodologies.

The primary objective of seismic data processing is to transform the initially acquired raw data into comprehensible and insightful images that geoscientists can scrutinize and interpret. This transformation involves a series of intricate steps, including data cleaning, noise reduction, and the correction of various distortions that may arise during the acquisition phase. Sophisticated algorithms are applied to enhance the signal-to-noise ratio, ensuring that the relevant geological information stands out amidst the background noise.

Once the preprocessing is complete, the processed seismic data is subjected to imaging algorithms that generate detailed subsurface representations. These images provide a visual depiction of the subsurface structures, showcasing the distribution of geological layers, rock formations, and potential reservoirs of oil and gas. Geoscientists can then analyze these images to make informed decisions about the feasibility of exploration and extraction activities in a particular area.

Beyond the realm of oil and gas exploration, seismic data processing finds application in a myriad of geophysical endeavors. Environmental studies leverage this process to investigate the Earth's subsurface for purposes such as groundwater mapping, identification of geological hazards, and monitoring of underground carbon storage sites. In addition, the construction industry benefits from seismic data processing in assessing the stability of the ground for building infrastructure.

Seismic data processing stands as a sophisticated and indispensable facet of geophysics, providing a gateway to the intricate world beneath our feet. Its applications extend far beyond the quest for energy resources, encompassing a diverse range of scientific and practical pursuits that contribute to our understanding of the Earth's subsurface and its dynamic geological processes.

Here is a typical seismic data processing workflow, including but not limited to:

Acquisition of Seismic Data

Preprocessing

Field Data QC (Quality Control)

Missing Traces

Noise Removal or Denoise

Data Irregularities

Geometry Correction

Timing Corrections

Coherent Noise Attenuation

Ground Roll Removal

Multiple Attenuation

Predictive Deconvolution

Radon Transform

SRME

Wave Equation Multipe Elimination

Amplitude Corrections

Normalize Amplitudes

True Amplitude Recovery

Data Conditioning

Filtering

Bandpass Filtering

Notch Filtering

Amplitude Scaling

Gain Recovery

True Amplitude Recovery

Deconvolution

Designature

Wavelet Compression

Ghosting and Multiple Reflections

Resolution Enhancement

Attenuation Compensation

Q Attenuation

Methods of Deconvolution in Seismic Data Processing

Wiener Deconvolution

Inverse Filtering

Spiking Deconvolution

Non-Stationary Deconvolution

1. Challenges and Considerations
2. Trade-off with Noise
3. Parameter Selection
4. Computational Complexity
5. Velocity Analysis

Normal Moveout (NMO) Correction

Stacking

Migration

Time vs Depth

Migration Aperture

Aperture Size

Resolution Considerations

Computational Impact

Practical Considerations

Post-Stack Processing

Interpretation

Model Building

Inversion

Final Imaging

Quality Control QC: Happens throughout the whole process

AVO Compliant True Amplitude Processing

Structural Processing

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Acquisition of Seismic Data:

Acquisition of seismic data is a crucial process in the field of geophysics, providing valuable insights into the subsurface characteristics of the Earth. This method is widely employed in various industries, including oil and gas exploration, environmental monitoring, and geological research. The process involves the deployment of specialized instruments and the generation of controlled energy sources to produce seismic waves that traverse the subsurface, revealing important geological information.

One key component of seismic data acquisition is the use of arrays of geophones or hydrophones. These sensitive sensors are strategically positioned across the Earth's surface or inserted into boreholes, forming a network that captures the seismic waves generated during the exploration process. Geophones are commonly used on land, while hydrophones are employed in marine environments to detect underwater seismic signals.

The energy sources utilized in seismic data acquisition play a pivotal role in the success of the exploration. Air guns, for instance, are frequently employed in marine seismic surveys. These devices release high-pressure air pulses into the water, creating acoustic signals that penetrate the seafloor and underlying rock layers. In land-based surveys, explosive charges or vibroseis equipment may be used instead of air guns to generate seismic waves. These controlled sources of energy produce vibrations that travel through the Earth, interacting with subsurface structures in a way that allows geophysicists to analyze the composition and characteristics of the geological formations.

As seismic waves propagate through the subsurface, they encounter different rock layers and geological features. The geophones or hydrophones record the arrival times and amplitudes of these waves, creating a dataset that is subsequently processed and analyzed. By interpreting the seismic data, geoscientists can construct detailed images of the subsurface, identifying potential reservoirs of oil and gas, understanding geological structures, and assessing the suitability of a location for various purposes.

The acquisition of seismic data is a complex and precise undertaking that requires careful planning and execution. Advances in technology and methodology continue to enhance the efficiency and accuracy of seismic surveys, contributing to a better understanding of the Earth's subsurface and supporting various industries in making informed decisions about resource exploration and environmental management

Preprocessing:

Preprocessing is a crucial stage in this process, as it addresses various issues associated with the acquired seismic data. Here's an expansion on the mentioned preprocessing steps:

Field Data QC (Quality Control):

Missing Traces: Identify and address any gaps or missing traces in the acquired data. This can occur due to instrument malfunctions, data transmission issues, or other operational challenges.

Noise Removal: Denoise: The removal of noise, often termed Denoise, stands as a pivotal stage in the seismic data processing workflow. This crucial step involves a meticulous analysis aimed at identifying and mitigating undesirable signals that can distort seismic data, stemming from environmental factors, equipment malfunctions, or human activities. The denoising process encompasses a thorough examination of the dataset for anomalies, an assessment of the frequency and amplitude characteristics of noise, and the application of denoising techniques such as adaptive filtering and wavelet denoising. To minimize the introduction of noise, quality control measures, including data and navigation validation, are indispensable. Denoising is an iterative procedure, allowing for adjustments based on evolving insights into the characteristics of noise. Ultimately, successful noise removal enhances the signal-to-noise ratio, delivering a clearer and more precise representation of subsurface structures for thorough geological and geophysical analysis.

