Macro compressive sensing data acquisition

Abstract

The invention relates to a streamer is described. The streamers include one or more streamer segments. The streamer segments may have sensors, channels, and / or an analog array of sensors arranged along their length. At least one of the streamer segments has sensors, channels and / or analog arrays of variable density along the length.

Description

[0001] Divisional application statement

[0002] This application is a divisional application of Chinese patent application No. 202080008963.X. Technical Field

[0003] The present invention relates to a streamer device, in particular, a sensor device having different densities of sensors, channels or sensor arrays along a streamer or streamer section.

[0004] The main application of the invention is seismic data acquisition, but it can also be applied to other fields such as acoustics and ultrasound. Background Art

[0005] Towed ocean streamers are used to perform seismic surveys of the seafloor to map properties both at the surface and beneath the surface. Each streamer consists of a cable that is towed behind a vessel below the water's surface. The streamer has a series of sensors coupled to it, which typically measure pressure differences in the water. Sensors that measure velocity or acceleration (motion sensors) may be used instead, or both types of sensors may be used together to reduce noise in the output signal, as described below. The sensors are typically distributed evenly along the streamer, and the distance between adjacent sensors may be fixed at approximately 12.5 m. This configuration results in a sensor density that does not vary along the length of the streamer (e.g., in this example, the sensor density would be one sensor every 12.5 m, or 0.08 m). -1 , in all directions along the streamer).

[0006] One or more seismic sources are located on a vessel and periodically transmit seismic waves, which travel through the water surface and reflect off the seafloor or bottom. These compression waves then return to the ocean surface and are measured by sensors as a series of compressions and decompressions of adjacent water molecules, providing information about the topography and structure of the ocean surface and the seabed beneath the vessel. A single streamer can be used, or multiple streamers can be used together and towed alongside each other behind the vessel to form a sensor blanket (these are known as 3D surveys because of the underwater images that can be produced using this configuration). False signals are generated by pressure waves that have already passed through sensors on the streamers and are reflected back to the streamers by the ocean surface once they are close to them. Generally, these false signals do not provide additional useful information, and it is desirable to remove them from the final signal. This can be achieved by supplementing hydrophone sensor readings with particle motion sensors, which typically achieve a better signal-to-noise ratio than particle motion sensors. Particle motion sensors provide directional measurements and can therefore be used together with pressure readings to distinguish between waves propagating from the sea surface and the bottom. Combining the readings of these two sensor types allows for the removal of false signals.

[0007] When a streamer is towed, the received signal is affected by a high level of additive noise due to the natural motion of the water, the motion of the vessel itself, and the drag of the streamer itself in the water. Naturally, it is desirable to reduce this noise as much as possible and increase the signal-to-noise ratio.

[0008] Traditionally, sensors and output points are evenly spaced at constant intervals along the length of a streamer or streamer segment. In this case, the distance between adjacent output points, or between adjacent sensors on a streamer, does not vary along the streamer. The choice of distance between output points and the number of output points required on a streamer is driven by signal sampling requirements, while the choice of distance between sensors and the number of sensors used is driven by noise sampling and attenuation requirements.

[0009] The Nyquist sampling criterion determines the minimum signal sampling rate to retain a certain amount of information from the signal and stipulates that to correctly sample the signal, an output is required approximately every 3 meters (3.125m), corresponding to a measurement every 2ms. However, this does increase the cost and complexity of the equipment, and outputs are typically provided every 12.5 meters on the streamer. This lower sampling rate can lead to aliasing, especially at higher frequencies and when the propagation direction of the compression wave is close to horizontal. Summary of the Invention

[0010] The present invention addresses the noise and aliasing issues in this type of signal, as described in detail below. While the present invention is applicable to all seismic data acquisition scenarios, including towed streamer, seabed, node, and land seismic data acquisition, we use the towed streamer scenario as a platform to explain these ideas in the following description. Therefore, when presenting arguments, we will use towed streamer terminology (where each streamer may include multiple streamer segments, such as 100m streamer segments) without affecting the generality of the final claims.

[0011] The following terms have broader meanings to those skilled in the art than will be presented. These terms are more concise but no limitation to the standard meanings is implied or intended.

[0012] It is important to understand the concepts of input and output points. Input points correspond to the physical locations of sensors that sense or measure raw data, while output points correspond to physical locations along the streamer that provide signal estimates, typically provided by combining data from multiple sensors grouped together using hardware or software. The positions of the sensors within the group can be measured relative to the location of the linked output points, and the positions of the individual output points can be used along with the received signals to reconstruct the seafloor structure by processing the output signals received from their known locations along the streamer.

[0013] A streamer, which can be several kilometers long, is typically not a continuous cable but is instead made by connecting several streamer segments together. For example, each streamer segment might be 100 meters, 150 meters, or 200 meters long. A combination of streamers of different lengths can be used. This allows the length of the streamer to be changed as needed and provides the ability to replace a single streamer segment rather than the entire streamer in the event of a technical failure.

[0014] As used herein, the term sensor refers to any device or devices that can be used to measure various properties of interest. Particle motion sensors (e.g., geophones, accelerometers, and / or rotation sensors) or pressure sensors (e.g., hydrophones) that measure seismic signals are most common, and these are particularly well-suited for use with the methods described below. However, any sensor suitable for measuring particle motion or pressure changes and suitable for mounting on a streamer may be used.

