GeoManager AI-Agent Beta: Join the Early Access Program
Help us shape the first truly useful AI orchestrator for geoscience projects. This short survey (5–7 minutes) will help us understand your team, your workflows, and whether GeoManager AI-Agent is a good fit for your seismic interpretation projects. By answering these questions, you’ll get a chance to join a small group of early adopters, influence our roadmap, and be among the first to try Geoplat’s new AI‑powered experience in real projects.
You agree with our Terms and Conditions
Features
Our ML solutions are here to streamline the processes for your individual business needs and to provide valuable data insights
Structure-Oriented Seismic Conditioning
Low quality seismic datasets often makes it difficult to build a structural framework
and predicting pay zone properties. Machine learning algorithms developed by Geoplat provide a powerful workflow for interactive and intelligent data conditioning of post stack seismic data. It enables getting instant results considerably saving time on defining functions and workflows.
and predicting pay zone properties. Machine learning algorithms developed by Geoplat provide a powerful workflow for interactive and intelligent data conditioning of post stack seismic data. It enables getting instant results considerably saving time on defining functions and workflows.
Increase seismic image quality
Improve the signal to noise ratio while maintaining vertical resolution
Reduce time spent on parameterization
The Challenge
A desired result of seismic data processing supposed to be geologically plausible, be balanced both horizontally and vertically and have a high signal to noise ratio.
There are many different filtering techniques available to address various types of noise and to improve the quality of seismic data in general. However, dealing with filter settings is time consuming and often gives insignificant improvements in results. Changing the settings may also distort seismic amplitude and frequency spectrums. Thus, selecting optimal post stack procedures in each specific case is a common challenge that is hard to tackle.
A desired result of seismic data processing supposed to be geologically plausible, be balanced both horizontally and vertically and have a high signal to noise ratio.
There are many different filtering techniques available to address various types of noise and to improve the quality of seismic data in general. However, dealing with filter settings is time consuming and often gives insignificant improvements in results. Changing the settings may also distort seismic amplitude and frequency spectrums. Thus, selecting optimal post stack procedures in each specific case is a common challenge that is hard to tackle.
Our Solution
Geoplat AI allows to clean a dataset using machine learning capabilities. Our new generation convolutional neural network is trained on hundreds of thousands of unique synthetic datasets covering a large number of noise effects and types causing artificial seismic responses.
The trained neural network makes it possible to improve the quality of seismic data while maintaining the correct amplitude ratio: amplifying the visual display of fault zones, removing noise, restoring "removed" reflections, smoothing effects, etc.
Geoplat AI allows to clean a dataset using machine learning capabilities. Our new generation convolutional neural network is trained on hundreds of thousands of unique synthetic datasets covering a large number of noise effects and types causing artificial seismic responses.
The trained neural network makes it possible to improve the quality of seismic data while maintaining the correct amplitude ratio: amplifying the visual display of fault zones, removing noise, restoring "removed" reflections, smoothing effects, etc.
Application
— Removing irregular noise.
— Improving the signal to noise ratio.
— Removing irregular noise.
— Improving the signal to noise ratio.
Mean Seismic Conditioning
Poor quality of the seismic data often makes it challenging to interpret various structural features and hence significantly reduces predictive power of the following geological model. Our new neural network for seismic data conditioning helps to improve imaging and visibility of fault zones and other geological objects by means of suppressing incoherent noise and enhancing signal to noise ratio.
Highlight fault zones
Increase seismic image quality
Improve the signal to noise ratio while
maintaining vertical resolution
maintaining vertical resolution
Reduce time spent on parameterization
The Challenge
When interpreting seismic data, one of the crucial goals is to determine the structural framework of the surveyed area. Often, this phase can be complicated by poor data quality, noisy intervals, and areas with low amplitudes.
Existing analytical methods in the conventional approach to structural interpretation frequently yield noisy results without providing detailed insights into objects. Sometimes, especially in more complex geological conditions, these methods fail to produce meaningful outcomes altogether.
When interpreting seismic data, one of the crucial goals is to determine the structural framework of the surveyed area. Often, this phase can be complicated by poor data quality, noisy intervals, and areas with low amplitudes.
Existing analytical methods in the conventional approach to structural interpretation frequently yield noisy results without providing detailed insights into objects. Sometimes, especially in more complex geological conditions, these methods fail to produce meaningful outcomes altogether.