Various denoising techniques, including wavelet denoising and statistical filtering, play a pivotal role in eliminating or reducing random noise within seismic data. These methods selectively suppress noise while preserving the seismic signal, contributing significantly to the production of clearer and more accurate subsurface images. Adaptive filtering techniques add another layer of sophistication, dynamically adjusting filter parameters based on the distinct characteristics of the seismic data. This adaptability allows for effective noise removal while ensuring the integrity of seismic reflections. Additionally, advanced migration and inversion methods further contribute to designature by iteratively refining subsurface models, thus enhancing the overall representation of the subsurface and aiding in the mitigation of unwanted signatures.

Data Irregularities: Detect and correct irregularities in the field data, such as abrupt amplitude changes or inconsistencies. These irregularities can arise from various sources, including equipment malfunction or geological features.

Geometry Correction:

Sensor Layout Irregularities: Adjust the sensor layout to account for irregularities in the field setup. This involves ensuring that the sensor positions and orientations are accurately represented in the data. Corrections may be necessary for variations in sensor coupling, spacing, or alignment.

Surface Consistency: Ensure that the surface consistency is maintained throughout the data. Address any issues related to changes in topography or surface conditions that may affect the seismic wave propagation.

Timing Corrections:

Sensor Response Times: Correct for variations in sensor response times. Different sensors may have slightly different response characteristics, and accounting for these differences ensures that the seismic data is temporally aligned.

Timing Delays: Adjust for timing delays introduced during the data acquisition process. This correction is essential for maintaining the accuracy of the seismic wave arrival times, which is crucial for later s tages of data processing, such as velocity analysis and imaging.

Coherent Noise Attenuation:

Ground Roll Removal: Eliminate low-frequency noise caused by ground roll, which can interfere with the subsurface signal. Various filtering techniques, such as f-k filtering, can be employed to suppress ground roll while preserving the seismic reflections.

Multiple Attenuation: Mitigate the effects of multiple reflections, which can complicate the interpretation of subsurface structures. Techniques like predictive deconvolution or Radon transform may be applied to attenuate multiples.

Predictive Deconvolution:Utilized for alleviating the influence of multiples, improving the seismic data quality, and suppressing or eliminating the repercussions of multiple reflections identified and modeled according to the seismic data characteristics. This process includes forecasting the multiples existing in the recorded data.

Radon Transform:a mathematical technique utilized in seismic data processing, addresses issues related to multiple reflections and attenuations. The complexity introduced by these phenomena in seismic images can make interpretation and extraction of meaningful subsurface structure information challenging. By applying the Radon transform, seismic data is shifted from the time domain to the angle domain. This transformation aids in organizing and distinguishing events associated with various subsurface angles. Following the Radon transform, seismic data is arranged into angle-domain gathers, where each gather represents reflections at different angles, facilitating the separation of primary reflections from multiple reflections.

Surface Related Multiple Elimination: Tailored to address the complexities introduced by unwanted multiples in seismic records, Surface-Related Multiple Elimination (SRME) is specifically crafted for scenarios where seismic waves have interacted with the Earth's subsurface and surfaces multiple times before being recorded, posing challenges to subsurface structure interpretation. Primarily applied in marine seismic data processing, SRME is particularly effective in contexts where water bottom and air-water interface interactions generate significant multiples.

In the SRME process, the initial step involves predicting multiples using a model that considers both acquisition geometry and subsurface structure. This predictive model is then utilized to subtract or suppress the predicted multiples from the original seismic data. The subtraction process often occurs in the common offset or common midpoint domain, leveraging the distinct moveout behavior between primary reflections and multiples. The iterative nature of the procedure refines prediction and subtraction steps, enhancing the accuracy of multiple removal.

The benefits of SRME are substantial, as the technique contributes to the improvement of overall seismic image quality and interpretability. It results in enhanced subsurface resolution and bolsters confidence in geological interpretations. However, it is important to note that SRME may not completely eliminate all types of multiples, particularly those stemming from intricate subsurface structures. Successful implementation of SRME hinges on meticulous parameter tuning, consideration of data quality, and a comprehensive understanding of the geological context.

Wave Equation Multiple Elimination (WEME) : is an advanced seismic data processing technique designed to address the challenges posed by multiple reflections in seismic records. By leveraging the wave equation, a fundamental equation in seismology, WEME takes a physics- based approach to model the entire seismic acquisition process. This includes simulating the propagation of seismic waves through the Earth, considering interactions with subsurface layers and boundaries. WEME predicts multiples through advanced numerical algorithms, providing a detailed representation of the expected seismic response.

The key strength of WEME lies in its comprehensive removal of multiples. The technique separates the simulated wavefield into components associated with primary reflections and multiples, allowing for the isolation and subsequent removal or suppression of unwanted multiples from the original seismic data. The adaptability of WEME to diverse geological settings is a notable advantage, as it incorporates adaptive methods and inversion techniques to adjust model parameters based on observed differences between simulated and recorded data. While WEME's implementation can be computationally demanding due to the complexity of numerical modeling, its accuracy and effectiveness make it a valuable tool in scenarios where precise multiple elimination is crucial for accurate subsurface interpretation.

In summary, WEME represents a sophisticated approach to seismic data processing, offering a physics-based solution to the challenges of multiple reflections. Through numerical modeling, wavefield separation, and adaptive methods, WEME aims to enhance the overall quality of seismic images by comprehensively addressing the interference caused by unwanted multiples in the recorded data.

Amplitude Corrections:

Normalize Amplitudes: Normalization of amplitudes in a seismic dataset is a crucial step in the preprocessing workflow that plays a pivotal role in enhancing the reliability and interpretability of seismic data. This process involves adjusting the amplitudes of seismic traces to ensure uniform scaling across the entire dataset. The primary objective is to eliminate variations in signal strength caused by factors such as acquisition geometry, distance from the source, and geological properties.

One key benefit of normalizing amplitudes is the facilitation of consistent and meaningful quantitative analysis. By removing amplitude disparities, geoscientists and researchers can make more accurate comparisons between seismic traces and gathers. This consistency is particularly valuable in identifying subtle features within the subsurface and accurately characterizing geological structures. Normalization provides a common ground for assessing the amplitude variations and enables a more reliable understanding of the subsurface conditions.

Furthermore, the normalization process contributes to the mitigation of amplitude-related distortions that may arise during seismic data acquisition. It addresses issues such as amplitude decay with distance, differences in source-receiver coupling, and variations in subsurface reflectivity. As a result, the seismic dataset becomes a more faithful representation of the subsurface, allowing for clearer and more accurate interpretation.