[0015] The particle motion sensor may be a sensor for measuring velocity or acceleration, such as a moving coil geophone, a MEMS accelerometer or a piezoelectric accelerometer. A hydrophone is a sensor for measuring pressure changes, for example by piezoelectric means.

[0016] An analog array is a group of hardwired sensors connected together to a single electronic channel. These sensors can typically include 4 to 16 sensors, each 2 to 15 meters long (and can overlap). Sensor distribution can be uniform or non-uniform. The output point is typically located at the center of the array. The center of the array can be the physical center, the center of gravity, or both. For this type of array, at least some noise attenuation is achieved by summing the analog signals from the sensors within the array.

[0017] Where individual sensors are used rather than forming an analog array, the sensors can each be linked to its own channel, and the channels of multiple sensors can provide input data to a particular output point.

[0018] Channels, or electronic channels, typically include amplifiers or preamplifiers and ADCs (analog-to-digital converters). They digitize the signals from a sensor or sensor array, so each time a reading from an analog sensor or analog sensor array is digitized, there is a channel. If only one sensor is connected to a channel, this is called single-sensor recording. If multiple sensors are connected to a channel, this is called sensor array recording. The number of channels present can correspond to the number of data points transmitted to the vessel via the seismic cable. Output points themselves are not equivalent to channels. Output points correspond to data points recorded to tape or provided as output to a client. These data points can be linked to a channel that itself carries data collected from multiple sensors, or they can be linked to multiple channels that each transmit measurement data collected from a single sensor. Alternatively, multiple channels, each linked to more than one sensor, can be coupled to a single output point. Therefore, each output point does not necessarily have to be associated with one or only one analog sensor array or channel. Other channels carry data (usually digitized at this point) from the output point to processing software. This processing software typically receives signals from multiple output points located at known physical locations along the streamer to reconstruct the signals received from the subsurface and the structure of the subsurface itself.

[0019] For single sensor recordings (one channel for one sensor), the channel position or channel location is the same as the sensor position. For analog array recordings (multiple sensors combined into an array for one channel), the channel position is usually at the center or centroid of the array.

[0020] In some cases, noise events are locally coherent. Figure 1 An example of strong local coherent noise from a tow test is disclosed. In some data acquisition scenarios, the data may be affected by noise with relatively short coherence lengths. An example is shown in Figure 1 ,exist Figure 1 Accelerometer data acquired during a towed streamer experiment are shown in Figure 1. The left panel is in the time offset (spatial) domain, and the right panel is in the frequency offset domain.

[0021] Especially where locally coherent noise is a problem, signal reproduction can be improved by sampling the sensors more densely near the output point, as described below. To illustrate this, we again use the example of towed streamer seismic acquisition. Figure 2 An example of sensors is disclosed where the sensors are spaced to provide an alias-free output of 125 Hz (or higher if a filter is included for anti-aliasing). The locations of the sensors along the streamer segments are represented using triangles in the figure. Figure 2 、 3 , 5, and 7 use arrows to indicate the locations of output points along the streamer.

[0022] This application describes a new method for acquiring data with a higher signal-to-noise ratio (SNR) than commonly used methods in the presence of locally coherent noise. The system described is particularly relevant when the number of sensors is limited, and describes how to place the sensors to reduce the adverse effects of noise and aliasing while minimizing the number of sensors required on a streamer. The invention achieves this in part by using a variable density function of sensors, channels, and / or analog sensor arrays along the length of a streamer or streamer segment.

[0023] Consider two types of sensor layouts, e.g. Figure 2 and Figure 3 The blue triangle indicates the relative position of the sensor and the red arrow indicates the output point. Figure 2 In the figure, the desired output points are evenly spaced at 6.25m intervals. This is intended to provide alias-free output up to 120Hz, assuming an apparent velocity of 1500m / s for the slowest seismic signal energy in marine seismic acquisition. This shows a non-uniformly spaced sensor to avoid noise aliasing. If an anti-aliasing filter is used, alias-free output to higher frequencies can be achieved, but at the expense of some dipping events at higher frequencies.

[0024] The sensor density at a point on a streamer is the number of sensors per unit length at that point. If the number of sensors per unit length is fixed, then the sensors can be distributed in a variety of ways. If there are enough sensors available, they can be placed at regular intervals according to the Nyquist criterion, but doing so usually means high cost, weight, and complexity of the equipment due to the large number of sensors required. If the sensor spacing is fixed, then the sensor density does not vary along the streamer. For example, with a sensor spacing of 5 meters, the sensor density at all locations on the streamer will be 0.2 sensors per meter (or 1 / 5m -1 ).

[0025] An alternative approach is to place sensors pseudo-randomly (at non-uniform intervals) to avoid hard aliasing. A possible pseudo-random sensor distribution is Figure 2 To provide clean output at all desired output points, the sensors must be relatively well distributed along the streamer.

