GB2495906A

GB2495906A - Identifying objects buried close to the surface from surface wave and p-wave data extracted from seismic survey data

Info

Publication number: GB2495906A
Application number: GB201117884A
Authority: GB
Inventors: David Walker
Original assignee: SEG SQUARED Ltd
Current assignee: SEG SQUARED Ltd
Priority date: 2011-10-17
Filing date: 2011-10-17
Publication date: 2013-05-01
Also published as: GB201117884D0; WO2013057484A2; WO2013057484A3

Abstract

A method of processing seismic survey data to identify objects buried close to the surface in a region of interest comprises extracting surface wave data from the seismic survey data, processing the surface wave data to identify areas in which buried objects are not located, extracting p-wave data from the seismic survey data for the areas in which buried objects are located, and processing the p-wave data for the areas in which buried objects are located to identify the buried object. The object may be an IED (improvised explosive device). The p-wave data may be used to determine velocity signatures for grid cells representing areas in which buried objects are located, and the velocity signatures may be used to identify characteristics of the buried objects. Electromagnetic survey data may additionally be used to determine conductivity and/or attenuation signatures. Also disclosed is a tomographic post comprising a seismic source located in a chamber between inner and outer housing, wherein the chamber is pressurised with an inert gas.

Description

Seismic survey data collection and processing

Technical Field of the Invention

The invention relates to the collection and processing of seismic survey data, and in particular relates to the improved collection and processing of seismic survey data for detecting and/or locating buried objects.

Background to the Invention

In the oil and gas industry, seismic survey data is collected and processed in order to identify subsurface regions where oil and gas may be found. In a seismic survey, a seismic source and one or more receivers are generally located on or near the surface, and the seismic source is actuated to emit a seismic wave that propagates generally downwards through the ground. When the seismic wave encounters a boundary between two types of material having different physical properties, some of the energy of the seismic wave is reflected back towards the surface, where is it detected by the receiver(s).

The data collected during the seismic survey, referred to as a seismogram, is then proceased to reduce noise before being analysed or interpreted using knowledge of the physical properties of various types of material in order to determine or model the structure of the subsurface region being surveyed. Due to the large amount of data to be analysed in a seismic survey, conventional techniques for processing seismograms can often smooth' the data, which can lead to the loss of information that is valuable for characterising the structure of the subsurface region. However, this smoothing makes it easier to determine a model for the subsurface region. The resolution generally obtained in a conventional seismic survey is around 12.5m in the horizontal plane and around 5m in the vertical direction. Typically, the seismic source emits seismic waves having a relatively low frequency in order to reduce the affects of attenuation and noise as the wave propagates through the ground.

In another type of seismic survey, referred to as cross-well tomography, a seismic source is placed in a borehole and a receiver or array of receivers are placed in a neighbouring borehole. The source emits seismic waves, and the receiver(s) detect the energy of the seismic waves that have propagated in a generally lateral direction between the boreholes. This seismic data can then be processed in order to model the properties of the subsurface region between the source and receiver(s).

In addition to its use in identifying and locating possible reservoirs of gas and oil, efforts have been made to use seismic survey data to identify and locate objects, structures or anomalies that are buried near to the surface (typically within the first 5m) over a significant area (for example an area of around 1km2). In particular, these efforts have focussed on the identification of buried devices such as land mines or improvised explosive devices (IEDs), which can have dimensions of no more than 30 cm. Other applications include the use of seismic surveys in archaeological surveying or for locating precious metals.

However, these efforts have so far proved unsuccessful. Firstly, the resolution obtainable with existing seismic data analysis techniques does not allow objects of the required size to be identified; and secondly, the seismic data obtained using a conventional cross-well tomography apparatus used near or at the surface contains too 16 much noise, which means that it is not possible to detect objects that are a significant distance (for example 1km) from the wave source.

Although attempts have been made to detect objects, structures or anomalies buried close to the surface using alternative techniques, these have also proved unsuccessful.

For example, ground penetrating radar does not provide a sufficient resolution, and a gravitational and magnetic study does not provide enough data to be used in isolation (and, in the case where lEDs are to be detected, there is a risk that they could be detonated while performing the survey).

Therefore, there is a need for a method and system that is able to detect and identify objects, structures or anomalies buried close to the surface (such as IEDs).

Summary of the Invention

According to a first aspect of the invention, there is provided a method of processing seismic survey data to identify objects buried close to the surface in a region of interest, the method comprising extracting surface wave data from the seismic survey data; processing the extracted surface wave data to identify areas in the region of interest in which buried objects are not located; extracting p-wave data from the seismic survey data for the areas in which buried objects are located; and processing the extracted p-wave data for the areas in which buried objects are located to identify the buried object.

In some embodiments, the step of extracting the surface wave data comprises filtering the seismic survey data to remove p-wave data and/or noise and interference.

In some embodiments, the step of processing the extracted surface wave data to identify areas in the region of interest in which buried objects are not located comprises comparing the velocity of surface waves within the extracted surface wave data to a predetermined range of velocities for the surface waves, and identifying areas in which buried objects are not located as those areas where the associated surface waves have a velocity within the predetermined range of velocities.

In some embodiments, the step of processing the extracted surface wave data to identify areas in the region of interest in which buried objects are located comprises identifying surface waves within the extracted surface wave data having velocities not falling within a predetermined range.

In some embodiments, the step of processing the extracted surface wave data comprises using a brute-stack method to convert the surface wave data into a 3D volume; determining the first surface wave in each sequence; and mapping the first surface waves into a 2D surface image.