In the realm of seismic interpretation, where the goal is often to extract valuable information about the Earth's subsurface, normalization is indispensable. It facilitates the creation of amplitude-consistent seismic sections, which are essential for delineating geological structures, identifying hydrocarbon reservoirs, and making informed decisions in exploration and reservoir characterization.

Additionally, normalized amplitudes contribute to the creation of reliable seismic attributes, such as amplitude maps and amplitude-versus-offset (AVO) analyses. These attributes serve as valuable tools for geoscientists to discriminate between different rock types, assess fluid content, and refine the understanding of subsurface properties.

Normalization of amplitudes in a seismic dataset is not merely a technical preprocessing step; it is a fundamental enabler of accurate and meaningful seismic analysis and interpretation. By providing a standardized basis for amplitude comparisons, normalization enhances the reliability of seismic data, ultimately advancing our ability to unravel the mysteries hidden beneath the Earth's surface.

True Amplitude Recovery: True Amplitude Recovery (TAR) plays a crucial role in seismic data analysis by rectifying distortions in seismic amplitudes that may occur during the data acquisition process. This corrective procedure becomes necessary due to various factors impacting amplitude fidelity, such as absorption, transmission losses, and other distortions.

When seismic waves traverse the subsurface, they may undergo absorption, resulting in energy loss and diminished amplitudes in the recorded data. True Amplitude Recovery compensates for this phenomenon, ensuring that the amplitudes accurately reflect the original subsurface characteristics.

Transmission losses, stemming from the diverse physical properties of subsurface layers, can also cause amplitude variations. TAR addresses these losses, applying corrections to maintain the accuracy of amplitude representation and provide a reliable depiction of subsurface structures.

Moreover, amplitude distortions can arise from instrumental effects, source-receiver coupling variations, and environmental factors during data acquisition. True Amplitude Recovery employs intricate algorithms and mathematical techniques to meticulously correct these distortions, aiming to eliminate discrepancies and faithfully reproduce the original amplitude information.

By accounting for absorption, transmission losses, and other distortions, True Amplitude Recovery enhances the precision of seismic data interpretation. This process, often involving sophisticated computational methods, contributes to the production of seismic images that faithfully represent the true amplitudes of subsurface geological features. Ultimately, TAR is indispensable in ensuring accurate subsurface analysis for applications such as oil and gas exploration and geological studies in the field of earth sciences.

By carefully addressing these preprocessing steps, seismic data processors can enhance the quality and reliability of the data, laying the foundation for more accurate and informative subsurface imaging and interpretation.

Data Conditioning:

Data conditioning in the context of seismic data processing involves various pre-processing steps to enhance the quality and reliability of the seismic data. Two important aspects of data conditioning are filtering and amplitude scaling:

Filtering:

Objective: The primary goal of filtering is to remove noise and unwanted frequencies from the seismic data, thereby improving the signal-to-noise ratio.

Methods:

Bandpass Filtering: Bandpass filtering is a critical signal processing method widely employed in seismic data analysis. This technique involves selectively permitting a specific range of frequencies to pass through a system while filtering out both high and low-frequency components. In the context of seismic studies, bandpass filtering is instrumental in isolating the seismic signal of interest from background noise, providing researchers with a more accurate and focused representation of geological phenomena. The process consists of identifying the relevant frequency range, designing a bandpass filter with specific cutoff frequencies, applying the filter to the seismic data, and subsequently conducting enhanced signal analysis.

The primary objective of bandpass filtering in seismic analysis is noise reduction. By excluding frequencies outside the desired range, this technique effectively suppresses unwanted interference, resulting in a cleaner and more refined seismic signal. The application of bandpass filters is crucial for researchers seeking to unravel valuable information from seismic data, as it allows for a targeted examination of seismic events with improved precision. This focused approach not only enhances the overall quality of the data but also facilitates more accurate interpretations of geological processes occurring beneath the Earth's surface.

Bandpass filtering contributes significantly to advancing our understanding of geological phenomena by improving the reliability of seismic studies. Through the meticulous application of filters, scientists and geophysicists can delve into the characteristics of seismic signals, identify patterns, and determine the magnitude and depth of seismic events. Overall, bandpass filtering stands as a fundamental tool in the seismic analyst's toolkit, enabling a more nuanced and detailed exploration of subsurface geological features and enhancing the overall quality of seismic data interpretation.

Notch Filtering: Notch filtering is a specialized signal processing technique designed to suppress specific frequencies within a given signal. This method is particularly useful when there is a need to eliminate or significantly reduce unwanted interference or noise at specific frequencies. The term "notch" refers to a narrow band of frequencies that are effectively removed or attenuated, creating a "notch" in the frequency spectrum. This targeted approach allows for the precise removal of problematic frequencies without affecting the broader frequency content of the signal.

In practical applications, notch filtering is often employed to mitigate interference caused by external sources or environmental factors. For instance, in seismic data analysis or vibration monitoring, certain frequencies may be associated with cultural noise, such as machinery vibrations or human activities. Notch filters can be strategically applied to suppress these specific frequencies, ensuring that the analysis focuses on the seismic or vibrational signals of interest while minimizing the impact of external disturbances. The ability to selectively target and attenuate specific frequencies makes notch filtering a valuable tool in scenarios where isolating a clean signal from a noisy environment is paramount.

The implementation of notch filters involves identifying the frequencies that need to be suppressed, and then designing filters with narrow bandwidths centered around these frequencies. Common filter designs for notch filtering include comb filters and infinite impulse response (IIR) filters. By precisely tuning these filters to the frequencies of unwanted interference, engineers and researchers can significantly improve the signal-to- noise ratio, enhancing the accuracy and reliability of data analysis in fields such as telecommunications, audio processing, and environmental monitoring. Notch filtering thus plays a crucial role in enhancing the quality of signals in various applications by selectively addressing and mitigating specific frequency disturbances.

Amplitude Scaling:

Objective: Seismic traces may have variations in amplitude due to differences in source-receiver distances, recording equipment, or other factors. Amplitude scaling is applied to normalize these variations and ensure consistency in the amplitude domain.