Our Solution
Geoplat AI enables the conditioning of seismic data using machine learning techniques. The training algorithm of the convolutional neural network closely resembles the earlier-described approach for structural noise reduction. In this neural network, a substantial amount of unique synthetic data was also used for training. An important distinction lies in the intermediate step – the construction of the specified seismic horizon interpretation volume under the hood – LGT volume – and hence helps to improve fault visibility.
By incorporating this volume into the neural network training, we've managed to preserve and accentuate structural features during calculations. This normalization of amplitudes and enhancement of fault zones are achieved. This conditioning algorithm significantly reduces the time required for identifying fault trajectories and facilitates both manual and automatic correlation of seismic horizons.
Geoplat AI enables the conditioning of seismic data using machine learning techniques. The training algorithm of the convolutional neural network closely resembles the earlier-described approach for structural noise reduction. In this neural network, a substantial amount of unique synthetic data was also used for training. An important distinction lies in the intermediate step – the construction of the specified seismic horizon interpretation volume under the hood – LGT volume – and hence helps to improve fault visibility.
By incorporating this volume into the neural network training, we've managed to preserve and accentuate structural features during calculations. This normalization of amplitudes and enhancement of fault zones are achieved. This conditioning algorithm significantly reduces the time required for identifying fault trajectories and facilitates both manual and automatic correlation of seismic horizons.
Application
— Highlighting structural features and fault zones.
— Removing irregular noise.
— Improving the signal to noise ratio.
— Highlighting structural features and fault zones.
— Removing irregular noise.
— Improving the signal to noise ratio.
High Resolution Seismic Conditioning
One of the issues that can come with seismic data beside poor quality is also low resolution of the data. It is crucial to determine reservoir intervals and be able to obtain detailed stratigraphic models in order to improve quality of final reservoir models. New powerful neural network made by Geoplat AI allows to enhance seismic data resolution along with improving overall data quality
Enhance data resolution
Increase seismic image quality
Improve the signal to noise ratio while
maintaining vertical resolution
maintaining vertical resolution
Reduce time spent on parameterization
The Challenge
During the construction of structural and stratigraphic models under complex geological conditions, there's often a need for a more detailed interpretation of the section and the delineation of thinner bodies boundaries of thinner bodies. However, the resolution of seismic data frequently falls short of facilitating this.
Presently, there is a limited number of analytical methods for addressing this challenge. However, they all come with constraints and do not consistently provide a significant enhancement in resolution. Moreover, these methods tend to require substantial computation time.
During the construction of structural and stratigraphic models under complex geological conditions, there's often a need for a more detailed interpretation of the section and the delineation of thinner bodies boundaries of thinner bodies. However, the resolution of seismic data frequently falls short of facilitating this.
Presently, there is a limited number of analytical methods for addressing this challenge. However, they all come with constraints and do not consistently provide a significant enhancement in resolution. Moreover, these methods tend to require substantial computation time.
Our Solution
Geoplat AI enables the conditioning of original seismic data using machine learning methods. The algorithm, like other our conditioning techniques, was trained on synthetic data. However, a distinctive feature here is the utilization of data with varying frequencies.
This trained neural network is capable of enhancing the resolution of seismic data, enabling a more detailed observation of thin layers. These thin layers become more pronounced through this conditioning, resulting in a more detailed representation of the structural characteristics of the surveyed area.
Geoplat AI enables the conditioning of original seismic data using machine learning methods. The algorithm, like other our conditioning techniques, was trained on synthetic data. However, a distinctive feature here is the utilization of data with varying frequencies.
This trained neural network is capable of enhancing the resolution of seismic data, enabling a more detailed observation of thin layers. These thin layers become more pronounced through this conditioning, resulting in a more detailed representation of the structural characteristics of the surveyed area.
Application
— Enhancement of resolution of the seismic data.
— Removing irregular noise.
— Improving the signal to noise ratio.
— Enhancement of resolution of the seismic data.
— Removing irregular noise.
— Improving the signal to noise ratio.
Fault Interpretation
Fault interpretation is one of the most difficult tasks within a structural interpretation workflow. Geoplat developed the technology can help to significantly reduce time and resources spent on building a geological model. The use of machine learning based on deep neural networks allows to calculate fault probability distribution, extract surfaces, and eliminate interpretation uncertainties.
Better results on poor quality seismic data compared to conventional techniques
Interpretation results can be used to define the regional structural concept
Interactive fault surface interpretation and QC
Interpret different fault types present in challenging geological environments
The Challenge
Commonly, creating a fault model and tracing faults is a very time-consuming process which requires a detailed understanding of geological characteristics of an area. An interpreter often has to run automatic interpretation workflows with multiple iterations depending on various geological and physical factors.
