DEVICE AND METHOD FOR SMART PICKING SURFACE WAVES DISPERSION CURVES

Abstract

Device and method for calculating a set of surface wave dispersion curves. The method includes receiving seismic data recorded with seismic sensors over an area to be surveyed; selecting region units that cover the area to be surveyed; gathering traces for the region units; processing in a computing device the traces to obtain a set of candidate measurements for each region unit; teaching a decision algorithm based on a first subset of the set of candidate measurements; and calculating the set of surface wave dispersion curves by running the decision algorithm on a second subset of the set of candidate measurements.

Claims

1. A method for calculating a set of surface wave dispersion curves, the method comprising: receiving seismic data recorded with seismic sensors over an area to be surveyed; selecting region units that cover the area to be surveyed; gathering traces for the region units; processing in a computing device the traces to obtain a set of candidate measurements for each region unit; teaching a decision algorithm based on a first subset of the set of candidate measurements; and calculating the set of surface wave dispersion curves by running the decision algorithm on a second subset of the set of candidate measurements.

2. The method of claim 1, further comprising: calculating, for each trace and for each frequency, a number of local maxima that form the set of candidate measurements.

3. The method of claim 2, further comprising: converting arrival times associated with each trace and each frequency of the set of candidates measurements to a velocity.

4. The method of claim 3, further comprising: sorting the set of candidate measurements into confirmed measurements and ambiguous measurements, wherein a confirmed measurement includes a single maximum and an ambiguous measurement includes two or more maxima.

5. The method of claim 4, wherein the step of teaching the decision algorithm further comprises: approximating a dispersion curve with a straight line for a range of a few Hertz; using only the confirmed measurements to run a linear regression algorithm to model a set of straight lines over an entire frequency range; and teaching the decision algorithm over the entire frequency range.

6. The method of claim 5, wherein the step of calculating the set of surface wave dispersion curves further comprises: running the decision algorithm on the ambiguous measurements to calculate a predicted velocity for each measurement; and selecting a disambiguated measurement as a measurement having the measured velocity closest to the predicted velocity.

7. The method of claim 6, further comprising: generating the set of surface wave dispersion curves to include the confirmed measurement and the disambiguated measurement.

8. The method of claim 7, further comprising: removing from the seismic data the surface waves based on the set of surface wave dispersion curves.

9. The method of claim 8, further comprising: generating an image of the surveyed area based on the seismic data from which the set of surface wave dispersion curves has been removed.

10. The method of claim 1, further comprising: calculating a velocity model based on the set of surface wave dispersion curves.

11. A device for calculating a set of surface wave dispersion curves, the device comprising: an interface for receiving seismic data recorded with seismic sensors over an area to be surveyed; and a processor connected to the interface and configured to, select region units that cover the area to be surveyed, gather traces for the region units, process the traces to obtain a set of candidate measurements for each region unit, teach a decision algorithm based on a first subset of the set of candidate measurements, and calculate the set of surface wave dispersion curves by running the decision algorithm on a second subset of the set of candidate measurements.

12. The device of claim 11, wherein the processor is further configured to: calculate, for each trace and for each frequency, a number of local maxima that form the set of candidate measurements.

13. The device of claim 12, wherein the processor is further configured to: convert arrival times associated with each trace and each frequency of the set of candidate measurements to a velocity.

14. The device of claim 13, wherein the processor is further configured to: sort the set of candidate measurements into confirmed measurements and ambiguous measurements, wherein a confirmed measurement includes a single maximum and an ambiguous measurement includes two or more maxima.

15. The device of claim 14, wherein the processor is further configured to: approximate a dispersion curve with a straight line for a range of a few Hertz, use only the confirmed measurements to run a linear regression algorithm to model a set of straight lines over an entire frequency range, and teach the decision algorithm over the entire frequency range.

16. The device of claim 15, wherein the processor is further configured to: run the decision algorithm on the ambiguous measurements to calculate a predicted velocity for each measurement, and select a disambiguated measurement as a measurement having the measured velocity closest to the predicted velocity.