Methods:

Gain Recovery: Gain recovery is an essential aspect of seismic data processing, involving the application of a corrective factor to each seismic trace to address variations in amplitude. The goal is to ensure uniform amplitudes across the seismic dataset, allowing for more accurate and meaningful interpretation of subsurface structures and properties. The need for gain recovery arises from factors such as variations in source- receiver distances, differences in subsurface conditions, and the effects of attenuation during signal propagation. By applying a gain correction factor, seismic analysts can normalize these variations and enhance the overall quality and reliability of the seismic data.

The gain correction factor applied during gain recovery is typically determined through a combination of calibration processes and detailed analysis of the seismic dataset. Calibration involves the use of known references, such as seismic signals with well- understood characteristics or synthetic data, to establish a baseline for the expected amplitudes. The calibration process allows for the identification and quantification of factors contributing to amplitude variations. Additionally, sophisticated algorithms and statistical methods may be employed to analyze the entire seismic dataset and derive optimal gain correction factors for each trace.

The significance of gain recovery becomes particularly evident in seismic reflection studies where accurate amplitude information is crucial for interpreting subsurface features. Without proper gain correction, variations in signal amplitudes can lead to misinterpretations of geological structures, potentially obscuring important details or introducing artifacts into the analysis. Moreover, gain recovery contributes to the consistency of seismic datasets acquired at different times or locations, enabling effective comparison and integration of data from multiple sources.

In practical terms, the gain recovery process involves multiplying each seismic trace by its respective gain correction factor. This corrective action helps to bring all traces to a consistent amplitude scale, facilitating a more accurate representation of subsurface structures and geological properties. Gain recovery is, therefore, a critical step in the seismic data processing workflow, contributing to the reliability and precision of seismic interpretations in various applications, including oil and gas exploration, environmental studies, and geological research.

True Amplitude Recovery: True amplitude recovery represents an advanced and nuanced aspect of seismic data processing that goes beyond basic gain recovery. In seismic reflection studies, it is recognized that the recorded amplitudes of seismic reflections can be influenced by factors such as the angle of incidence, the geological properties of subsurface materials, and the radiation pattern of the seismic source. True amplitude recovery aims to correct for these complex influences, providing a more accurate representation of the actual amplitudes of subsurface reflections.

One key consideration in true amplitude recovery is the understanding that seismic reflections are not only affected by changes in subsurface properties but also by the angle at which the seismic waves interact with these subsurface layers. This angle-dependent behavior introduces amplitude variations that can be significant, especially in areas with complex geological structures. To address this, seismic analysts employ various techniques and algorithms to account for the effects of reflection angles and recover the true amplitudes of subsurface reflections.

In the context of true amplitude recovery, the application of correction factors involves more sophisticated methodologies compared to standard gain recovery. Specialized algorithms take into account the angle-dependent reflection coefficients and the geometrical spreading of seismic waves as they propagate through the subsurface. Additionally, the incorporation of well-log data, geological models, and information about the seismic source and receiver characteristics contributes to a more accurate estimation of the true amplitudes.

True amplitude recovery is particularly crucial in scenarios where precise amplitude information is essential for quantitative analysis of subsurface properties, such as estimating reservoir properties in hydrocarbon exploration. It ensures that the amplitudes in the seismic dataset are not only normalized but also reflect the actual variations in subsurface properties, allowing for more reliable interpretation and characterization of geological structures.

While true amplitude recovery involves more complexity and computational intensity than basic gain recovery, its application enhances the fidelity of seismic data and contributes to a more accurate representation of subsurface conditions. As technology and methodologies in seismic processing continue to advance, true amplitude recovery remains a critical component in achieving high-quality seismic interpretations for a range of geological and geophysical applications.

Why Data Conditioning is Important:

Enhanced Interpretability: Cleaned and scaled data provide a clearer representation of subsurface structures, making it easier for interpreters to identify geological features.

Improved Processing: Filtering reduces noise, enhancing the quality of seismic images and facilitating more accurate subsurface analysis.

Consistency: Amplitude scaling ensures that seismic amplitudes are consistent across traces, allowing for meaningful comparisons and analysis.

It's important to note that the specific techniques and parameters used for data conditioning can vary based on the characteristics of the seismic data and the objectives of the study.

Deconvolution: What is it?

Deconvolution is a mathematical operation widely used in various fields, including signal processing, image processing, and seismic data analysis. It involves the removal or minimization of the effects of convolutional processes from a signal or dataset. Convolution, the process that deconvolution aims to reverse, refers to the mathematical operation that combines two signals to produce a third one, often involving a rolling or sliding operation.

In the context of seismic data analysis, deconvolution plays a crucial role in enhancing the quality and resolution of seismic images. During seismic surveys in the oil and gas industry, seismic waves are emitted into the Earth's subsurface, and the recorded signals are affected by convolutional processes caused by the seismic source wavelet and the subsurface layers. These convolutional effects can lead to blurring or spreading of seismic reflections, reducing the clarity and interpretability of subsurface structures. Deconvolution is applied to mitigate these effects, aiming to recover the true seismic signal and improve the fidelity of the seismic data.

The deconvolution process involves the application of mathematical algorithms that act on the recorded seismic data. These algorithms are designed to invert or compensate for the convolutional effects, effectively restoring the sharpness and resolution of seismic reflections. There are various deconvolution techniques, such as predictive deconvolution, Wiener deconvolution, and wavelet-based deconvolution, each with its own set of advantages and applications.

In broader terms, deconvolution is not exclusive to seismic data processing. It finds applications in fields like image processing to enhance image sharpness and clarity, communications to improve signal transmission, and astronomy to deblur astronomical images affected by atmospheric conditions.

The effectiveness of deconvolution relies on the accurate understanding of the convolutional processes affecting the signal and the appropriate selection of deconvolution parameters. While it is a powerful tool for signal enhancement, improper application or misinterpretation of the data can lead to artifacts or distortions. As technology and methodologies in signal processing continue to advance, deconvolution remains a valuable and versatile tool for improving the quality and interpretability of data in various scientific and engineering disciplines.

