WO2026010507A1 - Procédé de correction de dérive d'horloge - Google Patents

Procédé de correction de dérive d'horloge

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Publication number
WO2026010507A1
WO2026010507A1 PCT/NO2025/050121 NO2025050121W WO2026010507A1 WO 2026010507 A1 WO2026010507 A1 WO 2026010507A1 NO 2025050121 W NO2025050121 W NO 2025050121W WO 2026010507 A1 WO2026010507 A1 WO 2026010507A1
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Prior art keywords
model
drift
clock
data
environmental
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Kambiz Iranpour
Vidar Anders Husom
Hans Paulson
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Reflection Marine Norge AS
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Reflection Marine Norge AS
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Publication of WO2026010507A1 publication Critical patent/WO2026010507A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/04—Generating or distributing clock signals or signals derived directly therefrom
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/16—Receiving elements for seismic signals; Arrangements or adaptations of receiving elements
    • G01V1/18—Receiving elements, e.g. seismometer, geophone or torque detectors, for localised single point measurements

Definitions

  • the present invention relates to a method for correcting for clock drift in a signal sampling device, to a method for preparing a model set for clock drift correction, to a sampling device comprising data storage having a model set for clock drift correction stored thereon, and to a computer implemented method for correcting for clock drift of the system clocks of an array of signal sampling devices.
  • the acoustic waves are reflected at boundaries between different materials within the subsurface structure, or are refracted, and travel back towards sensors located close to the surface either within one or more streamers towed behind a survey vessel, or positioned at particular locations on the earth’s surface (such as on the seabed).
  • Seabed seismic nodes may be located within nodes or along cables.
  • the sensors sample the acoustic signal received from the sub-surface periodically and convert the received acoustic signal into a digital signal which is sent to a processor for analysis.
  • the collected signals carry with them information about the materials either side of the different boundaries, and the positions of the boundaries themselves.
  • the seismic sensors themselves can include one or more of a number of different types of device capable of detecting a seismic wave within the surrounding material (air, water, or the material of the node itself). These can include pressure sensors such as hydrophones and motion sensors such as geophones or accelerometers (for example MEMS accelerometers). Nodes can also include additional sensors for measuring a node position, orientation, temperature, for location of the node, and for monitoring of other parameters. Sensor measurements are taken periodically, and the measurements need to be associated with timing information to allow for proper analysis of the received signal.
  • atomic clocks or oscillators such as crystal oscillators or MEMS oscillators
  • crystal oscillators make use of the natural mechanical resonance of piezoelectric materials when subjected to an electric field.
  • the resonant frequency with which the piezoelectric material changes shape and then returns to its previous shape is very regular and can be used to provide a precise timing signal.
  • Atomic clocks instead make use of a natural oscillation frequency of atoms changing state. While these types of clocks are very accurate, the frequency of the clock signal does drift over time in a manner which will depend on properties of the environment where the clock is located.
  • a correction for temperature changes that have occurred during the intervening time is applied to the comparison data before the fit is carried out to correct these to a constant reference temperature.
  • This correction is based either on measurements for the specific node to determine a relationship between frequency of oscillation and temperature for the system clock within a specific node or based on a generic relationship.
  • US-A- 2021/0263477 describes a method involving fitting a polynomial of order two or greater to clock drift data measured using a time reference just before and just after deployment. The above methods make various assumptions in order to estimate a drift rate during a period of deployment of a node for which a time reference is not available. They also require a comparison of the operation of the system clock and a reference clock to be carried out for the specific node at least for a period prior to and/or post deployment. A more efficient and effective method for correcting for clock drift is therefore desired.
  • a computer implemented method for correcting for clock drift of a system clock in a signal sampling device comprising the system clock and configured to sample a signal in real time, the method comprising: obtaining data relating to an environmental parameter of an environment of the signal sampling device; matching the data relating to the environmental parameter to an environmental scenario; selecting, from a model set comprising at least two different models, a model tagged with the matched environmental scenario; and correcting for clock drift of the system clock using the selected model.