[0026] Another approach that has proven particularly effective in reducing noise is to concentrate (cluster) the sensors close to the output point to better attenuate short coherence length noise. A possible cluster sensor distribution is Figure 3 This shows that the sensors are spaced apart to provide a cleaner signal at the output point (but only at 60Hz without aliasing - or higher if the filter includes anti-aliasing). The figure shows the evenly spaced output points, spaced at a distance of 12.5 meters. Figure 3The sensor density of the streamer section shown is now non-uniform. The number of sensors per meter (sensor density) is higher near the output point and lower (dropping to zero) away from the output point.

[0027] Since sensors are expensive and therefore very valuable, denser sampling near the output points may require a sparser distribution of the output points (the distance between adjacent output points is larger on average along the streamer). The superior noise attenuation achieved by denser sampling near the output points is at least partially achieved by dedicating more sensors to each output point (twice as many in this example) compared to the pseudo-random sampling strategy described above. For example, if the total number of sensors per section is fixed due to cost implications, then this means that the number of output points must be smaller. Figure 3 This is shown for output points spaced at intervals of 12.5 m, which is equivalent to Figure 2 In the example shown, half of the interval is output.

[0028] However, if the output points are uniformly spaced, sparser output points may result in unacceptable levels of signal aliasing. Output points can generally be positioned at uniform or uneven intervals. If they are positioned at uniform intervals, the Nyquist criterion places an upper limit on the length of the interval required to achieve non-aliased output data at the desired frequency.

[0029] If the number of sensors (e.g., per streamer segment) is fixed, then this defines the maximum number of sensors that can be allocated per output group. If better noise attenuation is required, a higher number of sensors per output point is required, which, since the number of sensors is fixed, requires a reduced number of output points. Using uniform output spacing, this will mean a longer spacing between output points, which can lead to aliasing.

[0030] Figure 4 Shown Figure 2 and Figure 3 The noise attenuation performance of the two distributions shown is compared (where the output points are uniformly distributed along the streamer so that the distance between adjacent output points is fixed). The figure shows the power spectral density of the noise versus frequency. We see that with different pseudo-random configurations (such as Figure 2 The clustered sensor configuration (similar to the configuration shown in Figure 3 ) achieves about 10dB higher noise attenuation (except at very low frequencies).

[0031] If the output points are evenly spaced, sparser output points may result in unacceptable levels of noise / signal aliasing. For example, a regular output spacing (the distance between adjacent output points along the streamer) of 12.5m will only provide 60Hz alias-free output. Therefore, if the output points are evenly spaced, then Figure 4The noise attenuation shown in is only possible at the expense of allowing aliasing of the output signal.

[0032] As mentioned above, denser sampling around an output point has been shown to be beneficial for attenuating short-coherent noise at that output point. This requires a given minimum number of sensors per output point (to achieve the desired level of noise attenuation), but there is typically also a maximum number of sensors achievable per seismic segment (this is often dictated by cost considerations). The result is that fewer output points must be used than is required according to classical sampling theory to adequately sample signals above a certain wavenumber. In particular, with uniform output points, some signal wavenumbers may be aliased and thus cannot be correctly recovered. Thus, there appears to be a dilemma: we must tolerate either more noise or more aliased signals than expected.

[0033] The solution to this is to position the output points at uneven intervals to prevent hard aliasing of the signal. The non-uniformly sampled output signals can be regularized (interpolated onto a regular grid) using one of several compressed sensing methods. Otherwise, they can be used directly, for example for seismic imaging, using their known irregular (non-uniform) locations.

[0034] A sensor density function can be defined to describe the density of sensors in a streamer segment centered at a particular location as a function of the location of that streamer segment. The density function is highest where sensors are closely spaced (the sensor density in a streamer segment with closely spaced sensors is high) and lowest where sensors are sparsely distributed. In the case where sensors are clustered together, each cluster will represent a local maximum in the density function. These local maxima do not have to be of the same magnitude, but will be considered a local maximum if the density is higher than at neighboring locations. The global maximum of the density function for the streamer will represent the highest of these local maxima. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Using a non-uniform distribution of output points along the streamer and different densities of sensors along the streamer, particularly where this corresponds to a higher density of sensors at or near the output points, can reduce noise and aliasing, so that a clean signal can be obtained without increasing the number of sensors required. Embodiments of the invention will now be described, by way of example only, with reference to the following drawings, in which:

[0036] Figure 1 The strong local coherent noise from the drag test is plotted.

[0037] Figure 2 An example of sensors spaced to provide an alias-free output of 125 Hz (or higher if the filter incorporates anti-aliasing) is shown.

[0038] Figure 3An example of sensors spaced apart to provide a cleaner signal at the output (but only unaliased at 60 Hz; higher if the filter incorporates anti-aliasing) is shown.

[0039] Figure 4 Test results for sensor placement and filter design for evenly spaced output points are shown.

[0040] Figure 5 An example of non-uniformly spaced output points is shown for avoiding aliasing while allowing good noise attenuation by including sensors clustered near the output points.

[0041] Figure 6 A flow chart showing a method for reproducing a signal whose output points are non-uniformly distributed.

[0042] Figure 7 Examples are disclosed where more than one output per local sensor cluster is used.

[0043] Figure 8 Illustrative examples of arrays that are non-uniformly distributed along a streamer are provided.