In some embodiments, the step of processing the p-wave seismic data comprises reducing noise in the extracted p-wave seismic data.

In some embodiments, the step of processing the p-wave seismic data comprises convo)ving a wavelet with each trace in the extracted p-wave seismic data to give wavelet data; and converting the wavelet data into acoustic impedance data.

In some embodiments, the step of processing the p-wave seismic data further comprises reducing noise in the acoustic impedance data by discarding data where changes in the vertical direction are smaller than a predefined threshold.

In some embodiments, the method further comprises the steps of identifying lateral continuities in the seismic survey data larger than a predetermined size; and discarding the seismic data that corresponds to the identified lateral continuities, such that the discarded data is not processed in subsequent processing steps.

In some embodiments, the step of processing the extracted p-wave seismic data for the areas in which buried objects are located comprises processing the extracted p-wave seismic data to determine velocity signatures for each grid cell in a plurality of grid cells representing the areas.

In some embodiments, the step of processing the extracted p-wave seismic data for the areas in which buried objects are located further comprises examining the velocity signatures to identify characteristics associated with a buried object.

In some embodiments, the step of processing the extracted p-wave seismic data for the areas in which buried objects are located comprises processing the extracted p-wave seismic data to determine attenuation signatures for each grid cell in a plurality of grid cells representing the areas.

In some embodiments, the method further comprises the steps of processing electro-magnetic survey data for the areas in which buried objects are located to determine conductivity and/or attenuation signatures for each grid cell in a plurality of grid cells representing the areas; and using the conductivity and/or attenuation signatures determined from the electro-magnetic survey data with the results of the processing of the extracted p-wave seismic data to identify the composition of the buried object.

In some embodiments, the method further comprises the step of comparing the determined signatures to a plurality of signatures corresponding to respective types of materials.

In some embodiments, the method further comprises the steps of storing information on buried objects identified in the region of interest; and using the stored information on buried objects identified in the region of interest in the processing of subsequently obtained seismic survey data for the region of interest.

According to a second aspect of the invention, there is provided a processing unit for processing seismic survey data to identify objects buried close to the surface in a region of interest, the processing unit being configured to perform the method as described above.

In some embodiments, the processing unit is configured to extract the surface wave data by filtering the seismic survey data to remove p-wave data and/or noise and interference.

In some embodiments, the processing unit is configured to process the extracted surface wave data to identify areas in the region of interest in which buried objects are not located by comparing the velocity of surface waves within the extracted surface wave data to a predetermined range of velocities for the surface waves, and identifying areas in which buried objects are not located as those areas where the associated surface waves have a velocity within the predetermined range of velocities.

In some embodiments, the processing unit is configured to process the extracted surface wave data to identify areas in the region of interest in which buried objects are located to identify surface waves within the extracted surface wave data having velocities not falling within a predetermined range.

In some embodiments, the processing unit is configured to process the extracted surface wave data by using a brute-stack method to convert the surface wave data into a 3D volume; determining the first surface wave in each sequence; and mapping the first surface waves into a 2D surface image.

In some embodiments, the processing unit is configured to process the p-wave seismic data to reduce noise in the extracted p-wave seismic data.

In some embodiments, the processing unit is configured to process the p-wave seismic data by convolving a wavelet with each trace in the extracted p-wave seismic data to give wavelet data; and converting the wavelet data into acoustic impedance data.

In some embodiments, the processing unit is configured to process the p-wave seismic data to reduce noise in the acoustic impedance data by discarding data where changes in the vertical direction are smaller than a predefined threshold.

In some embodiments, the processing unit is further configured to identify lateral continuities in the seismic survey data larger than a predetermined size; and discard the seismic data that corresponds to the identified lateral continuities, such that the discarded data is not subsequently processed by the processing unit.

In some embodiments, the processing unit is configured to process the extracted p-wave seismic data for the areas in which buried objects are located by processing the extracted p-wave seismic data to determine velocity signatures for each grid cell in a plurality of grid cells representing the areas, In some embodiments, the processing unit is configured to process the extracted p-wave seismic data for the areas in which buried objects are located by examining the velocity signatures to identify characteristics associated with a buried object.

In some embodiments, the processing unit is configured to process the extracted p-wave seismic data for the areas in which buried objects are located by processing the extracted p-wave seismic data to determine attenuation signatures for each grid cell in a plurality of grid cells representing the areas.

In some embodiments, the processing unit is further configured to process electro-magnetic survey data for the areas in which buried objects are located to determine conductivity and/or attenuation signatures for each grid cell in a plurality Df grid cells representing the areas; and use the conductivity and/or attenuation signatures determined from the electro-magnetic survey data with the results of the processing of the extracted p-wave seismic data to identity the composition of the buried object.

In some embodiments, the processing unit is further configured to compare the determined signatures to a plurality of signatures corresponding to respective types of materials.

In some embodiments, the processing unit is further configured to store information on buried objects identified in the region of interest in a memory module; and to use the stored information on buried objects identified in the region of interest in the processing of subsequently obtained seismic survey data for the region of interest, According to a third aspect of the invention, there is provided a tomographic post for use in seismic surveys, comprising an outer housing; an inner housing located inside the outer housing, the inner and outer housings being arranged to form a chamber therehetween; and a seismic wave source located within the inner housing for generating seismic waves; wherein the chamber is configured to be pressurised to a predetermined pressure with an inert gas prior to generation of seismic waves.

According to a fourth aspect of the invention, there is provided a seismic survey system, comprising a processing unit as described above for processing the seismic survey data obtained using the one or more tomographic posts.