17. The device of claim 16, wherein the processor is further configured to: generate the set of surface wave dispersion curves to include the confirmed measurement and the disambiguated measurement.

18. The device of claim 17, wherein the processor is further configured to: remove from the seismic data the surface waves based on the set of surface wave dispersion curves.

19. The device of claim 18, wherein the processor is further configured to: generate an image of the surveyed area based on the seismic data from which the set of surface wave dispersion curves has been removed.

20. A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a computer, implement a method for calculating a set of surface wave dispersion curves, the method comprising: receiving seismic data recorded with seismic sensors over an area to be surveyed; selecting region units that cover the area to be surveyed; gathering traces for the region units; processing the traces to obtain a set of candidate measurements; teaching a decision algorithm based on a first subset of the set of candidate measurements; and calculating the set of surface wave dispersion curves by running the decision algorithm on a second subset of the set of candidate measurements.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate one or more embodiments and, together with the description, explain these embodiments. In the drawings:

[0016] FIG. 1 illustrates a conventional land seismic data acquisition system;

[0017] FIG. 2 illustrates a conventional marine data acquisition system;

[0018] FIG. 3 illustrates body and surface waves that are generated during a seismic survey;

[0019] FIG. 4 is a flowchart of a method for calculating final dispersion curves for the surface waves;

[0020] FIG. 5 is a flowchart of a method for processing seismic data; and

[0021] FIG. 6 illustrates an exemplary data processing device or system which can be used to implement the methods discussed herein.

DETAILED DESCRIPTION

[0022] The following description of the embodiments refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. For simplicity, the following embodiments refer to the surface waves. However, the methods discussed herein equally apply to body waves and for this case, only one velocity or one propagation time is output.

[0023] Reference throughout the specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with an embodiment is included in at least one embodiment of the subject matter disclosed. Thus, the appearance of the phrases “in one embodiment” or “in an embodiment” in various places throughout the specification is not necessarily referring to the same embodiment. Further, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

[0024] According to an embodiment, there is a method for disambiguating seismic data that includes surface waves so that it is possible to select the propagation velocity of a surface wave as a function of its frequency. The method uses a decision algorithm for this task that is trained on a set of candidates as now discussed.

[0025] The method includes, as illustrated in FIG. 4, a step 400 of acquiring seismic data over a survey area in a marine or land environment, a step 402 of selecting region units for the survey area, a step 404 of gathering traces in one or more of the region units, a step 406 of processing the traces, for each selected region unit, to obtain a set of candidate measurements, a step 408 of teaching a decision algorithm based on selected measurements from the set of candidate measurements, a step 410 of running the decision algorithm, for each region unit, to obtain a set of final dispersion curves for the surface waves, a step 412 of applying the set of final dispersion curves for processing the acquired seismic data, and, optionally, a step 414 of generating an image of the surveyed subsurface.

[0026] The steps of this method are now discussed in more detail. Step 400 of acquiring the seismic data may happen on land with the configuration shown in FIG. 1, or on water, with the configuration shown in FIG. 2. Other configurations may be used, e.g., passive seismic acquisition, or seismic wave-field reconstructed by cross-correlation, or micro-seismicity. The seismic source may be any known source, e.g., a vibratory source or an air gun. The seismic detectors may include hydrophones, geophones, accelerometers, optical fibers, etc. The acquired data may be one component or multi-component. In one application, the operator of a computing system that runs the method receives the seismic data for processing instead of acquiring it.

[0027] In step 402, region units connected to the seismic survey are selected. The selected region units may cover the entire or, parts of the surveyed area. A “region unit” is defined as an ensemble of source-receiver pairs, lying in the same area, and presenting similar geometries. For example, in one application, the source-receiver pairs have similar offset distances and azimuths. Note that the term “source” includes a dedicated seismic source, e.g., vibrator, gun, another receiver and/or an earthquake. In the same application or another one, the ends of the source-receiver pairs need to be close to each other.

[0028] Thus, a region unit can be a spatial area on which a set of sources and receivers are located and the set of sources and receivers have similar geographical coordinates and/or similar azimuth and/or similar offset distances. It can also be defined as a set of areas with similar geography/geology. The size of a region unit depends on the wavelength of the surface waves. For example, the size of a region unit (its length) may be between one and ten wavelengths of the investigated surface waves.