  • the sampling device can include at least one sensor for monitoring an environmental parameter (i.e., temperature, magnetic field strength, mechanical stress or any other property of the surroundings close to or sometimes even within the device) and at least one sensor for sampling the signal itself (i.e., hydrophones or geophones to sample a seismic signal if the device is a seismic node).
  • an environmental parameter i.e., temperature, magnetic field strength, mechanical stress or any other property of the surroundings close to or sometimes even within the device
  • a sensor for sampling the signal itself i.e., hydrophones or geophones to sample a seismic signal if the device is a seismic node.
  • the environmental sensor can be coupled to the device housing or located some distance away from it, provided that conditions in the vicinity of the housing (in the environment of the device) can be monitored using the sensor.
  • the sampling device includes the system clock for which drift is to be corrected, and it is this clock which is used to timestamp samples taken, by the device, of the signal. Drift correction is therefore crucial to providing an accurate representation of the signal.
  • Providing different models for different environmental scenarios, along with a way to select between these models, is a simple way to account for possible differences in operating environments for the device whilst requiring minimal additional measurements of drift to be taken in relation to the specific device.
  • Reference to the model being tagged can be to the model comprising additional metadata linked to an environmental scenario.
  • the criteria for finding a match between environmental sensor data and the environmental scenario with which a model is tagged can be pre-set, and may comprise a range of possible measured values which are to be matched with a particular scenario.
  • the criteria for matching may be whether the environmental parameter falls below or above a particular threshold over a time period, for example, or whether the parameter rises or drops more than a certain amount within a time period.
  • the measurements taken of the environmental parameter can be matched with one scenario if some particular requirement is met, and with the other if these requirements are not met.
  • Reference to the at least two models being different may be to the models being of a different type or to models being of the same type, but with optimizable parameters optimized to different values.
  • obtaining the data relating to the environmental parameter comprises collecting, by one or more environmental sensors of the signal sampling device, the data relating to the environmental parameter. Collecting the data may comprise recording the data. This ensures that the conditions in the vicinity of the specific system clock at issue are accounted for in the best possible way.
  • the method comprises receiving, from the signal sampling device, data representing the sampled signal, wherein correcting for clock drift of the system clock comprises correcting the data representing the sampled signal in a postprocessing step.
  • correcting for clock drift of the system clock comprises adjusting the frequency of the system clock during collection and time stamping, by the signal sampling device, of data representing the sampled signal.
  • the correction will be applied either during deployment or during post-processing, but it is possible to apply corrections both during deployment and during post-processing.
  • the data relating to the environmental parameter is collected at the same time as the sampled data is collected by the device. This may be during the whole or a part of the deployment period for the device. In embodiments, the data relating to the environmental parameter is collected at a different time to the sampled data (such as during a previous survey or at another time).
  • the method comprises: prior to receiving the data, preparing the model set by: 1 ) selecting a group of oscillators and calculating a frequency error at a plurality of time points throughout a test period by comparing oscillator cycle counts to a reference clock source; 2) selecting a model type comprising one or more parameters for optimization; and 3) optimizing the parameters of the model by fitting to the calculated frequency error, and repeating steps 1 to 3 for each model in the set.
  • the group of oscillators can be the same or a different group for the different models, and can be of the same or a different size.
  • the method comprises, during the test period for each model in the set, controlling at least one property of the surroundings to subject the group of oscillators to controlled environmental conditions which are different for each model in the set, and tagging the optimized model with an environmental scenario relating to the conditions applied during the test period for that model.
  • the environmental parameter may be temperature, magnetic field strength, or mechanical stress, for example.
  • the model set comprises a first model for which the group of oscillators is subjected to a constant or slowly varying property of the surroundings during the test period and a second model for which the group of oscillators is subjected to a rapid change in the property of the surroundings (a shock) during the test period, and the first model is tagged with a non-stressed scenario and the second model with a stressed scenario.