Preferably, the seismic survey system comprises one or more of the tomographic posts described above.

There is also provided a computer program product comprising computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is configured to perform the method as described above.

Brief Description of the Drawings

Embodiments of the invention will now be described, by way of example only, with reference to the following drawings, in which: Figure 1 is a block diagram of a system for collecting and processing seismic data in accordance with an embodiment of the invention; Figure 2 is a schematic diagram of a tomographic post that can be used in the system of Figure 1; Figure 3 is a graph illustrating the attenuation of a p-wave emitted by a conventional tomographic post and a tomographic post according to an aspect of the invention; Figure 4 is an illustration of an arrangement of tomographic posts according to an aspect of the invention; Figure 5 illustrates two exemplary regions having differing geology; Figure 6 shows the difficulties of using a direct arrival technique to process seismic data; Figure 7 shows a two-stage process according to an embodiment of the invention; Figure 8 is a flow diagram illustrating the steps performed in processing the data collected using the system of Figure 1; Figure 9 is a graph illustrating unprocessed seismic data; Figure 10 is a graph illustrating the seismic data of Figure g after processing to remove the p-wave data and to enhance the surface wave data; Figure 11 is a flow diagram illustrating steps 64 and 66 in Figure 8 in more detail; Figure 12 illustrates the extraction and processing of surface wave data; Figure 13 is an illustration of a buried object; Figure 14 is an illustration of the processing according to the invention overlaid on seismic data; Figure 15 is an illustration of seismic data exhibiting lateral continuity; Figure 16 shows the relative velocity or densities for normal geology and a buried object; Figure 17 is a plot of seismic data in which there are two anomalies; Figure 18 shows un-processed seismic data in which the normal sequencing is broken up; Figure 19 is a map view of a detected anomaly; Figure 20 is a graph illustrating the relative velocity of a p-wave in different materials; and Figure 21 is a graph illustrating the relative conductivity of different materials.

Detailed Descrintion of the Preferred Embodiments Although the invention will be described below in terms of locating and identifying improvised explosive devices (lEDs), it will be appreciated that the tomographic post described herein and the improved techniques for processing the collected seismic data can be used to identify any other type of object, structure or anomaly buried close to the surface. In addition, it will be appreciated by those skilled in the art that the improved tomographic analysis techniques can be used in other applications, such as security and medical analysis.

A system for collecting and prccessing seismic data in accordance with an embodiment of the invention is shown in Figure 1. The system 2 comprises one or more tomographic posts 4 and at least one processing unit, such as a computer 6 for processing the data obtained by the tomographic posts 4.

The tomographic post 4 comprises a seismic wave source (transmitter) 8 for emitting seismic waves, an electro-magnetic (EM) wave source (transmitter) 10 for emitting electra-magnetic waves and a receiver or receiver array 12 for receiving and measuring seismic and EM waves incident on the post 4.

The operation of the tomographic post 4 is controlled by a control unit 14, which is connected to the transmitters 8, 10 and receiver 12. The post 4 also comprises a memory module 16 for storing the seismic and EM data obtained by the receiver or receiver array 12. Although not shown in Figure 1, the control unit 14 may comprise a seismograph which converts the seismic and/or EM data provided by the receiver or receiver array 12 into a suitable format for subsequent processing. One such format is the SEG-Y file format.

The seismic and EM data obtained by the receiver 12 is provided by the control unit 14 to a computer 6 that is configured to process the data or SEG-Y file to identify any objects, structures or anomalies buried close to the surface. The computer 6 comprises a processing unit 18, a memory module 20 for storing the data from each of the tomographic posts 4 in the system 2, a user interface 22, such as a keyboard, keypad, mouse, touch screen, etc. and a display 24 for displaying the results of the processing by the processing unit 18. The computer 6 may also be used to coordinate the activation and operation of the tomographic posts 4.

In this embodiment, each tomographic post 4 comprises source and receiver modules 8, 10, 12 that enable each post 4 to emit seismic and/or electro-magnetic waves and receive the waves reflected from objects or other boundaries or interfaces in the ground. Several (for example, three or four) tomographic posts 4 can be distributed around an area or region to be analysed or monitored, and each post 4 can be controlled to emit seismic and/or electro-magnetic waves in turn. The receiver modules 12 of each post 4 (including the post 4 that emitted the wave(s) if there are any reflections) receive the wave(s) and output data for the received wave(s). The use of multiple posts 4 allows the Iccation of any anomaly or other detected feature to be determined using triangulation.

In an alternative embodiment to that illustrated, the source and receiver modules 8, 10, 12 can be comprised in separate posts.

It will be appreciated that in alternative embodiments of the invention, the system 2 may only make use of seismic waves to detect anomalies, in which case the tomographic post 4 may comprise only source 8. However, it will be appreciated that the use of EM waves Improves the ability to determine the composition of any buried object, structure or anomaly.

As described above in the Background section, seismic data obtained using conventional tomographic posts or seismic sources contains too much noise and does not allow objects of the required size (e.g. around 30cm) or dispersed over the required area (e.g. around 1km) to be detected.

Typically, tomographic posts generate seismic (pressure) waves having relatively low frequencies in order to decrease the effects of attenuation and noise. However, this limits the effective resolution and range of the posts. Thus, one aspect of the invention provides a tomographic post that generates a seismic (pressure) wave having a much higher amplitude and frequency that is not as susceptible to attenuation and noise as those generated by conventional posts. This enables the effective range of the tomographic post to be increased and improves the resolution of objects or anomalies that can be detected in the data.