Designature: Designature methods in seismic data processing are essential techniques employed to mitigate the impact of noise and unwanted signatures, enhancing the quality and interpretability of seismic images. Deconvolution is a common method used to remove the seismic source signature, improving the resolution of seismic reflections. This is particularly useful in scenarios where the source signature interferes with the identification of subtle subsurface features. Wavelet shaping involves adjusting the shape of the seismic wavelet to enhance the signal-to-noise ratio and improve the frequency content of the seismic data. Spectral whitening (caution needed if True Amplitude Processing for AVO purposes) equalizes the amplitudes of seismic reflections across different frequency components, reducing the impact of frequency-dependent noise.

The Need for Deconvolution in Offshore Surveys:

Wavelet Compression:

Seismic waves generated by air guns or other sources undergo complex transformations as they travel through the subsurface. These transformations result in a seismic wavelet that may be broadened or distorted.

Deconvolution helps in compressing and sharpening the seismic wavelet, improving the temporal resolution of the seismic data.

Ghosting and Multiple Reflections:

Offshore seismic surveys are prone to additional challenges such as water layer multiples and ghost reflections. These phenomena can obscure the true subsurface structure.

Deconvolution is employed to suppress or remove unwanted multiples and ghosts, allowing for clearer imaging of the primary reflections from the subsurface layers.

Resolution Enhancement:

Offshore environments often have complex geological structures, and achieving high resolution is critical for accurate interpretation.

Deconvolution helps in increasing the vertical and horizontal resolution of seismic data, providing a clearer picture of the subsurface geology.

Attenuation Compensation:

Seismic waves may experience attenuation as they travel through the subsurface, resulting in amplitude loss.

Deconvolution can be used to correct for amplitude variations and compensate for attenuation effects, ensuring that the seismic amplitudes reflect the true subsurface properties.

Q Attenuation:

Q attenuation, denoted as the quality factor (Q), is a dimensionless parameter that characterizes the attenuation or damping of seismic waves as they traverse subsurface materials. It quantifies how quickly seismic energy dissipates, with higher Q values indicating lower attenuation. The mathematical expression for Q is often incorporated into the seismic wave equation, accounting for amplitude decay over time and distance. Q is frequently frequency-dependent, varying with different frequencies of seismic waves.

In seismic data processing, understanding and correcting for Q effects are crucial for improving data quality and subsurface imaging accuracy. Knowledge of Q impacts signal quality, with higher Q values resulting in clearer seismic reflections that travel greater distances. Conversely, lower Q values lead to more rapid signal decay and weaker reflections.

Q is especially relevant in hydrocarbon exploration, where variations in Q can signal changes in subsurface properties like fluid content, porosity, and lithology. Geoscientists use Q information to interpret seismic data, identify potential reservoirs, and make informed decisions in resource exploration. In some cases, inversion techniques are employed to estimate Q values from seismic data, contributing to refined subsurface models.

In summary, Q attenuation is a critical parameter in seismic analysis, influencing the behavior of seismic waves in subsurface materials. Its application is fundamental to accurate seismic data processing, interpretation, and exploration activities, providing valuable insights into subsurface structures and aiding in decision-making processes in the oil and gas industry.

Methods of Deconvolution in Seismic Data Processing:

Wiener Deconvolution:

Wiener deconvolution is a mathematical technique used in signal processing and image restoration. It aims to recover an estimated original signal or image from a degraded or blurred version by considering both the degradation process and statistical properties of the original signal. The method operates in the frequency domain, utilizing a Wiener filter to minimize the mean square error between the estimated and true signals. While effective when noise and degradation characteristics are well-known, it may amplify noise if these parameters are poorly understood, potentially leading to undesired artifacts in the restored signal.

Inverse Filtering: Inverse filtering aims to reverse the convolutional effects in signal or image processing. However, it is sensitive to noise and can amplify it during the deconvolution process. To address this issue, techniques such as regularization, filtering in the frequency domain, Wiener deconvolution, noise reduction, and careful parameter selection are employed to avoid noise amplification and enhance the quality of the reconstructed signal.

Spiking Deconvolution:

Spiking deconvolution in seismic data processing aims to enhance signal quality by applying a deconvolution filter to remove unwanted high-frequency noise or spikes. This process improves the resolution and clarity of seismic signals, leading to a clearer representation of subsurface structures. It is not typically associated with removing spikes or artifacts but focuses on refining the seismic data for better interpretation.

Non-Stationary Deconvolution: Non-stationary deconvolution methods in seismic signal processing are designed to address variations in signal characteristics across different locations in a survey area. Unlike traditional methods that assume uniform signal properties, non-stationary deconvolution considers spatial variability. These methods incorporate adaptive filtering, allowing adjustments based on local seismic data characteristics, and may involve time-varying filters and frequency-dependent processing. The goal is to enhance the accuracy of subsurface imaging by customizing the deconvolution process to the specific features of seismic signals in diverse geological areas.

Challenges and Considerations:

Trade-off with Noise:

Deconvolution is a seismic data processing technique aimed at enhancing subsurface imaging by removing the effects of the seismic source wavelet. However, it carries the risk of amplifying noise in the data, necessitating a careful balance between eliminating unwanted effects and preserving signal integrity. Strategies such as regularization, filtering, iterative approaches, and quality control are employed to mitigate noise amplification and achieve an optimal balance during the deconvolution process.

Parameter Selection:

Choosing appropriate parameters for deconvolution methods is essential. The parameters may need to be adjusted based on the characteristics of the seismic data and the geological features of the survey area.

Computational Complexity:

Some deconvolution methods can be computationally intensive, especially for large offshore datasets. Efficient algorithms and computing resources are required for timely processing.

In summary, deconvolution is a fundamental step in seismic data processing, addressing challenges specific to the marine environment. It plays a key role in enhancing the resolution and accuracy of seismic images, ultimately aiding in the discovery and extraction of offshore oil and gas resources.

Velocity Analysis:

The client/interpreter's role in data processing Quality Control (QC) is particularly crucial when it comes to velocity analysis in seismic data processing. Velocity is a fundamental parameter in seismic data interpretation, influencing the accuracy of subsurface imaging and the identification of geological structures. The client or interpreter, possessing a deep understanding of the geological context and objectives, plays a pivotal role in guiding and validating the velocity-related QC processes.