  • the “shock” may be represented as a quick or sudden change in the property of the surroundings (i.e. , the change in value of the property having a gradient above a predetermined threshold level for the stressed scenario and having a gradient below the predetermined threshold level for the nonstressed scenario).
  • the property of the surroundings may be one or more of temperature, magnetic field strength, or mechanical stress.
  • the property of the surroundings is temperature
  • the non-stressed model is a non-temperature shock model
  • the stressed model is a temperature shock model.
  • a temperature shock refers to a rapid rise or fall in temperature of a particular size within a defined time period.
  • the shock threshold can be defined as a specific temperature gradient being exceeded. The size of temperature change and/or the steepness of the temperature gradient representing the threshold should be selected based on the type of oscillator used and under what conditions the behaviour of the oscillator is likely to change significantly.
  • a temperature shock could be defined for a particular situation as a rise or fall in temperature of more than 10 degrees over a time period of less than 1 day.
  • thermo shock model relating to temperature rises or falls of different sizes and different gradients
  • one temperature shock and one non-temperature shock model provides good results if these are applied, respectively, in all situations where temperature shocks are present, and in all situations where they are not.
  • the same principle can be applied for other parameters than temperature, such as magnetic field strength or mechanical stress (a mechanical shock model and a nonmechanical shock model, for example).
  • the first model (non-stressed/non-shock) is a quadratic model and the second model (stressed/shock) is a non-quadratic non-linear model.
  • the non-temperature-shock model is a quadratic model and the temperature-shock model is a non-quadratic non-linear model.
  • the duration of the test period is at least 10 days.
  • the duration of the test period may be at least 30 days, more preferably at least 60 days, and most preferably at least 90 days. This provides an accurate model particularly suited for seismic surveys which are of roughly this duration.
  • optimizing the parameters of the model comprises minimizing max ⁇ err1 (tj), err2(tj), ... , errN(tj) ⁇ , where erri(tj) is the model error of drift for oscillator i at time j.
  • the model error is represented by the difference between the drift calculated by the model for that oscillator at that time point and the actual drift measured by comparison with the reference clock for that oscillator at that time point.
  • the plurality of time points are periodically spaced throughout the duration of the test period.
  • the time points may be evenly spaced apart.
  • the plurality of time points are spaced up to 10 seconds apart, and preferably up to 1 second apart.
  • the plurality of time points include a time point at the very start of the test period and a time point at the very end of the test period.
  • the method comprises storing the obtained data on data storage of the signal sampling device and the method comprises, prior to obtaining the data, storing the model set on the data storage of the signal sampling device.
  • the device is a seismic node.
  • the device may be a seabed seismic node.
  • Application of the model set, and preparation of the models in a lab environment, is particularly advantageous when applied to seismic nodes because, as mentioned, reducing the time required to prepare for the survey and time used post-survey is crucial in terms of cost.
  • a very accurate system clock is also required for these types of devices, and environmental factors can play an important role.
  • Using model sets which are adapted to different environmental scenarios is an efficient way to achieve an accurate correction with minimal additional time and effort required during and immediately before/after the survey itself.
  • the device is autonomous.
  • the device may be an autonomous node, such as an autonomous seismic node.
  • Autonomous refers to the fact that during a period in which signal data is being collected, there is no external communication or exchange of information between the device and any other device.
  • the device is configured to operate without communication to any other unit during a deployment period.
  • the deployment period may span the time period between deployment and retrieval for a seismic node, and may cover the entire time period during which seismic data is being collected.
  • This type of device will continue to record data, such as seismic data, and its clock will continue to run with related data being stored in a data storage unit of the device.
  • the data is stored within the device and can be downloaded once the deployment period has ended and the device is back on a survey vessel (for example).
  • the method comprises obtaining the data relating to an environmental parameter using the one or more environmental sensors of the node and obtaining seismic data as the signal data using one or more seismic sensors of the node.
