Patent Description:
One technique to resolve these issues is to perform surface wave analysis. During the analysis, dispersive velocity characteristics of the surface waves are extracted, and may then be exploited for noise modelling and inversion to build near-surface velocity models for imaging. The first step in this methodology is the analysis of the dispersive properties, e.g., frequency-phase velocity relationship, of the surface wave energy as it travels through the near-surface. Such analysis facilitates gaining an understanding of how the phase velocity of the surface waves changes with frequency over the full survey area.

As part of the analysis, frequency-wavenumber (F-K) semblances are created at each analysis location. These semblances are then interpreted to extract the frequency-phase velocity curves, referred to as dispersion curves, of the different surface wave modes across the survey. On completion of the analysis, the resulting dispersion curves can then be used for both modelling and removal of the complex surface wave noise as well as building a near-surface velocity model, through surface wave inversion, which is used for seismic imaging.

Surface wave analysis, however, involves intensive human parameterization of the picking and tracking of the dispersion curves within the semblance volumes.

<NPL> describes the training of algorithms on three distinguishing features of surface-wave dispersion curves in the k-w domain and finds that kernel-based support vector machine algorithms give the highest accuracy in predicting the surface-wave window in comparison to neural networks and logistic regression.

The present invention resides in a computer-implemented method for modeling a subterranean volume as defined in claim <NUM>, a non-transitory computer-readable medium as defined in claim <NUM>, a computing system as defined in claim <NUM> and a computer program as defined in claim <NUM>.

The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms "includes," "including," "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term "if" may be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context.

<FIG> illustrate simplified, schematic views of oilfield <NUM> having subterranean formation <NUM> containing reservoir <NUM> therein in accordance with implementations of various technologies and techniques described herein. <FIG> illustrates a survey operation being performed by a survey tool, such as seismic truck <NUM>, to measure properties of the subterranean formation. The survey operation is a seismic survey operation for producing sound vibrations. In <FIG>, one such sound vibration, e.g., sound vibration <NUM> generated by source <NUM>, reflects off horizons <NUM> in earth formation <NUM>. A set of sound vibrations is received by sensors, such as geophone-receivers <NUM>, situated on the earth's surface. The data received <NUM> is provided as input data to a computer <NUM> of a seismic truck <NUM>, and responsive to the input data, computer <NUM> generates seismic data output <NUM>. This seismic data output may be stored, transmitted or further processed as desired, for example, by data reduction.

<FIG> illustrates a drilling operation being performed by drilling tools <NUM> suspended by rig <NUM> and advanced into subterranean formations <NUM> to form wellbore <NUM>. Mud pit <NUM> is used to draw drilling mud into the drilling tools via flow line <NUM> for circulating drilling mud down through the drilling tools, then up wellbore <NUM> and back to the surface. The drilling mud is typically filtered and returned to the mud pit. A circulating system may be used for storing, controlling, or filtering the flowing drilling mud. The drilling tools are advanced into subterranean formations <NUM> to reach reservoir <NUM>. Each well may target one or more reservoirs. The drilling tools are adapted for measuring downhole properties using logging while drilling tools. The logging while drilling tools may also be adapted for taking core sample <NUM> as shown.

Typically, the wellbore is drilled according to a drilling plan that is established prior to drilling. The drilling plan typically sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also need adjustment as new information is collected.

Surface unit <NUM> may include transceiver <NUM> to allow communications between surface unit <NUM> and various portions of the oilfield <NUM> or other locations. Surface unit <NUM> may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield <NUM>. Surface unit <NUM> may then send command signals to oilfield <NUM> in response to data received. Surface unit <NUM> may receive commands via transceiver <NUM> or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, oilfield <NUM> may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.

As shown, the sensor (S) may be positioned in production tool <NUM> or associated equipment, such as Christmas tree <NUM>, gathering network <NUM>, surface facility <NUM>, and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation.

Data plots <NUM>-<NUM> are examples of static data plots that may be generated by data acquisition tools <NUM>-<NUM>, respectively; however, it should be understood that data plots <NUM>-<NUM> may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.