The client's input is essential in providing information about the expected velocity characteristics of the subsurface. This includes details about the geological formations, variations in lithology, and potential complexities that may impact velocity models. Clear communication between the data processing team and the client ensures that the velocity analysis is aligned with the geological reality of the region.

Their expertise is valuable in the interpretation of velocity analyses and migration results. They can assess whether the velocity models generated during processing accurately reflect the subsurface conditions. This involves identifying anomalies, such as areas where the velocity model might deviate from the expected geological structure. Any discrepancies observed by the client can guide further investigations and adjustments in the velocity analysis.

Additionally, the client's involvement is critical during the QC of migrated seismic images. Velocity errors can lead to mispositioning of subsurface reflectors, affecting the overall interpretability of the data. The client's insights help ensure that the migration process is informed by accurate velocity models, resulting in seismic images that faithfully represent the subsurface geology.

And finally, the client/interpreter's role in velocity-related QC goes beyond technical assessments; it involves the application of geological knowledge to validate and improve the accuracy of velocity models and their impact on seismic interpretation. This collaborative approach between the data processing team and the client enhances the reliability of velocity work, contributing to more accurate subsurface imaging and geological understanding in exploration and reservoir characterization projects.

Estimating a subsurface velocity model involves analyzing seismic travel times. The process includes collecting seismic data, determining event locations, picking arrival times of seismic waves, calculating travel times, and constructing velocity-depth relationships. This is done through iterative modeling, where an initial velocity model is refined to minimize the differences between observed and calculated travel times. The final model is validated, and geological interpretations are made based on the velocity variations. The results are presented graphically, and a comprehensive report is prepared, considering uncertainties and limitations in the model. Collaboration with experts and the use of complementary geophysical methods enhance the accuracy of the subsurface velocity model.

Normal Moveout (NMO) Correction:

Normal moveout (NMO) correction is a crucial step in seismic data processing. It addresses the effects of subsurface velocity variations by adjusting the arrival times of seismic reflections. This correction ensures that reflections from different depths align correctly, enhancing the accuracy of subsurface imaging. The correction is typically applied using a hyperbolic moveout equation based on measured arrival times, offset distance, and stacking velocity. NMO correction is essential for producing a clearer and more accurate representation of subsurface structures in seismic data.

Stacking:

Stacking is a crucial step in seismic data processing where multiple seismic traces are combined to enhance subsurface reflections and improve the signal-to-noise ratio. By adding together coherent signals from different traces, stacking helps to reinforce the desired seismic reflections while reducing the impact of random noise. This process results in a clearer and more reliable representation of subsurface structures in seismic data, contributing to improved subsurface imaging quality.

Migration:

Migration is a key step in seismic data processing that aims to accurately position subsurface reflections in their correct spatial locations. This correction is necessary to account for the effects of dipping subsurface layers and to obtain a more accurate representation of subsurface structures.

Dipping layers in the subsurface can cause reflections to appear displaced from their true spatial positions in seismic data. Migration involves applying mathematical algorithms to reposition these reflections to their correct locations. This correction is particularly important in areas with complex geological structures where traditional processing methods may result in inaccuracies.

Migration techniques, such as time migration or depth migration, take into account the angle of the subsurface reflectors and the velocity variations in the subsurface. By considering these factors, migration algorithms are able to accurately reposition reflections to their correct spatial coordinates.

In summary, migration is a crucial correction step in seismic data processing that addresses the effects of dipping subsurface layers. It ensures that seismic reflections are accurately positioned in their true spatial locations, leading to a more precise and reliable representation of subsurface structures in the final seismic image.

Time vs Depth Migration: Time-versus-depth seismic migration processing is a pivotal step in seismic data processing that aims to transform seismic data from the time domain to the depth domain. This process is essential for creating accurate subsurface images and improving the interpretation of geological structures. The first crucial step involves velocity analysis, where the subsurface velocity of seismic waves is determined through iterative modeling. A velocity model is then constructed to represent the varying subsurface velocities encountered by seismic waves during their travel.

There are two main approaches to time-versus-depth seismic migration: pre-stack migration and post- stack migration. Pre-stack migration is performed on individual seismic traces before stacking and is suitable for handling complex geological structures or anisotropic conditions. On the other hand, post- stack migration is applied after stacking seismic traces and is computationally less demanding, making it suitable for simpler subsurface structures. The migration algorithms employed in this process include the widely used Kirchhoff migration, which applies the Kirchhoff integral to compute the amplitude and phase of seismic reflections, and finite-difference migration, a numerical method suitable for handling complex geological settings.

The outcome of the migration process is a seismic image in the depth domain, where subsurface structures are depicted in terms of their true depth below the Earth's surface. This depth-imaged seismic data offers a more accurate representation for geological interpretation, reservoir characterization, and other subsurface analyses. Quality control measures are applied to assess the accuracy of the depth- imaged seismic data, and iterative refinement processes may be employed to enhance the precision of migrated seismic images, particularly in regions with complex geological structures. Overall, time- versus-depth seismic migration is a crucial tool in the oil and gas exploration industry and other geophysical applications, enabling a more accurate understanding of subsurface structures.

Seismic migration is a crucial process in seismic data processing that transforms data from the time domain to the depth domain, providing a more accurate representation of subsurface structures. Various migration techniques are employed to address different geological scenarios and challenges.

One widely used method is Kirchhoff Migration, which applies the Kirchhoff integral to compute the amplitude and phase of seismic reflections at each subsurface point. This approach is flexible and can handle irregularities in the subsurface structure. Finite-Difference Migration is a numerical method that solves the wave equation using finite-difference methods. It is particularly suited for complex geological settings and anisotropic conditions, providing accurate modeling of wave propagation.

Reverse Time Migration (RTM) is an advanced technique that simulates seismic wavefield propagation backward from receivers to sources. RTM is effective in handling complex geological structures, such as subsalt imaging challenges, and is known for accurate imaging of steeply dipping reflectors. Another method, One-Way Wave Equation Migration, simplifies the wave equation to a one-way form, offering computational efficiency while maintaining reasonably accurate results. Additionally, there is Angle Domain Migration, which focuses on the angles of incidence and reflection of seismic waves, providing improved imaging of steeply dipping reflectors and addressing regions with structural complexity.