  • the seismic sensors may be geophones, accelerometers, and/or hydrophones.
  • the environmental sensors may measure temperature, magnetic field strength, or mechanical stress. These environmental sensors may be positioned outside of a main node housing or inside to measure the conditions closer to the system clock itself.
  • the method of the first aspect may be carried out separately for some or each sensor device within an array of multiple devices, so that in principle a different model for clock drift correction can be selected for different devices of the array.
  • the array may comprise a plurality of autonomous sampling devices, each having their own system clock.
  • the nodes can be connected via cabling for transfer of data and/or power, the nodes can also be completely physically separate from one another, or can be connected via ropes or similar for handling only, with no data/power connection between the different nodes in the array. This allows the system to adapt to conditions which are very variable across the area over which an array is distributed.
  • selected devices which are distributed spatially throughout the array, can be used to select a model from the set which is applied to correct for drift of the system clocks of a group of devices adjacent or near to the selected device.
  • Each device in the array or each group of devices may be equipped with an environmental sensor from which the data relating to an environmental parameter of an environment of that device is obtained. If the data is very different for two devices within the array, or two groups of devices, this could result in different models being selected from the set for correction for the two devices or the two groups.
  • the system is therefore extremely adaptable to different or extreme environmental conditions, while simplifying processing in cases where complex models are not necessarily required.
  • a sampling device comprising data storage, the data storage having a model set prepared according to the second aspect stored thereon.
  • the device is a seismic node.
  • the device is a seabed seismic node.
  • a computer implemented method for correcting for clock drift of the system clocks of an array of signal sampling devices configured to sample signals in real time
  • the array of signal sampling devices includes a first signal sampling device comprising a first system clock and a second signal sampling device comprising a second system clock
  • the method comprising: correcting for clock drift of the first system clock using a first model selected from a model set prepared according to the second aspect; correcting for clock drift of the second system clock using a second model selected from the same model set; wherein the first model and the second model are mutually different models.
  • the signal sampling devices may be autonomous signal sampling devices. Different nodes within the same array can therefore have their system clocks corrected differently, despite their proximity.
  • a computer implemented method for correcting for clock drift in a signal sampling device comprising a system clock
  • the method comprising: collecting, using at least one sensor of the device, data representing a sampled signal; selecting, from a model set comprising one or more models stored in data storage and based on expected or measured conditions during data collection, a model tagged with an environmental scenario; and correcting for clock drift of the system clock using the selected model.
  • the selecting of the model can be carried out either before or after sampling of the data.
  • the method comprises, prior to selecting the model, preparing the model set by: 1 ) selecting a group of oscillators and calculating a frequency error at a plurality of time points throughout a test period by comparing oscillator cycle counts to a reference clock source; 2) selecting a model type comprising one or more parameters for optimization; and 3) optimizing the parameters of the model by fitting to the calculated frequency error, and repeating steps 1 to 3 for each of the one or more models in the set.
  • the model set comprises two or more models tagged with different environmental scenarios.
  • the environmental scenarios may be a stressed scenario and a non-stressed scenario, for example.
  • the stressed model, tagged with the stressed scenario may be a mechanical shock model or a temperature shock model.
  • the model set can include a non-stressed model (no mechanical or temperature shock - tagged with a non-stressed scenario), a mechanical shock model, and a temperature shock model.
  • the set can include a further model associated with a scenario where combinations of different types of shock or nonshock scenarios are present.
  • one of the models in the set can be tagged with a scenario where both a temperature shock and a mechanical shock are present.
  • the method comprises receiving, from the signal sampling device, data representing the sampled signal, wherein correcting for clock drift of the system clock comprises correcting the data representing the sampled signal.
  • correcting for clock drift of the system clock comprises adjusting the frequency of the system clock during collection and time stamping, by the signal sampling device, of data representing a sampled signal.