[0044] Figure 9 Results for a synthetic example using SEAM finite-difference data are shown.

[0045] Figure 10 Schematic examples of sensors distributed in groups of different sizes along a streamer are shown. The top row has sensor groups evenly distributed along the streamer segment, and the bottom row has the same groups distributed unevenly.

[0046] Figure 11 A method for reproducing a signal is illustrated, wherein different sets of sensor groups are used to account for different frequency ranges in the signal.

[0047] Figure 12 A method for reproducing a signal is illustrated in which different sets of sensor clusters are used to account for different frequency ranges in the signal and the clusters are non-uniformly distributed along the streamer segment. DETAILED DESCRIPTION

[0048] In some embodiments, the present invention uses non-uniform spacing of input points (sensor locations) and output points to obtain high quality (high SNR) estimates of the signal from noise-affected measurements.

[0049] As mentioned previously, there is a trade-off between allowing more noise than desired or allowing more aliased signals. Non-uniform output sampling can address both the signal and noise sampling issues. The aliasing problem can be at least partially overcome by relaxing the requirement that the output points should be regularly spaced, and by allowing non-uniformly spaced output points, compressed sensing (CS) can be achieved.

[0050] Figure 5 One possible configuration of sensors (input points) and output points in an embodiment is shown. Non-uniformly spaced output points (average spacing greater than 12.5 m) are included to avoid aliasing, while the device allows for good noise attenuation by using sensors clustered near the output points. The sensors are clustered by output and are not uniformly distributed. Non-uniform distribution means that the distance between adjacent sensors or between adjacent output points is not fixed along the streamer. For example, for an output point located between two adjacent output points, the distance to the nearest output point in one direction along the streamer may be less than the distance to the nearest output point in another direction along the streamer. If the distribution of sensors is also chosen to be non-uniform, then the same applies to sensors located between two other sensors on the streamer. The output points may be pseudo-randomly spaced along a streamer segment or streamer.

[0051] If a 6.25m regular spacing is chosen, this example contains fewer output points per unit length of streamer compared to 16 output points per 100m streamer segment, but produces a cleaner signal (i.e., produces output data with a higher SNR). Cleaner output data is more suitable for compressed sensing solutions, including regularization to a regular grid using state-of-the-art CS methods. Figure 6 A flow chart illustrating a method for reconstructing a signal using non-uniformly spaced output points. A compressed sensing algorithm is used to account for the non-uniform distribution of output points by reconstructing regularly spaced outputs along a streamer using the non-uniformly spaced outputs at the output points. The number of reconstructed outputs is typically greater than if the output points were physically spaced along the streamer.

[0052] Thus, the above example allows for various noise attenuation methods to be used to produce cleaner output traces. As a non-limiting example, assume that each streamer segment (perhaps 100m long) has 56 sensors (input points). If there are 16 evenly sampled output points as in the example above, there will be 3.5 sensors per output point, and these sensors must be fairly well (non-uniformly) distributed along the streamer to ensure that each of these 16 output points gives a reasonably clean output. Conversely, if there are fewer output points that require attention (but are unevenly spaced), then noise attenuation methods can provide cleaner results because there will be more sensors available to provide data to each output point (or the device can use fewer sensors). For example, if we find that 8 unevenly sampled output points per streamer segment are sufficient, then there will be 7 sensors per output point, and if we only need 5 output points, then 11 sensors will be needed per output point.

[0053] Compressed sensing recovery algorithms are used to reconstruct the signal using cleaner output data. Examples include: IAA-MP and BPDN. Other examples will be apparent to those skilled in the art.

[0054] When implementing an embodiment using a non-uniform distribution of sensors and output points, it is recommended to first optimize the non-uniform output point positions. Assuming a clean (signal-only) output, one can search for the best non-uniform output positions that allow for optimal reconstruction. This is followed by a step of optimizing the input (sensor) positions to match the output points.

[0055] Adjusting any current sensor optimization techniques we have, we will then optimize the sensor locations to provide us with the cleanest output at the selected location. This may involve clustering sensor locations around an output point. The sensors within a swarm associated with a particular output point can be evenly distributed (e.g., a swarm of 5 sensors can be placed with 0.2 meter spacing, with the output point equidistant between the two outermost sensors). This will still result in an uneven or non-uniform distribution of sensors along the streamer, as the distance to the adjacent sensor swarm associated with the next output point along the streamer will be different from the spacing between sensors within the swarm (i.e., it will not be 0.2 meters in this example).

[0056] Of course, in some embodiments, sensors within a group coupled to a single output point may also be distributed unevenly. A non-uniform distribution of sensors or output points may, but does not always, mean that all distances between adjacent sensors or adjacent output points on a particular streamer segment are different from one another.

[0057] However, the distribution of sensors or output points may be considered non-uniform as long as at least some of the distances between adjacent sensors or adjacent output points on a streamer segment are different.

[0058] This two-stage optimization can also be coupled to a single stage, but this will require more expensive optimization work.

[0059] In some embodiments, it is possible (and advantageous) to produce more than one output per sensor cluster.

[0060] We can design noise attenuation methods to provide more than one point of output for a given sensor cluster. This can facilitate reconstruction; for example, by defining a complementary finite difference signal. An example of such a configuration is shown in Figure 7 In this example the average interval is greater than or equal to 12.5.