Static data plot <NUM> is a seismic two-way response over a period of time. Static plot <NUM> is core sample data measured from a core sample of the formation <NUM>. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Static data plot <NUM> is a logging trace that typically provides a resistivity or other measurement of the formation at various depths.

A production decline curve or graph <NUM> is a dynamic data plot of the fluid flow rate over time. The production decline curve typically provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc..

While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield <NUM> may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, typically below the water line, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in oilfield <NUM>, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis.

The data collected from various sources, such as the data acquisition tools of <FIG>, may then be processed and/or evaluated. Typically, seismic data displayed in static data plot <NUM> from data acquisition tool <NUM> is used by a geophysicist to determine characteristics of the subterranean formations and features. The core data shown in static plot <NUM> and/or log data from well log <NUM> are typically used by a geologist to determine various characteristics of the subterranean formation. The production data from graph <NUM> is typically used by the reservoir engineer to determine fluid flow reservoir characteristics. The data analyzed by the geologist, geophysicist and the reservoir engineer may be analyzed using modeling techniques.

<FIG> illustrates an oilfield <NUM> for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the oilfield has a plurality of wellsites <NUM> operatively connected to central processing facility <NUM>. The oilfield configuration of <FIG> is not intended to limit the scope of the oilfield application system. Part, or all, of the oilfield may be on land and/or sea. Also, while a single oilfield with a single processing facility and a plurality of wellsites is depicted, any combination of one or more oilfields, one or more processing facilities and one or more wellsites may be present.

Attention is now directed to <FIG>, which illustrates a side view of a marine-based survey <NUM> of a subterranean subsurface <NUM> in accordance with one or more implementations of various techniques described herein. Subsurface <NUM> includes seafloor surface <NUM>. Seismic sources <NUM> may include marine sources such as vibroseis or airguns, which may propagate seismic waves <NUM> (e.g., energy signals) into the Earth over an extended period of time or at a nearly instantaneous energy provided by impulsive sources. The seismic waves may be propagated by marine sources as a frequency sweep signal. For example, marine sources of the vibroseis type may initially emit a seismic wave at a low frequency (e.g., <NUM>) and increase the seismic wave to a high frequency (e.g., <NUM>-<NUM>) over time.

The component(s) of the seismic waves <NUM> may be reflected and converted by seafloor surface <NUM> (i.e., reflector), and seismic wave reflections <NUM> may be received by a plurality of seismic receivers <NUM>. Seismic receivers <NUM> may be disposed on a plurality of streamers (i.e., streamer array <NUM>). The seismic receivers <NUM> may generate electrical signals representative of the received seismic wave reflections <NUM>. The electrical signals may be embedded with information regarding the subsurface <NUM> and captured as a record of seismic data.

In one implementation, seismic wave reflections <NUM> may travel upward and reach the water/air interface at the water surface <NUM>, a portion of reflections <NUM> may then reflect downward again (i.e., sea-surface ghost waves <NUM>) and be received by the plurality of seismic receivers <NUM>. The sea-surface ghost waves <NUM> may be referred to as surface multiples. The point on the water surface <NUM> at which the wave is reflected downward is generally referred to as the downward reflection point.

The electrical signals may be transmitted to a vessel <NUM> via transmission cables, wireless communication or the like. The vessel <NUM> may then transmit the electrical signals to a data processing center. Alternatively, the vessel <NUM> may include an onboard computer capable of processing the electrical signals (i.e., seismic data). Those skilled in the art having the benefit of this disclosure will appreciate that this illustration is highly idealized. For instance, surveys may be of formations deep beneath the surface. The formations may typically include multiple reflectors, some of which may include dipping events, and may generate multiple reflections (including wave conversion) for receipt by the seismic receivers <NUM>. In one implementation, the seismic data may be processed to generate a seismic image of the subsurface <NUM>.

Marine seismic acquisition systems tow each streamer in streamer array <NUM> at the same depth (e.g., <NUM>-<NUM>). However, marine based survey <NUM> may tow each streamer in streamer array <NUM> at different depths such that seismic data may be acquired and processed in a manner that avoids the effects of destructive interference due to sea-surface ghost waves. For instance, marine-based survey <NUM> of <FIG> illustrates eight streamers towed by vessel <NUM> at eight different depths. The depth of each streamer may be controlled and maintained using the birds disposed on each streamer.