Some migration methods incorporate adaptive velocity models during the migration process. This adaptive approach adjusts the velocity model based on observed differences between simulated and recorded data, enhancing the accuracy of the migration process in varying subsurface conditions. The selection of a migration method depends on factors such as geological complexity, computational resources, and the specific challenges of a seismic survey. In practice, a combination of migration techniques or the application of advanced algorithms is often employed to achieve optimal results in subsurface imaging and interpretation.

Migration Aperture:

Migration aperture in seismic data processing refers to the size or extent of the region considered during the migration process. Migration is a crucial step in correcting for subsurface velocity variations and accurately positioning seismic reflections. The migration aperture parameter is significant as it directly influences the accuracy and resolution of the migrated seismic image.

Key Points:

Aperture Size:

Migration aperture refers to the size of the area considered during migration.

A larger migration aperture captures a broader region, potentially revealing a comprehensive range of subsurface features.

Resolution Considerations:

Choice of migration aperture involves a trade-off between resolution and coverage

A larger aperture can provide a more comprehensive view but may lead to lower resolution for smaller- scale features.

A smaller aperture may offer higher resolution but might miss broader geological structures.

Computational Impact:

Computational demand increases with the size of the migration aperture.

Geophysicists balance the desired imaging objectives with available computational resources.

Practical Considerations:

Geophysicists carefully choose the migration aperture based on geological objectives and computational constraints.

The goal is to achieve a balance that captures both detailed and broad-scale subsurface features in the migrated seismic image.

In summary, migration aperture is a critical parameter in seismic data processing, influencing the accuracy, resolution, and computational efficiency of the migration process. Careful consideration of the migration aperture is essential for achieving a well-balanced seismic image that meets geological objectives.

Post-Stack Processing:

Post-stack processing is a crucial phase in seismic data processing that occurs after stacking and migration. During this stage, various additional processing steps are applied to further enhance the quality and interpretability of seismic images. One common post-stack processing step is amplitude correction.

Amplitude correction involves adjusting the amplitudes of seismic reflections to account for factors that may have affected the recorded amplitudes during the data acquisition and processing stages. These factors include variations in source-receiver distances, absorption, and other amplitude-related anomalies. By correcting for these effects, the amplitudes of seismic reflections become more consistent and accurately reflect the subsurface geology.

In addition to amplitude correction, other post-stack processing steps may include filtering to remove unwanted frequencies, noise reduction techniques, and the application of advanced imaging algorithms to enhance specific geological features. The goal of post-stack processing is to create a final seismic image that provides a clear and accurate representation of subsurface structures.

In summary, post-stack processing involves applying additional steps, such as amplitude correction, to further refine and improve seismic images after stacking and migration. These enhancements contribute to a more accurate and interpretable representation of the subsurface geology in the final seismic dataset.

Interpretation:

Geoscientists play a crucial role in interpreting processed seismic data to uncover information about the subsurface. They identify and map subsurface structures, detect faults, and assess the potential for hydrocarbon reservoirs. This interpretation involves analyzing seismic images, integrating well data, creating 3D visualizations, and conducting risk assessments. The insights gained guide decision-making in the exploration and development of oil and gas resources. Review next Post on Interpretation.

Model Building:

Building geological models involves integrating interpreted seismic data with well log information and other geological data sets. Geoscientists create three-dimensional (3D) models that represent the subsurface, capturing structural and stratigraphic features. The models incorporate property distributions, aiding in the evaluation of potential hydrocarbon reservoirs. This collaborative process, including uncertainty analysis, provides a comprehensive understanding of the subsurface geology, supporting informed decisions in resource exploration and development.

Inversion:

Inversion is a geophysical technique used to estimate subsurface properties like rock characteristics and fluid content from observed seismic data. The process involves forward modeling, parameterization of subsurface properties, and defining an objective function to minimize the difference between observed and simulated seismic data. Inversion algorithms iteratively adjust parameters, and regularization techniques are applied to stabilize solutions. The final outcome includes estimates of rock properties (e.g., velocity, density) and fluid content in the subsurface. Inversion results aid in refining geological models, providing quantitative insights for hydrocarbon exploration or environmental assessments. The process also includes an assessment of uncertainties, considering factors like data noise and parameter variations. Review in future post coming soon... I hope.

Final Imaging:

The generation of final seismic images and attribute maps involves the integration of processed seismic data, interpretation results, and geological models to create a comprehensive view of the subsurface. Key steps include the creation of high-quality seismic images that visually represent subsurface structures, attribute mapping to highlight specific properties like amplitude and frequency, and the generation of maps depicting rock properties and fluid content. Structural and stratigraphic maps showcase geological features, while cross-sections offer detailed profiles of the subsurface.

The integration of well data further validates the geological model, ensuring accuracy. Thorough quality control and review processes are conducted to maintain reliability. The final seismic images and maps are presented in visually intuitive formats, supporting interpretation, decision-making, and communication among geoscientists and stakeholders. This detailed process aids in exploration, resource development, and environmental assessments by providing a comprehensive and accurate understanding of the subsurface.

Throughout the entire seismic data processing workflow, iterative quality control and validation are essential to ensure the reliability of the final subsurface images. This process requires a combination of geophysical expertise, computational resources, and specialized software tools.

Quality control (QC) is a crucial aspect of seismic data processing to ensure that the processed data accurately reflects subsurface conditions and meets the standards required for geological interpretation. The seismic data processing workflow involves several key steps, and QC checks are essential at various stages. Here are the main areas to identify in the QC process:

Data Acquisition QC:

Sensor Calibration: Verify that the seismic sensors (geophones or hydrophones) are properly calibrated to ensure accurate recording of seismic signals.

Source Verification: Confirm that the seismic source is functioning correctly and producing the intended energy levels. Check for any variations in source strength during data acquisition.

Pre-Processing QC:

Data Cleaning: Ensure that the raw data is free from artifacts, such as spikes or irregular noise, which can adversely affect subsequent processing steps.

Frequency Content: Verify that the data has the expected frequency content and that there are no distortions introduced during data acquisition.