[0061] Figure 8 A schematic example of an array that is non-uniformly distributed along a streamer is disclosed. Each triangle represents a sensor, and a group of sensors connected together represents the sensor array. Note that the sensor array itself is distributed in a non-uniform manner along the streamer segment. A single channel will transmit data from all sensors within the array to the output point. Although Figure 8The sensor arrays are shown as being equally spaced along or within each sensor array, but this is for illustration purposes only. The sensors may also be non-uniformly spaced. The output point locations may correspond to the physical center and / or center of gravity of each array.

[0062] Figure 9 Numerical examples are shown. The data represented by the curves in the figure were created using a finite-difference code based on the SEAM model. The red box in the top panel shows the portion of the data used for testing. The left panel in the middle row shows the fk transform of the test data; the right panel shows the aliasing that would occur if the data were regularly sampled at a fixed interval of 12.5. The bottom row shows the test results, comparing a finely sampled reference with a reconstruction performed using the IAA-MP algorithm in a single tx-window.

[0063] If the sampling is uneven, the clean signal can be reconstructed (regularized) onto a regular grid. This signal has frequency components up to 125Hz, so if sampled regularly at 12.5m, there is obvious aliasing. Figure 7 The second panel in the bottom row shows an example where eight output points are non-uniformly distributed over 100m with an average spacing of 12.5m. The data is then normalized to a fine grid for comparison with the input. The reconstruction error using the IAA-MP algorithm is less than 5% RMS.

[0064] The density of at least one streamer segment has a density of sensors, channels, and / or sensor arrays that varies by a factor of at least 1.3 over a sliding window that is at least 5 meters long. For example, the sensor density within a 6.25 meter sliding window may vary by a factor of more than 1.5, 2, or 3 along the streamer. The sensor density within a 12.5 meter sliding window may vary by a factor of more than 1.5, 2, or 3 along the streamer. The sensor density within a 25 meter sliding window may vary by a factor of more than 1.5, 2, or 3 along the streamer, or the sensor density within a 50 meter sliding window may vary by a factor of more than 1.5, 2, or 3 along the streamer.

[0065] A sliding window refers to a movable streamer segment or portion in which the number of sensors can be counted to determine a measurement of sensor density. The number of sensors counted will, of course, depend on the size of this window. The measurement of sensor density at the streamer segment within the window can then be determined by dividing the counted number of sensors by the window size. By sliding the window along a segment of streamer (moving the position of the streamer segment being inspected), multiple measurements representing the sensor density at each streamer segment location can be determined. These locations may or may not overlap. For example, a statement that the sensor density varies by a factor of more than 1.5 within a 5m sliding window indicates that there will be a location on the streamer where the window can be positioned to provide a minimum density measurement and a location where the window can be positioned to provide a maximum density measurement, and that the maximum density measurement will be more than 1.5 times the minimum density measurement.

[0066] In some cases, the density measured at certain locations in the window may be zero. In one example streamer, sensors are arranged in clusters, with each cluster covering approximately 3 meters of the streamer. These 3-meter-long sensor clusters are typically each connected to a single output point located in the center of the cluster (approximately 1.5 meters from each outermost sensor), but may also be connected to multiple output points. The output points are spaced approximately 12.5 meters apart along the streamer segment.

[0067] The distances between the output points may not be uniform, so that the output points are spaced an average of 12.5 meters apart along the streamer segment. In this case, the average gap between sensor clusters is approximately 9.5 meters. When the 5 meter window is above this gap, there will be no sensors within the window, and the density will be measured as zero. In this case, the ratio of the highest density measured in a sliding window on the streamer (which can be a 1 meter, 2.5 meter, 5 meter, 6.25 meter, 12.5 meter, or 25 meter sliding window) to the lowest non-zero density measured in a sliding window of the same size (e.g., a window containing 1 sensor) will exceed 1.5, 2, or 3.

[0068] If a sliding window at a point on the streamer measures zero density, then the ratio between the highest density measured in a 5m sliding window and the lowest non-zero density measured in a 1m, 2m, 5m, 6.35m, 12.5m, or 25m sliding window will exceed 1.5, 2, 3, 5, or 10. Any combination of these window sizes and ratios is intended to be covered herein.

[0069] For a given length of streamer, how many output points are needed to achieve a given signal quality will depend on the frequency of the signal being measured. For higher frequency signals, the output points will need to be spaced closer together (the density of output points will be higher) in order to achieve a good reproduction of the signal in the processing stage. This is because too low a sampling rate may not be able to distinguish between two signals of different frequencies. For higher frequency signals, a higher sampling rate is generally required. However, in the case of seismic reflections at the streamer sensors, the noise is higher at lower frequencies (see Figure 4 ). Therefore, for lower frequencies, a lower density of output points is required, but the number of sensors required to feed the signal to each output point needs to be greater to offset the effects of the higher noise level in the signal.

[0070] To cater for both high and low frequencies in the signal while minimizing the number of sensors required for a particular streamer segment, the sensors can be as follows: Figure 10 As shown, the output points (and sensor clusters) are evenly distributed along the streamer in the top row, and non-evenly distributed in the second row. In the example shown, the sensors are positioned in clusters, each cluster centered around an output point, and the sensors are clustered around their associated output points as described above.