However, the capabilities of existing fault auto tracking techniques have serious limitations. Some challenges around automatic interpretation are still not resolved: poor data quality, regional and local discontinuity, volume size, reverse or listric faults.
Commonly, creating a fault model and tracing faults is a very time-consuming process which requires a detailed understanding of geological characteristics of an area. An interpreter often has to run automatic interpretation workflows with multiple iterations depending on various geological and physical factors.
However, the capabilities of existing fault auto tracking techniques have serious limitations. Some challenges around automatic interpretation are still not resolved: poor data quality, regional and local discontinuity, volume size, reverse or listric faults.
Our Solution
Geoplat AI calculates image segmentation based on previously trained neural network datasets.
Our new generation convolutional neural network is trained on data samples covering multiple fault segments. During the training process, the neural network generates a fault database that includes presence signs and identifies presence patterns in geological structures.
The initial seismic volume is automatically divided into sets of segments by Geoplat AI. The probability of fault presence at each segment point is automatically calculated based on a set of features generated during the training process.
There is also an option to customise the neural network by training it through manual fault labeling. User's input is being added to the training set to retrain the network which changes the resulting model.
Finally, the algorithm automatically acquires surface groups using the calculated probability field, enabling a user to further customise the fault extraction parameters.
Geoplat AI calculates image segmentation based on previously trained neural network datasets.
Our new generation convolutional neural network is trained on data samples covering multiple fault segments. During the training process, the neural network generates a fault database that includes presence signs and identifies presence patterns in geological structures.
The initial seismic volume is automatically divided into sets of segments by Geoplat AI. The probability of fault presence at each segment point is automatically calculated based on a set of features generated during the training process.
There is also an option to customise the neural network by training it through manual fault labeling. User's input is being added to the training set to retrain the network which changes the resulting model.
Finally, the algorithm automatically acquires surface groups using the calculated probability field, enabling a user to further customise the fault extraction parameters.
Application
— Obtaining a detailed fault
model framework.
— Generating a fault probability field from the data that highlights fault distribution for all disconnected types within the entire seismic dataset.
— Automatically extracting fault surfaces.
— Obtaining a detailed fault
model framework.
— Generating a fault probability field from the data that highlights fault distribution for all disconnected types within the entire seismic dataset.
— Automatically extracting fault surfaces.
Horizon Interpretation
Horizon interpretation is the core process in understanding the structural features of a geological profile and conduct reliable dynamic seismic analysis. Geoplat offers a new approach to address automatic horizon tracing. You can trace a single horizon or the whole set, preserving complex fault structures and regional geological features.
Better results even with poor quality of seismic data
Improved level of
structural detail
structural detail
Accurate tracing of
different fault types
different fault types
Automatic interpretation of structurally complex horizons
The Challenge
The quality of horizon tracking is one of the key challenges in seismic interpretation.
It is also one of the most time-consuming tasks.
Nowadays there is a large number of auto tracking algorithms available. However, addressing a structural interpretation workflow in a complex geological environment still poses considerable technological limitations. This drives both project timings and costs up significantly.
Our Solution
Geoplat AI allows to clean a dataset using machine learning capabilities. Our new generation convolutional neural network is trained on hundreds of thousands of unique synthetic datasets covering a large number of noise effects and types causing artificial seismic responses.
The trained neural network helps to improve the seismic data quality while maintaining the correct amplitude ratio: amplifying the visual display of fault zones, removing noise, restoring 'removed' reflections, smoothing effects, etc.
The algorithm picks the contours corresponding to the horizons on the predicted LGT (local geological time) section.
It provides an effective solution to the problem of auto tracking on different kinds of seismic data, preserving input data specifics (including multiple fault zones and complex horizons).
Geoplat AI allows to clean a dataset using machine learning capabilities. Our new generation convolutional neural network is trained on hundreds of thousands of unique synthetic datasets covering a large number of noise effects and types causing artificial seismic responses.
The trained neural network helps to improve the seismic data quality while maintaining the correct amplitude ratio: amplifying the visual display of fault zones, removing noise, restoring 'removed' reflections, smoothing effects, etc.
The algorithm picks the contours corresponding to the horizons on the predicted LGT (local geological time) section.
It provides an effective solution to the problem of auto tracking on different kinds of seismic data, preserving input data specifics (including multiple fault zones and complex horizons).
Application
— Automated interpretation of seismic horizons using the following signal features: peaks, troughs, zero crossings.