The quality of onshore seismic data may be affected by characteristics of the near-surface. To compensate for the distortion of travel times of seismic energy, workflows have been formulated to analyze, model, and invert surface waves. Such approaches generally include human labor-intensive picking of high energy modes on conditioned semblances that represent each analysis location on the dispersion survey. Embodiments of the present disclosure implement a globally trained machine learning model to extract one or more surface wave modes (e.g., the fundamental mode, i.e., the mode with the highest surface wave energy), minimizing or potentially avoiding human intervention.

Surface wave analysis is generally conducted in a two-stage approach. The first stage is to create high-resolution semblances in a frequency-wavenumber (F-K) domain at both source and receiver locations, which provide a representation of the various surface wave modes in F-K domain. For example, <FIG> illustrates seismic data in an offset-time domain, which is converted to a semblance in the F-K domain, as shown in <FIG>. The second stage involves picking F-K pairs for the dominant modes (e.g., the fundamental mode) from the F-K semblance. A peak picker may be used, constrained by a combination of velocity and frequency zones that are determined interactively, to pick the F-K pairs (<FIG>). Once the main modes are picked, the resulting F-K pairs are consolidated into a single mode track, which may then be ordered, so that the picks within a single track are consistently numbered at associated analysis locations, e.g., with the fundamental mode numbered as <NUM>. This ordering is used for the subsequent noise modelling step, as these track-order numbers directly map to the order of the surface wave noise removal.

A challenge is presented by the presence of aliased surface wave modes, the close proximity of modes to one another and the individual modes exhibiting incoherency, i.e. not one single continuous energy train in the F-K semblance. These issues may occur in combination, and are further exacerbated if there is lateral variation over a short distance, and may call for high levels of human intervention and parameter tuning to achieve satisfactory picking.

<FIG> illustrates a flowchart of a method <NUM> for seismic processing, according to an embodiment. In particular, for example, the method <NUM> may be used for surface wave analysis, which may be used to generate a surface wave noise model and/or velocity model, e.g., stemming from surface waves propagating at or near the surface. These models may, in turn, be used to attenuate noise model and/or subsurface seismic imaging. The method <NUM> may leverage machine learning to automate the extraction of wave modes from the F-K semblance, thereby avoiding at least some of the labor-intensive parameterization tasks discussed above.

Since the method <NUM> implements a machine learning model, there may generally be two stages: a training stage <NUM> and an implementation stage <NUM>, each of which includes potentially several worksteps. In practice, there may not be a bright line distinction between these two stages <NUM>, <NUM>, at least because the machine learning model may be trained during the implementation stage <NUM>, e.g., based on user feedback (supervised learning) or self-learning/behavior pattern identification (e.g., clustering or another form of unsupervised learning). Further, the training stage <NUM> is described herein by way of example as using a supervised learning technique, capitalizing on a (relatively) small data set of labeled input; however, it will be appreciated that this supervised learning may be supplemented or replaced, in some embodiments, with unsupervised learning, e.g., clustering.

In general, the training stage <NUM> may include receiving seismic training data, as at <NUM>. The seismic training data may include signals recorded by geophones or other seismic receiving devices in a field, or synthetic (computer-generated) seismic data representing a simulated survey. In an embodiment, the seismic training data may include a plurality of seismic signals, e.g., in a time/space domain. For example, offset and time may be plotted for the seismic signals. The seismic data may then be converted (transformed) to a frequency-wavenumber (F-K) domain, and a semblance may be generated which generally represents coherency of the signals at the different locations in the F-K domain. These semblances may be relatively high-resolution and may be created at both source and receiver locations in the survey. As will be described, according to an example, in greater detail below, the training stage <NUM> may then train the machine learning model to extract dispersion curves, each representing a wave mode in the semblance. For example, the machine learning model may be implemented to recognize visible features and create a non-linear function that predicts the presence of dispersion curves (e.g., as a set of pixels in the semblance), labels the dispersion curves, and outputs the labels, e.g., in a text file that identifies locations (frequency/wave number pairings) in the semblance that represent the dispersion curves.