A tomographic post 4 according to an embodiment of the invention is shown schematically in Figure 2. It will be noted that those elements of the tomographic post 4 that are also shown in Figure 1 have been given the same reference numerals in Figure 2.

The tomographic post 4 comprises a first, outer, housing 26 and a second, inner, housing 28 that is located within the first housing 26, such that a chamber 30 is formed between the walls of the two housings 26, 28. At least the seismic wave source 8 is located inside the inner housing 28, and in this illustrated embodiment, the EM-wave source 10, receiver array 12 and control unit 14 are also located inside the inner housing 28, although in alternative embodiments these components can be located outside the inner housing 28.

When the tomographic post 4 is in use, i.e. when it is to be used to emit a seismic wave, the chamber 30 between the two housings 26, 28 is pressurised with an inert gas. The inert gas may be, for example, nitrogen. The gas may be pressurised to between 300 psi (2.07 MPa) and up to a maximum pressure of 1500 psi (approximately 10.34 MPa). The gas is provided from a gas reservoir 32 via a pump or valve 34. The control unit 14 determines the required pressure and controls the pump or valve 34 so that gas is supplied into the chamber 30 until it reaches the required pressure.

Once the required pressure has been attained, the seismic wave source 8 can be activated to generate a seismic waves (in particular pressure (p-) waves and surface waves) in the form of a Rickerwavelet.

The p-wave emitted by the tomographic post 4 is a focussed, pulsed p-wave that moves radially outwards from the post 4 in a generally horizontal field. The focussing of the p-wave described above results in significantly reduced ground attenuation and signal diminution compared to standard seismic (p and surface -s) waves. This allows the p-waves to be emitted at a much higher frequency and power than conventional waves, thereby improving the resolution of the subsequent analysis and the effective range of the post 4. In the subsequent processing of the seismic data, the wavelet reflections are handled laterally rather than vertically, effectively turning the p-wave on its side. The actual frequency and power of the p-waves emitted by the tomographic post 4 will depend on the environment in which the system 2 is deployed. Figure 3 shows a rough comparison of the attenuation of a conventionally-generated p-wave with distance from the source (line 40) and a p-wave generated using the tomographic post described above (line 42).

The tomographic post 4 is controlled to emit a series of discrete p-waves, which allows the region to be monitored to be repeatedly scanned and the received data to be built up into a detailed image, as described further below.

Figure 4 illustrates how multiple tomographic posts as described above can be used to monitor a region of interest. In particular three tomographic posts labelled 4k 4B and are arranged (specifically buried to a shallow depth of around 0-2 metres) around a region 50 to be monitored. Within the illustrated region 50 are two types of terrain having differing physical characteristics and a potential threat 52. such as an lED.

Each of the tomographic posts 4k 4B and 4C are operated in turn to emit p and surface, S, waves as described above and the receivers in each post record the received p-waves. As shown in Figure 4, p-waves emitted by post 4A may propagate directly through the first type of terrain and be received at the other two posts 4B and 40. The emitted p-wave may also be reflected at the boundary between the two terrain types towards post 4B. In addition, the p-wave may he reflected by the potential threat 52 back towards the source post 4A.

Once each of the posts has been operated and an appropriate amount of data collected, the data can he processed to trace reflections in the data and to identify the presence and/or location (using triangulation) of the potential threat 52. Furthermore, in some embodiments it is possible to determine the composition or part of the composition of the potential threat 52.

Although the data collected by the tomographic posts 4 according to the invention can be processed using conventional processing techniques in order to trace the reflections and identify buried objects, structures or anomalies, in a preferred embodiment the data is processed using the improved seismic data analysis techniques described below.

One of the limitations with conventional analysis of seismic data is that it is constrained by mathematics, physics and geology to the point where it cannot provide reliable identification of anomalies. For example, Figure 5(a) shows a mainly homogeneous region in which area 52 corresponds to higher velocities, area 54 corresponds to lower velocities and area 56 represents an area for which it is not possible to resolve the velocity and/or other properties at the required resolution due to limitations with the conventional processing techniques. This problem is more pronounced in regions of more complex geology, for example as shown in Figure 5(b).

Figure 6(a) illustrates the difference in distance travelled for a wave (A) that propagates straight through an anomaly located between a transmitter (8) and receiver (12) and a wave (B) that reflects from the boundary at the base of the anomaly. The difference between distance A and distance B is so small that any attempt to measure them is not possible given the noise and other factors present in the received seismic data. Thus, a direct arrival' method, used in isolation, is not able to discover anomalies of the required size with any degree of certainty.

However, one effect of the improved seismic data analysis techniques described below and the tomographic post 4 described above is that the distance between the source and receiver is effectively lowered, which means that the difference between distance A and distance B becomes more evident and is measurable by the data analysis technique. This is illustrated in Figure 6(b).

Briefly, the algorithm acquires, processes and images surface wave scatter data from the seismic data in order to identify regions in the area of interest that are anomaly-free (i.e. are safe zones') before applying a second tomography stage, namely a direct arrival (p-wave based) seismic method, to identify actual anomalies (including their shape and/or composition, where required) in the area(s) of interest not identified by the surface scatter wave analysis as anomaly-free. Thus, the algorithm uses higher resolution/shod distance surface wave acquisition and processing together with complex solutions in the direct arrival seismic method.