[0071] Clusters of five sensors (green / black triangles) are placed every 12.5 meters along the streamer. Smaller clusters of three sensors (pink / light triangles) are placed every 6.25 meters. Some (in this case, three) sensors in each larger cluster also form one of the smaller clusters of sensors. Some or all of the sensors in the cluster of five sensors are thus used to provide output for both the lower frequency signal and the higher frequency signal. Both frequencies meet the requirements for noise reduction and sampling rate, but fewer sensors are used than in the case of clusters of five sensors placed every 6.25 meters along the streamer.

[0072] Of course, the exact distances given are examples only, and the distance between groups and the number of sensors in each group may be optimized for specific situations or where specific types of signals are to be detected. Groups of 3 to 12, preferably 4 to 10, more preferably 4 to 6, most preferably 5 sensors may be provided every 10 to 25 meters, preferably every 10 to 20 meters, more preferably every 11 to 15 meters, and most preferably every 12.5 meters along the streamer, with smaller groups of 1 to 5, preferably 2 to 4, most preferably 3 sensors provided at shorter intervals, for example every 2 to 15 meters, preferably every 4 to 8 meters, and most preferably every 6.25 meters.

[0073] The distances between clusters can be averaged so that the distribution of output points along the streamer segment, and the distribution of sensors along the streamer segment, is non-uniform to reduce the effects of noise and aliasing as described above (e.g., Figure 10(As shown in the lower example distribution shown). If a non-uniform distribution of output points is used, fewer points may be needed along the streamer and the average distance between larger and smaller groups may be increased. A particular pattern of groups can be repeated multiple times along each streamer segment, and multiple such streamer segments can be connected to form longer streamers.

[0074] To account for higher frequencies, additional single sensors can be included between clusters, so that an output point is placed every 3.125 meters, or, if a non-uniform distribution of clusters and / or output points is used, so that an output point is placed every 3.125 meters on average. These sensors will be located between the 5-sensor cluster and the 3-sensor cluster. If a non-uniform cluster distribution is used, each pair of adjacent large and small sensor clusters will have a single sensor halfway or on average halfway.

[0075] The table below summarizes the estimated requirements for each frequency range. This table is particularly relevant when using particle motion sensors, but may also be relevant in other situations.

[0076] While it is preferred that the distribution of sensors and output points be non-uniform along the streamer segment, this is not necessary to tailor the frequencies used to different groups. For example, sensors could be positioned at fixed intervals along the length of the streamer, and the output points used for different frequency measurements simply linked to specific groups of these sensors, thereby meeting the requirements for the number of sensors and sampling frequency.

[0077]

[0078] Figure 11 A flow chart showing a method for reproducing a signal including two sets of sensor groups to account for low and high frequencies in the signal is shown. Figure 11 In the illustrated approach, the groups are evenly distributed along the streamer (this typically means the output points are also evenly distributed, as these are typically located at the center of each group of sensors). A first group of sensors is specifically designed to attenuate noise in a first frequency band, while a second group of sensors is specifically designed to attenuate noise in a higher frequency band. These sensors are spaced along the streamer so that the second group is located between the first groups. Some sensors will belong to both groups. Noise-attenuated output in the first frequency band is obtained from sensors in the first group and spatially interpolated to smaller spacings to correlate with the spacing of the second group's output points. Noise-attenuated output in the second frequency band can be collected for the smaller spacings of the output points from both groups (or sensors that only form part of the second group). These outputs for both frequency bands are combined, resulting in smaller spacings of outputs for both frequency bands from the second group. When acquiring a signal for the first frequency band, only the signal from the larger first group can be used (because this avoids using noise signals from the second group, which has fewer sensors). In some cases, signals from both groups can be used simultaneously.

[0079] Figure 12 is a flow chart of a similar method for signal reproduction. Figure 12 The method shown involves non-uniformly spaced sensor clusters along the streamer. In this case, the output points linked to the first and second clusters are non-uniformly spaced. This method is similar to Figure 11 method, but it must be used when processing signals from two groups of sensors Figure 10 to create a set of noise-attenuated regularly spaced outputs from non-uniformly distributed output points.

[0080] So far, we have only discussed operations in the common source setting. We can also consider the possibility of incorporating non-uniformly sampled source points into the high-dimensional reconstruction problem. It is well known that high-dimensional signal representations are sparser than low-dimensional ones, thus improving the reconstruction quality using CS techniques. This in turn can allow for a sparser sampling of the output points in the first place.

[0081] The non-uniform sampling source points mentioned here can be considered as the coaxial direction (the general direction of the streamer) and the cross direction. Therefore, the coaxial (in-line) direction refers to the direction along the longitudinal direction or parallel to the longest side of the streamer. The cross-line direction refers to the direction perpendicular to the coaxial direction, or generally parallel to the shortest side of the streamer. The sensors and / or output points can be non-uniformly distributed in either or both of the coaxial direction along the streamer and the cross direction along the streamer, or can have varying densities. In addition, streamers that are unevenly spaced in the cross direction can also be considered, which increases the sparsity of the signal in the high-dimensional signal reconstruction method using compressed sensing methods. The same non-uniform distribution of output points and sensors can be applied to the cross direction and or instead of the coaxial direction. In order to extend the sensor coverage in the cross direction and along each streamer, several streamers need to be towed side by side off the rear of the ship, or the streamer section needs to have a certain thickness to allow the sensor to be installed on a specific streamer in a 2D rather than a 1D configuration.