Once the machine learning model is (at least partially) trained, i.e., the training stage <NUM> is complete, the method <NUM> may proceed to the implementation stage <NUM>. The implementation stage <NUM> includes receiving seismic input data, e.g., seismic signals collected as part of a survey and, e.g., in a time/space domain, such as an offset-time domain. A semblance in the frequency-wavenumber domain, representing signal coherence in the seismic data, is then generated as at <NUM>. From this semblance, one or more dispersion curves representing one or more wave modes in the semblance are extracted (e.g., identified, labeled, etc.) by the machine learning model, as at <NUM>. Since the conditioned semblances are normalized to have amplitude values in [<NUM>, <NUM>], preprocessing may be omitted. Image rotation may also not be applied so as to retain the information about the positioning of the modes relative to the entire semblance panel and the higher modes of energy.

In some embodiments, the dispersion curve for the fundamental mode may be identified, but in other embodiments, multiple dispersion curves for multiple wave modes may be extracted. Accordingly, in the latter embodiments, the dispersion curves may be ordered, as at <NUM>, e.g., in terms of frequency, energy, or any other parameter that may prove useful in subsequent processing application. The method <NUM> then integrates the extraction of the dispersion curves in the semblance into further processing, which assists seismologists and other users to generate models of the subsurface domain, and to make decisions about wellbore locations, well trajectories, probabilities of subsurface reservoir locations, or any other technical field for which seismic data is applicable. The method <NUM> includes generating a model representing surface wave propagation based at least in part on the extracted dispersion curves, as at <NUM>. This model may then be used to attenuate the surface wave noise, as at <NUM>, or for building velocity models, imaging, etc..

<FIG> illustrates a flowchart of the training stage <NUM> of the method <NUM>, according to an embodiment. As mentioned above, the training stage <NUM> may include receiving seismic training data as input at <NUM>. The seismic input may include or be selected to provide a collection of dispersion volumes from diverse geographic regions that cover at least some near-surface conditions. This seismic training data may form the basis for the subsequent building/training of the networks that may make up the machine learning model, providing the machine learning model with sufficient "knowledge" to reliably extract dispersion curves as well as, if not better than, and potentially more efficiently than, human experts.

For example, as also discussed above, training at <NUM> may include generating semblances representing signal coherence in the seismic training data in the frequency-wavenumber domain, as at <NUM>. An example of such a semblance is provided in <FIG>.

Next, training at <NUM> may include generating binary masks for the semblances by receiving picks of locations in the semblance that represent part of a dispersion curve of a wave mode, as at <NUM>. <FIG> illustrates an example of such a binary mask. Generating the binary masks may be based at least partially on manual input, e.g., the "picks. " Specifically, a user may be presented with the semblance and may select (e.g., pick, using a mouse cursor or another input device) locations/pixels associated with a dispersion curve. In the example of <FIG>, the semblance <NUM> includes a dispersion curve <NUM>, which is identified by relatively sparse picks (represented as individual points) <NUM> in <FIG>.

The user-selected points <NUM> on the dispersion curve <NUM> may be relatively sparse (e.g., the user may not be called upon to select every pixel associated with the dispersion curve in the visualization of the semblance). Further, the manual picks <NUM> may have gaps of varying lengths therebetween that might introduce inconsistency in the learning process, if not rectified. Accordingly, the binary mask may be augmented through interpolation of the labels, as at <NUM>. The interpolation may connect together the picks, and also identify the beginning and end of a given dispersion curve, e.g., near to the most extreme selected locations of the given curve, e.g., based on the semblance data associated with these locations. <FIG> illustrates the binary mask after the picks after such interpolation at <NUM>.

Accordingly, the binary mask and the semblance combination may provide a plurality of training couples, e.g., the picks (and/or interpolations thereof). The picks/interpolation points may be identified as Fy with a corresponding semblance location Sx. Any number of training couples may be provided, e.g., tens, hundreds, or thousands thereof, as the training corpus for the machine learning model. Furthermore, some of the training pairs may be based on the same underlying data. For example, to supplement what may be a relatively small number of training pairs, the semblance may be shifted vertically or horizontally by a distance in the frequency-wavenumber domain, with the mask likewise shifted.