As shown in Figure 7(a), the first stage extracts surface wave scatter data and identifies safe zones' where no anomalies have been found (i.e. the areas outside the dense' and less-dense' zones indicated), and the second stage processes the direct arrival (p-wave) data for the dense' and less-dense' zones (Figure 7(b)). It will be noted that where the tomographic post 4 (or separate receiver apparatus, if implemented) includes multiple receivers 12, multiple direct arrival sequences may be recorded.

Figure 8 is a flow diagram illustrating exemplary steps performed in processing the data collected using the system of Figure 1. The steps in the flow diagram may be implemented as software1 firmware or hardware within the one or more computers 6, or any suitable combination thereof.

The exemplary processing operates on the seismic data 60 and the E-M data 62 obtained by the tomographic posts 4 (which can be stored in the memory module 20 of the computer 6, for example in the SEG-Y format). Prior to the processing, the seismic and E-M data can be stacked using conventional tools into an array corresponding to the region being monitored.

In step 64, surface wave data in the seismic data 60 is extracted and processed to identify locations of potential anomalies. Put another way, step 64 uses the results of the surface wave processing to identify safe zones' within the region of interest, leaving other zones where further analysis of the seismic data 60 is required. In particular, a surface wave will propagate through a uniform medium at a specific speed, which is based on the power used by the tomographic post 4 to generate the surface wave and the density of the medium through which it is moving. As a result, it is possible to determine an expected time of arrival at a receiver for a surface wave.

However, if a surface wave encounters a buried object, the speed of the surface wave through that object will change (due to a change in density of the medium through which the surface wave is moving) and therefore the surface wave will arrive at the receiver earlier or later than expected. Thus, the safe zones' can be identified as those areas where the surface wave arrives in the expected time window. This is illustrated in Figures 9 and 10.

Figure 9 shows unprocessed seismic data collected during a test by a series of geophones placed at 1 metre intervals from a tomographic post 4. The x-axis shows the time in milliseconds from the initiation of the seismic pulse. The seismic signal (trace) recorded by each geophone is illustrated in a vertical direction. The wave packet at the top of the graph, which is delineated by lines 90, represents the p-wave i.e. primary or compressional wave) that propagates through the ground. It can be seen that this wave is moving through the array of geophones at around 1400 mIs.

The surface scatter wave (Rayleigh wave) is also visible in this graph, delineated by lines 92, and it can be seen that this wave propagates through the ground much slower than the p-wave.

In step 64, the raw seismic data (as shown in Figure 9) is filtered in order to extract the surface wave data. In particular, the data is filtered to remove the data relating to the p-wave and to remove seismic modes (including the p-wave) which are moving through the array with a particular ve]ocity. The filtering can also remove interference and S noise from the seismic data. F-K filtering can be used to filter the data in this step.

The graph in Figure 10 shows the seismic data after this filtering step. In particular, the data relating to the p-wave has largely been removed, although some evidence (noise) of the p-wave remains (as indicated by "A"). Nevertheless, the signal representing the surface wave (again delineated by lines 92) has been enhanced in the data. As shown, the surface wave is passing through the array at around 133 mIs, which is known as the group velocity, Vg.

Unlike the p-wave, it is evident that the phase of the surface wave, indicated by line 94, is travelling at a different velocity. This phase' velocity, V, is about 250 mIs.

The difference between Vg and V is a fundamental characteristic of all surface waves, and can be used to discriminate between surface waves and other kinds of seismic energy in a seismogram. The phase velocity changes as a function of frequency.

It can be seen that the data delineated by lines 92 shows a range of frequencies between about 40 Hz and 65 Hz. This equates to a wavelength range of between about 4m and 6.25m. Generally speaking, there would be surface wave scattering only if the wavelength is less that about twice the dimension of the artefact disturbing the surface. Thus, scattered energy could be observed from objects bigger than about 2m.

with alternative filtering of the data, higher frequencies, and thus smaller artefacts can be seen in the data.

In Figure 10, it can be seen that the surface wave arrived at geophones 11 to 21 much quicker than expected (which is shown in the region labelled "B"), and thus this is an indication of an area that contains an anomaly (i.e. buried object). With triangulation using the data from multiple tomographic posts 4, it is possible to locate this area within the region 50 being monitored.

Returning to Figure 5, in step 66, the information on the p-waves in the seismic data is processed into a form that is suitable for subsequent velocity and attenuation analysis steps. In particular, step 66 can comprise filtering or reducing the noise in the seismic data 60 for the non-safe zones' identified in step 64.

An exemplary process for executing steps 64 and 66 is shown in Figure 11 which is described below. In step 102 of Figure 11, surface wave data for the region of interest is extracted from the seismic data 60. In particular, the seismic data 60 is processed such that the surface wave data is more evident in the raw (pre-stack) dataset for each source-receiver pair as described above with reference to Figures 9 and 10. This processing provides data as shown in Figure 12(a). A brute-stack method is then used to convert this data into a 3D volume. It is possible to determine the first wave in the sequence, and then measure, classify and map it into a 2D surface image.

In step 104, potential locations where surface anomalies may be found are identified using the surface image (i.e. by identifying those areas where a surface wave is received earlier or later than expected for the surrounding medium).

The regions that are deemed safe, i.e. that do not contain anomalies, are identified in step 106. The further processing performed by the algorithm now concentrates on the data corresponding to regions identified in the surface image where anomalies have been found.

In step 108, the result of step 106 is used to filter the direct arrivals' (p-waves) from the seismic data 60 (which also includes reflections, refractions, surface waves, etc.) for the identified locations that contain anomalies.

Steps 110-114 of the algorithm in Figure 11 generally correspond to step 66 in Figure 8. In step 110, a first noise reduction step is applied to the direct arrival' (p-wave) seismic data 60 obtained in step 108. This step removes all evidence of noise at the start of the processing routine.