  • a computer including a processor and a memory configured to perform a method according to any one or more of the first, second, fourth, and fifth aspects; a computer program comprising instructions which, when executed by a computer including a processor and a memory, cause the computer to perform a method according to any one or more of the first, second, fourth, and fifth aspects; and/or a non-transient computer- readable storage medium comprising instructions which, when executed by a computer including a processor and a memory, cause the computer to perform a method according to any one or more of the first, second, fourth, and fifth aspects.
  • Figure 1 illustrates a model fit to data measured in the laboratory
  • Figure 3 is a block diagram illustrating the steps in a method for applying a model set in order to correct for clock drift.
  • the method described herein is based on the preparation and application of a model set which can be applied to clock-based systems to correct for clock drift.
  • Use of the model set to correct seismic or environmental data sampled by a seismic node during a seismic survey is described in detail herein, but the method can be applied to any device to be used for signal sampling, and more generally to any electronic system using a system clock exhibiting drift.
  • the model set is prepared using a plurality of oscillators of the same type, which are monitored throughout a test period under laboratory conditions while access to a global reference clock is available.
  • the oscillators referred to here can each be a single oscillator or can be made up of a group of oscillators that together make up a sampling or system clock.
  • the duration of the test period is selected to be the same as, similar to, or at least as long as a length of time that the system to which the models will be applied is to be used without access to an external time reference during sampling.
  • the test period will be at least 10 days, more preferably at least 30 days, still more preferably at least 60 days, and most preferably at least 90 days.
  • Monitoring of the group of oscillators during the test period comprises collecting data reflecting a drift of the oscillator frequency at specific time points throughout the test period.
  • the drift is the difference between the counted oscillator cycles per second and the reading from the global reference clock (which can be any reference clock, such as GNSS or a disciplined rubidium atomic clock).
  • the global reference clock which can be any reference clock, such as GNSS or a disciplined rubidium atomic clock.
  • a comparison is made between the number of oscillator tics counted by the oscillator from the time of one global time measurement to the time of the next global time measurement and the “expected” nominal number of cycles between these two time points. This is used to estimate the difference between the nominal oscillator frequency (which is the number of oscillations per second for a perfect oscillator with zero deviation) and the actual number of cycles per second for the oscillator under test.
  • the number of oscillators in the group, N may be between 10 and 1000, preferably between 50 and 200.
  • the number of oscillators in the group can vary, and may be selected as the lowest number of oscillators that is statistically significant based on the observed variations in the type of oscillator at issue.
  • the model set can include a plurality of different model types which are designed to be applied to different environmental scenarios. In a simple case, two different model types are used which are associated with two different environmental scenarios. These scenarios may represent the presence and absence of a mechanical or temperature shock, or may relate to a particular change in magnetic field that is likely to occur during use of the modelled system.
  • the first model in the set (a “temperature shock model”) is to be applied in a situation where the node or device will be subjected to a temperature shock during use
  • the second model (a “non-temperature shock model”) is to be applied in cases where there will be no temperature shock.
  • a temperature shock can refer, for example, to a sharp change in temperature over a short time period, for example a drop or a rise in temperature of over 10 degrees within a time frame of less than 1 day.
  • the thresholds for what constitutes a temperature shock can be selected depending on the type of oscillator being tested in the production of the models.
  • a temperature gradient that causes a significant change in the behaviour of the oscillator at issue will be considered a temperature-shock scenario.
  • the model is represented by one or more equations including one or more parameters to be optimised, and preparation of the model comprises optimisation of these parameters.
  • the model can, for example, be polynomial, logarithmic, or exponential, or can involve any other function of time containing constraints and parameters that can be optimized.
  • the model type i.e. , the form of the equations making up the model and the parameters to be optimized
  • the non- temperature shock model is based on a quadratic function and the temperature shock model on a non-linear function, one example of which is set out below.
  • the temperature shock model can, for example, comprise a function including a logarithmic part plus some polynomial terms.