Next, the training at <NUM> may proceed to the actual building of the layers of the machine-learning model. For example, training at <NUM> may include training an encoder network and a decoder network to extract the dispersion curves from semblance data, as at <NUM>. The machine learning model may perceive the semblance input Sx as a set of visual features and applies the learned rules that capture the spatial visual dependencies on a given semblance to the predict one or more modes. In this way, the modes may be annotated for each pixel of the semblance panel throughout the dispersion volume, making the picking process a binary segmentation task.

In an embodiment, the machine learning model may be a global fundamental picker that is a program based on tensors and is structured as a U-Net encoder-decoder residual convolutional neural network, for example, as schematically depicted in <FIG>. Accordingly, as shown in <FIG>, the machine learning model <NUM> includes an encoder network <NUM> and a decoder network <NUM>, with a blottleneck <NUM> therebetween. The encoder network <NUM> includes initialized tensors that encode the spatial features of the input semblance as strided convolution down-samples thereof. The decoder network <NUM> takes some (e.g., relatively impactful) reduced low-level features when delineating a mode (e.g., the fundamental) on a semblance and constructs a segmentation mask at the same scale as the input using a series of transposed convolutions, padding pixels between the reduced features.

The encoder-decoder architecture is symmetrical, and thus features extracted in one encoding layer (e.g., layer <NUM>) can be concatenated to its corresponding decoding layer (e.g., layer <NUM>) using long skip connections <NUM>. These connections <NUM> may help propagate small/midscale features or signals through the machine learning model <NUM>. Thus, a largescale feature learned in the model <NUM> can help form a multiscale feature set at a later part of the network. In addition to long skip connections, blocks in the encoder and decoder networks <NUM>, <NUM> may include short skip connections that include gating identity mapping connection and full pre-activation residual units to although marginally increase the training error but significantly reducing the test error.

Overall, the architecture of the machine learning model <NUM> may be considered a U-Net backbone with residual blocks, built to handle multiscale features. This architecture may be selected based on a comprehensive empirical comparitive study of performance with and without the residual blocks.

During the training process, the weights of the connections between neurons in the architecture may be adjusted as the method <NUM> parses sample semblances and attempts to reduce a loss between the predicted fundamental (Fx) (or any other mode) and the target fundamental (Fy) (or, correspondingly, any other mode). The loss function may be a Dice Loss, as shown, that measures the overlap between the predicted and the label matrices. The values associated with the network connections are adjusted based on the scale of the loss.

Being fully convolutional, this architecture may be invariant to translation and morphology, so it can learn to pick the modes that may vary in size, resolution, degree of slope, and may be continuous or fragmented. The model <NUM> may, for example, be trained with a batch size of <NUM> samples, and <NUM> percent of the training data may be set aside as validation samples. After each epoch, the gradients are updated according to the Nesterov Adam optimizer, for example, and performance on the validation samples may be monitored.

The learning rate may be made to decay by a factor, e.g., of <NUM> when the loss on validation samples stagnates but has a minimum slab at <NUM> x <NUM>-<NUM>. On monitoring the statistics of the training (<FIG>), the network converges at about <NUM> epochs as can be seen by the stabilizing validation accuracy and training loss. In order to study the consistency and stability of the architecture with tuned hyperparameters, five-fold cross-validation may be done on data by splitting it in five equal parts with five iterations of holding out one part for validation and training of the remaining four. As <FIG> shows, the study concluded that the architecture is quite stable with an average accuracy of <NUM> and average loss of <NUM>.

Once cross validation determines the architecture in its tuned form to be stable, it is retrained on the entire training dataset. During inference, semblances from any survey unseen during the training process can be made to pass through this calibrated neural network architecture and its associated trained weights to obtain a prediction of the fundamental. Occasionally the machine may pick very small segments from the higher modes given their high amplitude and close proximity to the fundamental, and these segments can be removed using DBSCAN, as a method of clustering the points in the predicted fundamental mask that jointly form the fundamental mode separate from all other noisy segments. The cluster that contains the noisy segments can be isolated and removed from the predictions.