Next, in step 112, the wavelet is convolved for each trace in the direct arrival seismic data 60, and the wavelet data is converted into acoustic impedance data. The methodologies employed in this step have been previously used in oil exploration and other seismic events usage and thus would be within the capability of the person skilled in the art.

instep 114, a further noise reduction step is applied to the acoustic impedance data in which areas where vertical changes are smaller than the system's predefined size characteristics for an anomaly and therefore appear to be random are removed.

It should be noted that the operation of the processing in step 114 can be configured to ensure that it does not remove valuable data from the output or more noise than is required which could otherwise be interpreted as an anomaly.

Returning to Figure 8, in step 67, the E-M data 62 is processed using noise reduction and filtering techniques to improve the quality of the data for subsequent processing steps.

After step 66, the acoustic impedance data is subject to velocity analysis (step 68) in which the noise-reduced wavelet data is processed to determine velocity (or density) signatures for each grid cell representing the region being monitored (with each grid cell corresponding to a physical volume in the region of interest).

In step 70! the noise-reduced EM-data is subject to conductivity analysis in which the EM-data is processed to determine conductivity signatures for each grid cell representing the region being monitored.

Furthermore, both the noise-reduced seismic data and EM-data are subject to attenuation analysis (step 72) in which amplitude attenuation and attenuation coefficients are processed to determine respective seismic and EM attenuation signatures for each grid cell representing the region being monitored. It will be appreciated that although a single attenuation analysis block is shown in Figure 8, respective analysis blocks could be provided for each of the seismic data and EM data.

The output of the velocity analysis step 68, the conductivity analysis step 70 and the attenuation analysis step 72 are combined and provided to subsequent processing stages that interpret the signatures as particular physical characteristics, structures or features of the region being monitored.

In a first processing stage (step 76), since different types of materials will have different effects on the velocity and attenuation of the p-wave and have different conductivities (as determined from the EM data), tomographic analysis is performed in which the velocity, conductivity and/or attenuation signatures are used to classify or determine the composition of each grid cell. The result of the tomographic analysis can be output in visual form on the display 24 of the computer 6 as a series of 2 dimensional cross-sections through the region being monitored (step 78).

In a second processing stage (step 80), which does not necessarily require step 76 to have been performed beforehand, the presence and location of any anomalies in the region being monitored is determined. In this step, the velocity, conductivity and/or attenuation signatures are used to identify wavelets that indicate the presence of an anomaly. In some embodiments, an upper bound, such as 1m2, can be placed on the size of an anomaly to be detected. In some embodiments, where the aim of the anomaly detection is to identify the presence of a buried object, such as an lED, the anomaly detection can attempt to identify areas having characteristics of a burial associated with a detected object.

For example, Figure 13 shows the typical burial of an object 96 in the ground (sediment) 97. In addition to detecting the object 96 itself, which may exhibit distinctly different velocity, conductivity and attenuation characteristics to the surrounding ground 961 processing stage 80 can also identify the evidence of the burial, such as the disturbed sediment in region 98. The disturbed region will typically be associated with a lower velocity and a lower density than the rest of the ground 96.

Figure 14 shows the results of the processing of the acoustic impedance values overlaid on the original seismic data for a region where there is a buried object. Thus, it can be seen that there is zone of low velocity/low density around the located object, and a velocity/density change across geological sequences remains apparent.

The results of the anomaly detection can be displayed in visual form on the display 24 of the computer 6. The results may be provided in the form of a visual overlay for the tomographic results output by the tomographic analysis stage 76 showing any grid cell in which an anomaly has been detected, or in any other suitable format.

Finally, in processing stage 84 (which is typically only used when it is necessary to determine whether any detected anomalies are threats, such as IEDs), the anomaly is analysed to determine its composition. The composition can be determined by comparing the velocity, conductivity and/or attenuation signatures to known values for various materials (which will be described in more detail below). Processing stage 84 may have an associated threat library stored in a database 86 that includes details of shapes, sizes and compositions of various threats, to which the results of the composition determination can be compared. Any anomalies that are determined to be threats can he displayed (step 88) by display 24 in computer 6 along with their location and classification.

In some embodiments, anomalies can be characterised as threats if their signature matches signatures associated with a known threat such as metal or plastic, or if the signature does not match the characteristics of any material stored in the threat library 86. In some embodiments, detected anomalies can be discounted as threats if they are smaller than 1 m but have the same signature as a larger area.

In an embodiment of the system 2, zones of lateral continuity in the seismic data can be discarded. In particular, any continuous zone larger than a predefined lateral size is discarded. Figure 15 is an example where the algorithm uses a 2m span, or 4 traces, to determine lateral continuity. In this figure, there is lateral continuity in the raw seismic data and there is no evidence of disturbance. Thus, it is then clear to the algorithm that no burial has taken place. The sequences are also consistent laterally, and it can be indicated that no buried object is at present discernable from the seismic data for that region.

Vertical anomalies in the data can be identified after convolving the wavelet (step 112) and applying various techniques to establish an accurate relative velocity/density. This can be implemented in step 80 of FigureS. Figure 16 shows two examples of vertical velocity results that could be obtained from the seismic data. In the normal geology example, there is relatively little change in velocity density (although in a conventional survey there would be sufficient change to clearly identify geological layers).