[0029] In step 404, traces belonging to a common region unit are gathered to be processed together. As discussed above, one or more region units may be used. The number of region units depends on the goal of the survey. For example, if the goal is to map all the surface waves to the area of the survey, then the number of selected region units should completely cover the survey area. For other goals, a number of region units that do not fully cover the survey area may be used. Thus, this step is applied to some or all the region units that cover the survey area.

[0030] In step 406, the traces from the selected region units are processed. In each region unit, the traces may be processed with techniques that highlight arrival times as a function of frequency. For example, it is possible to use Time-Frequency Analysis (Levshin et al., 1989, “Seismic Surface Waves in Laterally Inhomogeneous Earth,” Kluwer Publ. House, Dordrecht/Boston/London, Russian Edition (Nauka), 1988), but it is also possible to use the technique MOPA (C. Strobbia and S Foti, “Multi-offset phase analysis of surface wave data (MOPA),” Journal of Applied Geophysics, Volume 59, Issue 4, Pages 300-313). Those skilled in the art would know to use other techniques if so desired.

[0031] Some of the above noted techniques usually project the traces in a domain in which the arrival times may be determined. Each trace in such domain is analysed with a computer for determining a number of local maxima. These local maxima are selected at each frequency to obtain the set of candidate measurements of arrival times as a function of frequency. Then, the arrival times may be converted to velocities. This means that for one trace and one frequency, there may be multiple velocities, i.e., the result is ambiguous.

[0032] These measurements (e.g., traces) are then sorted out between “confirmed” and “ambiguous” as follows. If for a given trace and a given frequency, only one maximum has been measured within the surface wave velocity range, that measurement is labelled as “confirmed.” If for a given trace and a given frequency, several maxima have been measured within the surface wave velocity range, the measurement is labelled as “ambiguous.” Both the confirmed and ambiguous measurements are part of the set of candidate measurements.

[0033] In step 408, the decision algorithm is trained only with the measurements labelled “confirmed” to calculate dispersion curves. The decision algorithm is an algorithm that will predict surface wave velocities at a given frequency using a certain model. This model is built locally (e.g., on a few Hertz range of data) by modeling a straight line using a linear regression.

[0034] The decision algorithm can be a “supervised learning algorithm” as described above, or an “unsupervised learning algorithm” where the learning algorithm would be fed with unlabeled candidate measurements, and would set its own decision making rules. The decision making rules can be set on the seismic survey being processed, or can be extracted from a database build using the lessons learned on previous surveys. The entire process can be run in real-time while seismic data is still being recorded, or as part of a processing sequence once all data has been acquired.

[0035] The model noted above may be built as follows: assume that a dispersion curve can locally (over a few Hertz range of frequency) be approximated to a straight line, and that all dispersion curves within the region unit are similar. This assumption is true as any curve can be approximated with a straight line for a short portion. Use then the measurements labelled as “confirmed” to run a linear regression algorithm to model a set of straight lines over the entire frequency range. The decision algorithm may be taught over the entire frequency range. The decision algorithm may be run on each selected region unit.

[0036] In step 410, the decision algorithm is run on the ambiguous measurements for calculating their final dispersion curves. The decision algorithm provides the predicted velocity for frequencies corresponding to measurements that were labeled ambiguous as now discussed. As noted above, because the measurements are ambiguous, there are plural measured velocities (the arrival times transformed in step 406 into velocities) for a given frequency. The decision algorithm predicts a single velocity for each given frequency. Thus, the measurement that is closest to the predicted velocity is kept as the disambiguated measurement.

[0037] In other words, assume that for a given frequency f, there are three measured velocities v1, v2, and v3. The decision algorithm predicts a velocity vp. The measured velocity closest to vp, (assume to be v2) is then selected as the measurement to be kept. The other two velocities v1 and v3 are discarded.

[0038] In this step, both the disambiguated measurements and those labelled “confirmed” are collected to form the final set of surface wave dispersion curves.