  • the above can also be applied in case of a rapid change in any environmental parameter likely to affect the operation of the sampling device, such as magnetic field strength or mechanical stress (magnetic or mechanical shocks), wherein the model tagged with a stressed scenario is based on a non-linear function and the model tagged with a non-stressed scenario is based on a quadratic function.
  • optimization of the model parameters is carried out. This is done using the data which has been collected for all of the oscillators in the group and for the entire duration of the test period.
  • the laboratory conditions will be adapted throughout the test period to represent the environmental scenario that the model is to reflect.
  • the temperature shock model therefore, the temperature of the oscillators in the group will be adjusted so that it falls or rises rapidly at least once during the test period to represent the temperature shock.
  • the temperature of surroundings will vary more slowly or will be kept constant throughout the test period.
  • Figure 1 illustrates data collected over the 90 day test period for one of the oscillators in the group (grey) and the frequency error estimated by the best model fit for a shocked model.
  • the effect of the temperature shock on the data itself can be seen as the steeper rise in frequency error within the first day or so of the test period.
  • the graph shows the relative frequency error estimates, df/f, measured in ppb based on GNSS readings (grey) and the model (black) for an oscillator.
  • the model has been optimized using data from the entire group of N oscillators, but data from only one of these is shown in the graph.
  • a drift per unit time (the drift rate) or any equivalent quantity including the relative frequency error, df/f, as a function of time over the whole test period for each oscillator in the group represents the data to be used in the optimization.
  • this drift rate, for each oscillator can be measured as the drift between two readings of the external reference time, divided by the time elapsed between the two readings, and will be noted periodically.
  • the data will be collected for each oscillator in the group at least once every minute, more preferably at least once every 10 seconds, and most preferably at least once every second (a 1 PPS GNSS signal output can provide a global time reference every full second, for example).
  • the model itself estimates a drift rate error for each of the oscillators, and parameters of the model can then be optimized by comparing the drift, which is the integral of the draft rate, estimated by the model to the drift value actually measured for each of the oscillators throughout the entire duration of the test period, or by comparing the drift rate itself or another equivalent quantity.
  • the difference between the drift relative to the global time reading and the drift estimated by the model is the model error ern(tj) for oscillator i at time j.
  • a preferred method for parameter optimization minimises the maximum model error for all oscillators and at all time points during the test period.
  • the values of the model parameters which minimize this value will then be selected.
  • erri(tj) is the model error of the drift for the ith oscillator at time point j.
  • methods such as Monte Carlo methods, simulated annealing, genetic algorithms, gradient descent, or similar can be used to assist with the optimization process.
  • FIG. 1 is a block diagram setting out the steps required for preparation of a model of the set.
  • step 1 a group of N oscillators is selected.
  • step 2 oscillator cycle counts for each of the N oscillators in the group and global clock readings are recorded periodically throughout the entire duration of the test period.
  • At step 3 at least one property of the environment in which the oscillators are located is controlled to reflect the environmental scenario to which the model will relate.
  • a model type is selected with one or more parameters to be optimized.
  • the one or more parameters of the model are optimized to find the parameters which are best able to fit the data collected during the test period. Preferably, this optimization minimizes the maximum error for all oscillators at each time point, although other methods can be used (minimizing the mean error for all of the oscillators at each time point, for example).
  • the model is validated by performing separate experiments with different durations that can confirm the model’s validity based on some given criteria.
  • Validation may comprise checking that the accuracy of the model meets some predetermined threshold accuracy requirement when a sensor node is actually in use. This may mean that the difference between the model-calculated frequency drift and the actual frequency drift is below a certain threshold after a certain time period (less than 1 ms maximum deviation over two months of deployment, for example).
  • steps 1 to 6 are repeated subjecting the N oscillators to different environmental conditions, and selecting a suitable model type for those conditions.
  • the final model set will therefore comprise a set of models that are optimized, with each model being associated with a particular environmental scenario and usually also a specific oscillator type.