In the right hand example of Figure 16, there is an area associated with the surface that has a low velocity density, corresponding to disturbed soil, and a slightly deeper area of high velocity, corresponding to the buried object. However, in reality this is actually very broken due to disturbance from burial changes seen in the velocity density in various, often apparently random events, which are normal from the surface with a high velocity/density around the anomaly. The system 2 would not expect to correlate the high velocity/density across more than two traces. Normal geology then resumes below the anomaly.

In step 80 or alternatively in step 82 of Figure 8, the algorithm can identify a list of anomalies and order them according to various configurable factors such as size and relative acoustic impedance change.

Anomalies are normally difficult to distinguish from one another since they cover, in this example, only up to four traces and ideally up to two full wavelets, There will be further evidence if the area around the anomaly has been disturbed, but this is difficult to detect visually since the system 2 would find a high velocity/density pattern for the object and a low velocity density zone around it. However, it is possible for these to cancel each other out in conventional processing which then renders the entire feature (anomaly) invisible.

Figure 17 shows that there are two potential anomalies (A and B) identified in the data, which, to the human eye, would be difficult to differentiate -if they could be located at all. However, once the system 2 has processed the data, it becomes clear which is anomalous and which is not.

The system 2 is also configured to identify burial by determining zones around an anomaly which have certain properties and clear characteristics. For example, Figure 18 is a visualisation of un-processed seismic data, and it can be seen that there is evidence that the normal sequencing is broken up and it is possible to record that the discrepancy is not simply noise.

If the system 2 provides an output in a map view (for example as shown in Figure 19), the system 2 can detect further information, which is automatically extracted from the raw seismic data. For example, the algorithm can define the direction of the burial and its lateral scale, The results can be quantified using the following core information.

* IL In-line number * XL X-line number * Depth Depth in meters to the deepest burial point or the top of the anomaly * Object System identified an object (anomaly) Size Size of object (anomaly) in metres * Burial System identified evidence of burial o Dir Offset (from vertical) of burial in degrees * Hor Horizontal direction of burial o Risk The risk associated with the buried anomaly * Rel The uncertainty factor associated with the processing o Cor Correction and adjustment within the possessing The system 2 will provide accurate information regarding the size of the buried object when the resolution of the data is adjusted. In the exemplary results shown beow, the system 2 was looking for evidence of anomalies that are less than liii in diameter or width (which is often only one trace).

Table I

Results for Survey 1 Location Object Burial Analysis # IL XL Depth Found Size Found Dir Hor Risk Rel 1 25 31 0.83m Yes -Yes 10° 3° -- 2 142 37 1.44rn Yes -Yes 19° 67° -- 3 72 28 1.24m Yes -Yes 27° 138° -- 4 121 70 0.88m Yes -Yes 13° 21° -- 60 3 0.9gm Yes -Yes 16° 140° -- 6 95 46 0.44m Yes -No ---- 7 70 74 0.31m No -Yes 2° 48° -- 8 11 60 0,73m Yes -Yes 12° 101° -- 9 32 45 0.8gm Processing or acquisition error o:m o j: iX 116' 1fb° Survey 2 -Vertical

Table 2

Results for Survey 2 Location Object Burial Analysis X Y Depth Found Size Burial Dir Hor Risk Rel 1 46 50 1.12m Yes -Yes 22° 151° -- 2 10 22 0.77m Yes -Yes 300 22° -- 3 35 31 0.Slm Yes -Yes 15° 370 -- 4 66 39 0.78m Yes -Yes 7° 140° -- 62 4 1.28m Yes -Yes 9° 1130 -- 6 91 37 lOin Yes -No ---- 7 104 47 1.41m Yes -No ---- 8 119 22 1.29m No -Yes 11° 18° -- 9 131 40 0.89m Yes -Yes 2° 132° -- 141 33 0.62m No -Yes 23° 74° --In some embodiments of the system 2 according to the invention, particularly where the system 2 is to be used to monitor a specific region of interest over time to detect changes, such as the burial of IEDs, the system 2 can be used to conduct multiple surveys of the region of interest, and the system 2 can learn and store the anomalies that are typically found in that region, but that are not considered threats.

For example, the system 2 can be used to monitor a region near to a base of operations, and each day, or several times a day, the system 2 can be activated in order to detect if any lEDs have been buried since the last survey was conducted. In addition to detecting any buried IEDs, the system 2 will identify objects that are permanent features of the region of interest like tree stumps, building foundations, etc. as anomalies' within the surface scatter wave data. Thus, the system 2 can be configured to store the location and composition of the permanent features of interest in the memory module 20, and this information can be used during the processing of subsequent survey data in order to reduce the number of anomalies that need to be analysed (i.e. only newly detected anomalies need to be analysed), and this can speed up the processing of the data and identification of any potential threats.

It will be appreciated that the system 2 according to the invention is particularly advantageous in that it is easy to deploy in the field (i.e. the tomographic posts 4 can be carried on a vehicle and put into the required positions quickly), and seismic survey data can be collected and analysed in near-real-time, enabling any threats in a region of interest bounded by the tomographic posts 4 to be quickly identified.

It will also be appreciated that other lED detection technologies can be used in combination with the present invention in order to further improve the reliability of the threat detection. For example, after identification of a potential threat by the system 2 as described above, another technology, such as ground penetrating radar, can be used to interrogate the area in which the anomaly is located in order to further identify and classify the composition of the threat.

It will also be appreciated from the above description that the invention is not limited to use in areas with homogenous lithology or geology.

Velocity signature analysis The following formula shows how velocity is made up of factors such as density of the material. =

N (1) where: K is the modulus of incompressibility; a p is the modulus of rigidity; and p is the density of the material through which the wave is propagating.