[0082] In one embodiment, the sensor density is highest around the output point and decreases between the output points. In another embodiment, there is no clustering around the output point. In these examples, the distribution of sensors is uneven.

[0083] Fewer output points (tracks) will enable cheaper compressed sensing algorithms. Although these methods are generally computationally expensive; their use will be more affordable given the small number of output tracks that need to be processed compared to the number of input tracks.

[0084] We use the word cluster in a general sense because there can be sensors between these locations. This approach can find applications in streamer, seabed, node, borehole, and land seismic data acquisition scenarios.

[0085] Typically, a streamer will comprise a plurality of streamer segments. Where there is only one streamer segment, the streamer segment may be referred to as a streamer.

[0086] The arrays (if used) may all be of the same type or vary by location on the streamer.

[0087] The non-uniform distribution of sensors discussed above can be applied to channels and analog arrays of sensors. Channels, sensors, and analog arrays of sensors can be collectively referred to as measurement volumes. For the present invention, any measurement volume can be used.

[0088] Item 1. A streamer comprising at least one streamer segment, the at least one streamer segment comprising sensors, wherein a density of the sensors varies along the at least one streamer segment.

[0089] Item 2. A streamer according to Item 1, wherein at least one streamer segment includes multiple output points, data from a group of sensors are fed to the multiple output points, and the sensor density is highest around the output points and the sensor density is lower between the output points.

[0090] Item 3. A streamer according to any one of Items 1 and 2, wherein the sensor is a particle motion sensor and / or a pressure sensor.

[0091] Item 4. A streamer according to any one of items 1 to 3, wherein the density of sensors measured within a 5m sliding window varies along the streamer by a factor of at least 1.3.

[0092] Item 5. A streamer according to Item 4, wherein the density of the sensor measured within a 6.25m sliding window varies by more than 1.5, 2 or 3 times, the density of the sensor measured within a 12.5m sliding window varies by more than 1.5, 2 or 3 times, the density of the sensor measured within a 25m sliding window varies by more than 1.5, 2 or 3 times, or the density of the sensor measured within a 50m sliding window varies by more than 1.5, 2 or 3 times.

[0093] Item 6. A streamer according to Item 2, wherein the density of sensors in a streamer segment as a function of the center position of the streamer segment is defined as a sensor density function, and wherein the local maxima of the sensor density function have a non-uniform distribution.

[0094] Item 7. The streamer of any one of items 1 to 6, wherein the separation distance between adjacent output points along the streamer is not constant.

[0095] Item 8. A streamer according to any one of items 1 to 7, wherein one or more of the sensors comprises an analog array of sensors.

[0096] Item 9. The streamer of any one of items 1 to 8, wherein each sensor is associated with a single channel for transmitting data from the sensor to an output point.

[0097] Item 10. The streamer of any one of items 1 to 9, wherein each of the output points is located midway between the positions of outermost sensors in a group of sensors from which the output point receives data.

[0098] Item 11. The streamer of any one of items 1 to 10, wherein the sensors are grouped as:

[0099] a plurality of first sensor clusters, each first sensor cluster comprising 3 to 7 sensors and configured to feed data to associated output points, wherein the output points fed by the first sensor clusters are spaced apart along the streamer at an average interval of 5 to 20 meters;

[0100] A plurality of second sensor groups, each second sensor group including fewer sensors than the number of sensors in each of the first sensor groups, the second sensor groups being located along the streamer between adjacent first sensor groups and each second sensor group being configured to feed data to an associated output point.

[0101] Item 12. The streamer of Item 11, wherein the first sensor clusters are spaced 10 to 15 meters apart along the streamer.

[0102] Item 13. The streamer of Item 12, wherein the first sensor clusters are spaced approximately 12.5 meters apart along the streamer on average.

[0103] Item 14. A streamer according to any one of items 11 to 13, wherein the first sensor groups each include 5 sensors and the second sensor groups each include 3 sensors.

[0104] Item 15. A streamer according to any one of Items 11 to 14, wherein all output points on the streamer are configured to forward to process sensor data associated with signals having a frequency greater than a threshold frequency, and only the output points associated with the first sensor group are configured to forward to process data associated with signals having a frequency equal to or less than the threshold frequency.

[0105] Item 16. The streamer of Item 15, wherein the threshold frequency is approximately 60 Hz.

[0106] Item 17. A method for optimizing the distribution of streamers along a plurality of sensors and a plurality of output points, to which data from a set of sensors is fed, the method comprising:

[0107] Determine the location of the output point along the streamer; and

[0108] Using the determined output point locations, sensor groups are configured to feed data to each output point and positioned along the streamer, wherein the sensor groups are positioned such that the distribution of sensors along the streamer is non-uniform.

[0109] Item 18. The method of Item 17, wherein each output point is located midway between the locations of the outermost sensors of the sensor group from which it is configured to receive data.