— Automated tracking of stratigraphic horizon sets within a selected interval.
— Automated interpretation of seismic horizons using the following signal features: peaks, troughs, zero crossings.
— Automated tracking of stratigraphic horizon sets within a selected interval.
Salt Bodies Delineation
Common interpretation methods are usually unable to determine the exact positioning of salt body's boundaries including its top and base. Our innovative machine learning approach helps to generate a unique volume attribute that predicts the distribution areas of salt layers.
Reduction of misinterpretation
resulting from human bias
resulting from human bias
Automatic extraction of salt boundaries, including the top and base
Additional seismic attributes to QC salt interpretation
The Challenge
Accurate salt body interpretation (and other similar stratigraphic objects) usually presents a complex challenge for geoscientists. Extreme dip values, doubtful horizon interpretation (including multi-Z cases), velocity anomalies and poor reflectivity data - all these factors contribute to uncertainties in interpretation.
Our Solution
Geoplat AI generates a salt probability attribute volume based on our
machine learning algorithm.
The algorithm uses a neural network trained on real data. The labelled data
generates a reference dataset that outlines defined top and base positions of
a salt body accurately.
Our new generation multilayer neural network compares fragmented data areas during the training with the desired features. It then assigns the salt probabilities to the original volume segments.
Geoplat AI machine learning algorithm divides the source data into segments, so the trained convolutional network applies a similar procedure and builds a 3D model.
Geoplat AI generates a salt probability attribute volume based on our
machine learning algorithm.
The algorithm uses a neural network trained on real data. The labelled data
generates a reference dataset that outlines defined top and base positions of
a salt body accurately.
Our new generation multilayer neural network compares fragmented data areas during the training with the desired features. It then assigns the salt probabilities to the original volume segments.
Geoplat AI machine learning algorithm divides the source data into segments, so the trained convolutional network applies a similar procedure and builds a 3D model.
Application
— Automatic horizon interpretation optimised for salt build-ups stratigraphy.
— Stratigraphic horizon slicing.
— Automatic horizon interpretation optimised for salt build-ups stratigraphy.
— Stratigraphic horizon slicing.
Paleo-Channels Detection
The problem of extracting subtle changes from the reflectivity data is often associated with various types of sedimentation responses. Sometimes there is insufficient detail to detect the entire object within the interval formed in the same geological period. Geoplat AI can generate a complete probabilistic model to detect channels and other geological objects in the entire seismic volume.
Ability to detect geobodies using seismic data only, without additional attributes
A unified model predicting channels, sand bodies, fans, injectites etc.
Object detection regardless
of reflectivity expression
of reflectivity expression
The Challenge
In the typical geoscience interpretation workflow, the task of identifying geobodies in the seismic data (channel bodies, bars, fans, etc.) could take a considerable amount of time, depending on data quality and geological uncertainty.
In the typical geoscience interpretation workflow, the task of identifying geobodies in the seismic data (channel bodies, bars, fans, etc.) could take a considerable amount of time, depending on data quality and geological uncertainty.
Traditional multi-attribute analysis techniques combined with RGB blending (or other blending variations) allow to identify geological objects in the seismic volume with a reasonable degree of accuracy. However, this approach has major disadvantages, such as false probabilities and the need to run complex and often
time-consuming attribute calculations.
Our Solution
Geoplat AI provides automatic identification of volume attributes that contain values of geobody's probability distribution.
The main principle is similar to the salt body extraction workflow: it uses a new generation multilayer convolutional neural network which supports a deep learning process on the pre labeled data samples to identify specific objects
like channel bodies.
Based on Geoplat's technology team experience, this algorithm helps to evaluate areas of interest even in extremely large seismic volumes. It can also be used as a QC tool.
Geoplat AI provides automatic identification of volume attributes that contain values of geobody's probability distribution.
The main principle is similar to the salt body extraction workflow: it uses a new generation multilayer convolutional neural network which supports a deep learning process on the pre labeled data samples to identify specific objects
like channel bodies.
Based on Geoplat's technology team experience, this algorithm helps to evaluate areas of interest even in extremely large seismic volumes. It can also be used as a QC tool.
Application
— Probabilistic predictive modelling for channels, sand bodies and other geological objects.
— Detailed interactive interpretation.
— Automatic geobody extraction.
— Probabilistic predictive modelling for channels, sand bodies and other geological objects.
— Detailed interactive interpretation.
— Automatic geobody extraction.
Book a Demo
You agree with our Terms and Conditions