The effectiveness of an emboidment of the present method, including the global fundamental picker, was validated on a 3D land survey, located in the Cooper Basin in South Australia. No training data from this region was used as part of training and the generalization capability of the machine learning architecture may improve as more diverse sets of surveys are appended to the training data. The validation involved the application of the global picker to <NUM>,<NUM> analysis locations followed by comparison against the already available human intensive non-ML picks. The ML tool provided approximately <NUM>% more picks than the non-ML picks over a frequency range of <NUM> to <NUM> and achieved this in just <NUM> hours versus <NUM>-<NUM> hours (including testing) for the established approach. The machine learning predictions consistently picked the higher frequency tails of the fundamental (<FIG>), comprehended breaks in the fundamental mode (<FIG>) and improved spatial consistency (<FIG>) adding quality improvements to the observed efficiency gains.

Accordingly, it will be appreciated that embodiments of the present dsiclosure may provide a robust and automated machine learning driven approach for dispersion curve picking that provides the opportunity to increase efficiency and reduce the turn-around time of the surface wave analysis phase, e.g., by reducing human interaction.

In one or more embodiments, the functions described can be implemented in hardware, software, firmware, or any combination thereof. For a software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, subprograms, programs, routines, subroutines, modules, software packages, classes, and so on) that perform the functions described herein. A module can be coupled to another module or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, or the like can be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, token passing, network transmission, and the like. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

The methods of the present disclosure are executed by a computing system. <FIG> illustrates an example of such a computing system <NUM>, in accordance with some embodiments. The computing system <NUM> may include a computer or computer system 1101A, which may be an individual computer system 1101A or an arrangement of distributed computer systems. The computer system 1101A includes one or more analysis module(s) <NUM> configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module <NUM> executes independently, or in coordination with, one or more processors <NUM>, which is (or are) connected to one or more storage media <NUM>. The processor(s) <NUM> is (or are) also connected to a network interface <NUM> to allow the computer system 1101A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 1101B, 1101C, and/or 1101D (note that computer systems 1101B, 1101C and/or 1101D may or may not share the same architecture as computer system 1101A, and may be located in different physical locations, e.g., computer systems 1101A and 1101B may be located in a processing facility, while in communication with one or more computer systems such as 1101C and/or 1101D that are located in one or more data centers, and/or located in varying countries on different continents).

The storage media <NUM> can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of <FIG> storage media <NUM> is depicted as within computer system 1101A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 1101A and/or additional computing systems. Storage media <NUM> may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

In some embodiments, computing system <NUM> contains one or more dispersion curve extraction module(s) <NUM>. In the example of computing system <NUM>, computer system 1101A includes the dispersion curve extraction module <NUM>. In some embodiments, a single dispersion curve extraction module may be used to perform some or all aspects of one or more embodiments of the methods. In alternate embodiments, a plurality of dispersion curve extraction modules may be used to perform some or all aspects of methods.

It should be appreciated that computing system <NUM> is only one example of a computing system, and that computing system <NUM> may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of <FIG>, and/or computing system <NUM> may have a different configuration or arrangement of the components depicted in <FIG>. The various components shown in <FIG> may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

Geologic interpretations, models and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to embodiments of the present methods discussed herein. This can include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system <NUM>, <FIG>), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

Claim 1:
A computer-implemented method for modeling a subterranean volume, comprising:
receiving (<NUM>) seismic data comprising a signal;
generating (<NUM>) a semblance (<NUM>) in a frequency-wavenumber domain for the seismic data, wherein the semblance (<NUM>) represents a coherence of the signal in the frequency-wavenumber domain;
extracting (<NUM>) one or more wave energy modes in the semblance (<NUM>) using a machine learning model (<NUM>) trained to identify dispersion curves (<NUM>) in the semblance (<NUM>) based on a visible characteristic of the dispersion curves (<NUM>),
generating (<NUM>) a model representing surface wave propagation based at least in part on the identified one or more wave energy modes;
characterised in that the method further comprises:
outputting the model representing surface wave propagation for use in well planning and oilfield operations;
and in that the extracting (<NUM>) the one or more wave energy modes in the semblance (<NUM>) comprises:
using an encoder network (<NUM>) of the machine learning model (<NUM>), the encoder network (<NUM>) having initialized tensors that encode spatial features of an input semblance as strided convolution down-samples; and
using a decoder network (<NUM>) to construct a segmentation mask at a same scale as the input semblance.