  • the temperature shock model is represented by the following equations: where c, Tm, and x are the parameters (in this case constants) to be optimized when preparing the model, ferr(t) is the final relative frequency error estimate, df/f, at time t, f’err(t) is the first approximation with f’m being its mean value over the test period.
  • D is the total drift during the test period
  • T is the total duration of the test period in seconds
  • t is the time in seconds
  • u is the unit of frequency error suitable for the type of oscillator being used (i.e in ppb or ppm).
  • the total drift D can then be represented by:
  • the model set can also include models relating to different oscillator types, again with each model relating to a specific environmental scenario. These can be produced by repeating steps 1 to 6 of figure 2 but with a group of oscillators of the relevant type.
  • measurements can be taken before and after deployment of the device of the local clock frequency and the global time. These measurements can be used to correct for any residual, or additional, linear drift, but they can also be used to determine a total deployment time and a total drift during the deployment period.
  • these values can be used to initialize the models.
  • an estimate for these parameters can be entered as an alternative if measurements are not taken or are not available. The estimate can be based on lab tests or using historical data for the same or a similar node, preferably under similar conditions.
  • a model set to be applied for a specific signal sensing device will include models relating to an oscillator of a particular type. This type will be the same as the system clock of the device.
  • the model set can be stored as software in the clocked system for which a correction is to be applied, or can be stored externally.
  • the correction itself can either be carried out in real time or after the sampling is complete (for example after a survey is complete in the case of correction for a seismic node).
  • a parameter is monitored in the environment surrounding the device.
  • This may, for example, be a temperature, mechanical stress, or a magnetic field strength close to a seismic node.
  • the seismic node in this case represents the device and a temperature or magnetic sensor on the node provides the reading.
  • the environmental parameter can be measured only once, but most often a series of measurements will be taken to give a profile of the environmental parameter over time.
  • the parameter may be measured periodically or continuously throughout the entire period during which the data to be corrected is being collected (i.e. the entire actual deployment period for a seismic node).
  • Models can be tagged with their associated environmental scenario as meta data, which can make model selection from the set simpler. Depending on which environmental parameters the models in the set are associated with, selection of a model will usually involve monitoring of one, some, or all of the relevant parameters during use of the clocked system and then selection of a suitable model from the set based on the measurements taken. In an example where a model set is intended for correction of clock drift in a seismic node being used for sampling of a seismic signal, an environmental parameter will be monitored using a sensor located on the node, and may be monitored throughout the duration of the seismic survey.

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Abstract

L'invention concerne un procédé mis en oeuvre par ordinateur pour corriger une dérive d'horloge d'une horloge système dans un dispositif d'échantillonnage de signal comprenant l'horloge système et configuré pour échantillonner un signal en temps réel, le procédé consistant à : obtenir des données relatives à un paramètre environnemental d'un environnement du dispositif d'échantillonnage de signal ; mettre en concordance les données relatives au paramètre environnemental avec un scénario environnemental ; sélectionner, à partir d'un ensemble de modèles, un modèle étiqueté avec le scénario environnemental concordant ; et corriger une dérive d'horloge de l'horloge système à l'aide du modèle sélectionné. L'invention concerne également un procédé de préparation d'un ensemble de modèles pour une correction de dérive d'horloge, un dispositif d'échantillonnage et un procédé mis en oeuvre par ordinateur pour corriger une dérive d'horloge des horloges système d'un réseau de dispositifs d'échantillonnage de signal.
PCT/NO2025/050121 2024-07-01 2025-07-01 Procédé de correction de dérive d'horloge Pending WO2026010507A1 (fr)

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US7148761B1 (en) * 2005-11-29 2006-12-12 Mediatek Inc. GPS receiver devices and compensation methods therefor
US20100045523A1 (en) * 2008-08-21 2010-02-25 Kung-Shuan Huang Methods and apparatus for compensating a clock bias in a gnss receiver
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