[0110] Item 19. The method of any one of Items 17 and 18, wherein the sensor positions are determined so that the density of sensors is highest around the output points and lowest between the output points.

[0111] Item 20. The method of any one of items 17 to 19, wherein the step of determining the positions of the output points includes ensuring that the output points are non-uniformly distributed along the streamer.

[0112] Item 21. The method of Item 20, wherein the method comprises reconstructing a set of regularly spaced outputs along the streamer from signals at output points using a compressed sensing algorithm, wherein the number of regularly spaced outputs is greater than the number of output points.

[0113] Item 22. A computer-readable program storage medium encoded with instructions that, when executed by a processor, perform the method according to any one of items 17 to 21.

[0114] Item 23. A streamer comprising one or more streamer segments, each streamer segment having a plurality of sensors mounted along it and a plurality of output points into which data from a set of sensors is input, the sensors comprising:

[0115] a plurality of first sensor groups, each first sensor group comprising 3 to 7 sensors and configured to feed data to one of the output points, wherein the output points fed by the first sensor groups are spaced on average 5 to 30 meters apart along the streamer;

[0116] A plurality of second sensor groups, each second sensor group including fewer sensors than the number of sensors in each first sensor group, are positioned between adjacent first sensor groups along the streamer and are each configured to feed data to an output point.

[0117] Item 24. The streamer of Item 23, wherein the first sensor groups are spaced 10 to 15 meters apart along the streamer.

[0118] Item 25. The streamer of Item 24, wherein the first sensor groups are spaced 12.5 meters apart along the streamer.

[0119] Item 26. A streamer according to any one of items 23 to 25, wherein the first sensor groups each include 5 sensors and the second sensor groups each include 3 sensors.

[0120] Item 27. A streamer according to any one of items 23 to 26, wherein each output point is located midway between the positions of the outermost sensors in the sensor group from which it receives data.

[0121] Item 28. The streamer of any one of Items 23 and 27, wherein the first sensor groups are spaced approximately 12.5 meters apart along the streamer.

[0122] Item 29. The streamer of any one of items 23 to 28, wherein the second sensor group is located midway along the streamer between adjacent first sensor groups.

[0123] Item 30. A streamer according to any one of Items 23 to 29, wherein all output points on the streamer are configured to forward sensor data associated with signals having a frequency greater than a threshold frequency for processing and only output points associated with the first sensor group are configured to forward data associated with signals having a frequency equal to or less than a threshold frequency for processing.

[0124] Item 31. The streamer of Item 30, wherein the threshold frequency is approximately 60 Hz.

[0125] Item 32. A streamer according to any one of items 23 to 31, wherein the density of the sensors is highest around the output points and lowest between the output points.

Claims

1. A marine seismic streamer for simultaneously sampling seismic signals and noise, wherein: The marine seismic streamer includes at least one streamer segment including a plurality of sensors and a plurality of output points to which data from a set of sensors is fed, the plurality of sensors being placed at non-uniform intervals along the at least one streamer segment to avoid hard aliasing.

2. The marine seismic streamer according to claim 1, wherein: Compressed sensing is used to reconstruct the seismic signal and the noise.

3. The marine seismic streamer according to claim 2, wherein: During reconstruction, the noise is attenuated.

4. The marine seismic streamer according to claim 1, wherein: The plurality of sensors includes a first group of sensors for attenuating noise in a first frequency band and a second group of sensors for attenuating noise in a second frequency band higher than the first frequency band.

5. The marine seismic streamer according to claim 4, wherein: The plurality of sensors are spaced apart along the marine seismic streamer such that the second set of sensors is located between the first set of sensors.

6. The marine seismic streamer according to claim 1, wherein: The plurality of output points are distributed uniformly or non-uniformly along the at least one streamer segment.

7. The marine seismic streamer according to claim 1, wherein: The plurality of sensors are particle motion sensors and / or pressure sensors.

8. A method for acquiring marine seismic data, wherein: A marine seismic streamer is used to simultaneously sample seismic signals and noise, the marine seismic streamer comprising at least one streamer section, the at least one streamer section comprising a plurality of sensors and a plurality of output points, data from a set of sensors being fed to the output points, the method comprising: The plurality of sensors are positioned at non-uniform intervals along the at least one streamer segment to avoid hard aliasing.

9. The method according to claim 8, wherein The seismic signal and the noise are reconstructed using compressed sensing.

10. The method according to claim 9, wherein: Reconstructing the seismic signal and the noise includes attenuating the noise.

11. The method according to claim 10, wherein: The plurality of sensors include a first group of sensors and a second group of sensors, Attenuating the noise includes attenuating noise in a first frequency band using the first set of sensors and attenuating noise in a second frequency band using the second set of sensors, wherein the second frequency band is higher than the first frequency band.

12. The method according to claim 11, wherein Positioning the plurality of sensors at non-uniform intervals along the at least one streamer segment includes positioning the second set of sensors between the first set of sensors.

13. The method of claim 8, comprising distributing the plurality of output points uniformly or non-uniformly along the at least one streamer segment.

14. The method according to claim 8, wherein The plurality of sensors are particle motion sensors and / or pressure sensors.

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