Velocity Analysis and Migration QC:

Velocity Model QC: Assess the quality of the velocity model used for migration. Errors in the velocity model can lead to mispositioning of subsurface reflectors.

Migration Artifacts: Check for migration artifacts that may arise from inaccuracies in the migration process, such as imaging distortions or false events.

Stacking QC:

Stacked Section Inspection: Examine the stacked seismic section for overall image quality, coherence of reflectors, and the presence of any anomalies or artifacts.

Amplitude Analysis: Verify that amplitude anomalies are consistent with geological expectations. Inconsistent amplitudes may indicate processing errors or problems.

Post-Stack Processing QC:

Filtering and Gain: Assess the effectiveness of post-stack processing steps, including filtering and amplitude scaling. Verify that they enhance signal quality without introducing noise.

Data Regularization: Check for any irregularities introduced during regularization processes, ensuring that the data maintains a consistent and realistic appearance.

Interpretation QC:

Geological Consistency: Verify that seismic reflections correspond to expected geological features. Inconsistencies may indicate errors in processing or interpretation.

Structural Continuity: Check the structural continuity of subsurface features across seismic sections to ensure that the interpretation is geologically plausible.

Final Deliverables QC:

Map and Volume Integrity: Ensure the integrity of final deliverables, such as seismic maps and volumes. Verify that they accurately represent subsurface structures and adhere to project specifications.

Documentation: Confirm that comprehensive documentation accompanies the processed data, detailing processing parameters, applied corrections, and any potential limitations.

Quality control (QC) is an integral part of seismic data processing, involving meticulous checks at various stages to ensure the accuracy and reliability of the processed data for geological interpretation. The process begins with confirming proper sensor calibration and source functionality during data acquisition, followed by pre-processing QC to ensure data integrity. Velocity analysis and migration QC focus on assessing the quality of the velocity model and identifying potential migration artifacts. Stacking QC examines overall image quality, coherence of reflectors, and amplitude anomalies. Post-stack processing QC evaluates the effectiveness of filtering and amplitude scaling. Interpretation QC ensures the geological consistency of seismic reflections and structural continuity. Final deliverables undergo scrutiny for map and volume integrity, accompanied by comprehensive documentation detailing processing parameters. This systematic approach to QC ensures the production of reliable seismic images essential for accurate geological interpretation and decision-making in exploration and reservoir characterization projects.

AVO Compliant True Amplitude

AVO (Amplitude Versus Offset) compliant true amplitude processing in seismic exploration involves a series of steps to ensure that seismic data accurately reflects subsurface properties. These steps are crucial for obtaining reliable information about the Earth's subsurface, such as lithology and fluid content. The key components of AVO-compliant true amplitude processing include:

Pre-processing:

Correcting seismic data for variations in source-receiver geometry to accurately represent subsurface reflectivity.

Amplitude Calibration:

Normalizing amplitudes to a standard reference point for consistency and comparability.

True Amplitude Restoration:

Compensating for absorption effects (Q attenuation) in the subsurface.

Correcting for amplitude losses due to transmission through the Earth.

AVO Analysis:

Studying how seismic reflection amplitudes change with offset to gain insights into subsurface properties.

Wavelet Processing:

Equalizing the seismic data wavelet across different offsets for accurate amplitude comparisons

Velocity Model Building:

Constructing an accurate subsurface velocity model for proper time-aligning and stacking seismic traces.

Migration:

Performing pre-stack depth migration to accurately position seismic events in depth, considering the velocity model.

Final Processing:

Applying post-stack processing techniques, such as filtering, deconvolution, and noise reduction, to enhance the quality of seismic images.

The process is often iterative, with seismic interpreters refining parameters to achieve the best results. Ongoing advancements in technology and methodologies contribute to improving the accuracy and efficiency of AVO-compliant true amplitude processing in seismic exploration.

Structural Processing:

Structural processing in seismic exploration involves a series of steps aimed at enhancing and revealing the subsurface structural features of the Earth. This type of processing is crucial for understanding the geometry and properties of subsurface geological formations. Here is an overview of the key steps involved in structural processing:

Data Quality Control:

Identify and address any issues related to data quality, such as noise, acquisition artifacts, or instrument malfunctions.

Geometry Correction:

Adjust the seismic data to correct for variations in source-receiver geometry. This correction ensures that the data accurately represents the subsurface reflectivity.

Velocity Analysis:

Estimate the subsurface velocities to correct for variations in the speed of seismic waves at different depths. A reliable velocity model is crucial for accurate imaging.

NMO (Normal Moveout) Correction:

Correct for the distortion in seismic reflection arrival times caused by variations in subsurface velocities. This correction is essential for properly aligning seismic events.

Migration:

Perform migration algorithms to reposition seismic events to their true subsurface locations. Migration improves the accuracy of seismic images, especially in areas with complex geological structures.

Structural Enhancement:

Apply filters and processing techniques to enhance structural features in the seismic data. This may include edge detection, dip filtering, or other methods to highlight faults, folds, and other geological structures.

Amplitude Corrections:

Address amplitude variations caused by factors such as absorption and transmission losses. Correcting for these effects ensures that the seismic amplitudes accurately represent subsurface reflectivity.

Post-Stack Processing:

Apply additional processing techniques to the stacked seismic data, such as deconvolution, filtering, and noise reduction, to improve the clarity of structural features.

Attribute Analysis:

Extract and analyze seismic attributes that provide information about subsurface properties, such as dip, azimuth, and amplitude variations. Attribute analysis can help identify geological structures and anomalies.

Inversion:

Perform inversion techniques to convert seismic data into quantitative information about subsurface properties, such as rock properties, fluid content, and lithology.

Structural processing is an integral part of seismic data interpretation and is essential for generating accurate images of the subsurface. The goal is to reveal the structural complexity of the Earth's subsurface, aiding in the exploration and characterization of geological formations for various applications, including oil and gas exploration, environmental studies, and geotechnical investigations.

For Reference: Image displayed in header taken from: Mitchell, V., et.al . 2019. Regional Overview With New Insights Into the Petroleum Prospectivity of the Southeastern Offshore Newfoundland Margin, Canada. 81st EAGE Conference London, 2019.

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