[0039] The final set of surface wave dispersion curves is used in step 412 to process the initial seismic data. For example, in one embodiment, the information from step 410 is used to map the surface waves to the surveyed surface, or to build a velocity model, or for statics corrections. In one embodiment, the final set of surface wave dispersion curves is used to design filters for removing the surface waves from the recorded seismic data. For this instance, the “cleaned data” (i.e., recorded seismic data from which the surface waves have been removed) is used in step 414 for generating an image of the surveyed subsurface.

[0040] The seismic data processed in step 412 may be run through other processing algorithms prior to generating a final image of the surveyed subsurface. For example, seismic data generated with the source elements as discussed with regard to FIGS. 1 and 2 may be received in step 500 of FIG. 5 at a processing device. In step 502, pre-processing methods are applied, e.g., demultiple, signature deconvolution, trace summing, vibroseis correlation, resampling, etc. In step 504, the main processing takes place, e.g., deconvolution, amplitude analysis, statics determination, common middle point gathering, velocity analysis, normal move-out correction, muting, trace equalization, stacking, noise rejection, amplitude equalization, etc. In step 506, final or post-processing methods are applied, e.g. migration, wavelet processing, inversion, etc. In step 508, the final image of the subsurface is generated.

[0041] The above-discussed procedures and methods may be implemented in a computing device as illustrated in FIG. 6. Hardware, firmware, software or a combination thereof may be used to perform the various steps and operations described herein. Computing device 600 of FIG. 6 is an exemplary computing structure that may be used in connection with such a system.

[0042] Exemplary computing device 600 suitable for performing the activities described in the exemplary embodiments may include a server 601. Such a server 601 may include a central processor (CPU) 602 coupled to a random access memory (RAM) 604 and to a read-only memory (ROM) 606. ROM 606 may also be other types of storage media to store programs, such as programmable ROM (PROM), erasable PROM (EPROM), etc. Processor 602 may communicate with other internal and external components through input/output (I/O) circuitry 608 and bussing 610 to provide control signals and the like. Processor 602 carries out a variety of functions as are known in the art, as dictated by software and/or firmware instructions.

[0043] Server 601 may also include one or more data storage devices, including hard drives 612, CD-ROM drives 614 and other hardware capable of reading and/or storing information, such as DVD, etc. In one embodiment, software for carrying out the above-discussed steps may be stored and distributed on a CD-ROM or DVD 616, a USB storage device 618 or other form of media capable of portably storing information. These storage media may be inserted into, and read by, devices such as CD-ROM drive 614, disk drive 612, etc. Server 601 may be coupled to a display 620, which may be any type of known display or presentation screen, such as LCD, plasma display, cathode ray tube (CRT), etc. A user input interface 622 is provided, including one or more user interface mechanisms such as a mouse, keyboard, microphone, touchpad, touch screen, voice-recognition system, etc.

[0044] Server 601 may be coupled to other devices, such as sources, detectors, etc. The server may be part of a larger network configuration as in a global area network (GAN) such as the Internet 628, which allows ultimate connection to various landline and/or mobile computing devices.

[0045] The disclosed exemplary embodiments provide a computing device, software and method for calculating a final set of surface wave dispersion curves. It should be understood that this description is not intended to limit the invention. On the contrary, the exemplary embodiments are intended to cover alternatives, modifications and equivalents, which are included in the spirit and scope of the invention. Further, in the detailed description of the exemplary embodiments, numerous specific details are set forth in order to provide a comprehensive understanding of the invention. However, one skilled in the art would understand that various embodiments may be practiced without such specific details.

[0046] Although the features and elements of the present exemplary embodiments are described in the embodiments in particular combinations, each feature or element can be used alone without the other features and elements of the embodiments or in various combinations with or without other features and elements disclosed herein. The methods or flowcharts provided in the present application may be implemented in a computer program, software, or firmware tangibly embodied in a computer-readable storage medium for execution by a geo-physics dedicated computer or a processor.

[0047] This written description uses examples of the subject matter disclosed to enable any person skilled in the art to practice the same, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.