The graph in Figure 20 shows how the velocity of a wave through metal and plastic is very different from sediment or a liquid such as oil or water, even where there is a relatively large margin of error. Therefore, it can be seen that it is relatively easy to use velocity signatures to identify anomalies made of plastic and/or metal from surrounding sediment or a liquid.

The following table, Table 3, shows a number of different plastics along with their unique velocity. Given the accuracy of the velocity signatures that is obtainable by the velocity analysis described above, it is possible to determine the type of material that is present in a particular anomaly.

Table 3

Velocity, Density, Material 1.4! ms kgm ABS 2230 1.03 2.31 Acrylic Plexiglas 2750 1.19 3.26 Adiprene 1680 1.16 1,94 Bakelite 1590 1.40 3.63 Cellulose Butyrate 2140 1.19 2.56 Deirin 2430 1.42 3.45 Ethyl vinyl acetate 1800 0.94 1.69 Neoprene 1600 1.31 2.1 Mylar 2540 1.18 3.00 Nylon 2600 1.12 2.9 Polycarbonate 2270 1.22 2.77 Polyethylene 1950 0,90 1.76 Polyethylene high density 2430 0.96 2.33 Polyethylene low density 1950 0.92 1.79 PoLypropylene 2740 0.88 2.40 Polystyrene 2320 1.04 2.42 Polyurethane 1700 1.04 1.8 PVC 2380 1.38 3.27 Vinyl rigid 2230 1.33 2.96 Conductivity signature analysis Conductivity is the reciprocal of resistivity, which can be described as follows:

E

/7 (2) S where: * p is the static resistivity (measured in volt-metres per ampere, V rn/A); E is the magnitude of the electric field (measured in volts per metre, V/rn); and * J is the magnitude of the current density (measured in amperes per square metre, A/rn2).

The conductivity of almost all types of soil, even when saturated, is clearly distinguishable from the conductivity of plastic (which is near zero) and the conductivity of metal (which is very high). This can be seen in the graph in Figure 21.

Although it may not be possible to distinguish between different types of plastic in an anomaly using conductivity measurements, it is possible to differentiate between plastics in general and other types of materials. The following table, Table 4, shows the conductivity of various materials:

Table 4

Material Electrical Conductivity (Sm) Silver 63.0 x 106 Copper 59.6 x Annealed Copper 58.0 x 106 Gold 45.2 x Aluminium 37.8 x 106 Seawater 4.8 Drinking water 0.0005 to 0.05 Deionized water 5.5 x 106 Jet A-i Kerosene 50 to 450 x 1 12 n-hexane 100 x 10 Air 0.3 to 0.8 x 1014 Attenuation signature analysis Attenuation of the p-wave as it propagates through the ground has both beneficial and detrimental effects. On the one hand, since attenuation is the gradual loss of amplitude, and therefore resolution1 it determines the maximum detection range of each tomographic post 4. On the other hand, since different sub-surface strata and anomalies have different attenuation coefficients, attenuation signatures can provide further information about the composition or structure of a grid cell.

Equation (3) below for attenuation shows how attenuation coefficients are used to quantify different media according to how strongly the transmitted source amplitude decreases as a function of frequency. The attenuation coefficient (a) can be used to determine total attenuation in dB in the medium using the following formula: 1Attønuation [ciB] a " "dR" /(ç.IHz X r j) -/ [:1 - [MYt] (3) As this equation shows, besides the medium length and attenuation coefficient, attenuation is also linearly dependent on the frequency of the input waveform (source).

There are two general ways in which acoustic energy is attenuated, the first is through absorption, and the second is through scattering. The seismic waves (velocity-based approach) are more prone to scattering, whilst the EM-waves (conductivity-based approach) are more prone to absorption.

Although the processing technique shown in Figure 8 has been described in terms of identifying buried objects from data obtained using the tomographic post of Figure 2, it will be appreciated that the processing technique can be used to process seismic and/or EM data obtained using conventional tomographic posts or other types of source, and the use of the technique will provide models or interpretations of the near-subsurface features or structure that has a greater resolution and accuracy than that obtainable by conventional processing techniques.

While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfil the functions of several items recited in the claims. Alternatively, multiple processors or processing units can fulfil the functions of the items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

Claims

<claim-text>Claims 1. A method of processing seismic survey data to identify objects buried close to the surface in a region of interest, the method comprising: extracting surface wave data from the seismic survey data; processing the extracted surface wave data to identify areas in the region of interest in which buried objects are not located; extracting p-wave data from the seismic survey data for the areas in which buried objects are located; and processing the extracted p-wave data for the areas in which buried objects are located to identify the buried object.</claim-text> <claim-text>2. A method as claimed in claim 1, wherein the step of extracting the surface wave data comprises filtering the seismic survey data to remove p-wave data and/or noise and interference.</claim-text> <claim-text>3. A method as claimed in claim 1 or 2, wherein the step of processing the extracted surface wave data to identify areas in the region of interest in which buried objects are not located comprises comparing the velocity of surface waves within the extracted surface wave data to a predetermined range of velocities for the surface waves, and identifying areas in which buried objects are not located as those areas where the associated surface waves have a velocity within the predetermined range of velocities.</claim-text> <claim-text>4, A method as claimed in claim 1, 2 or 3, wherein the step of processing the extracted surface wave data to identify areas in the region of interest in which buried objects are located comprises identifying surface waves within the extracted surface wave data having velocities not falling within a predetermined range.</claim-text> <claim-text>5. A method as claimed in claim 1 2, 3 or 4, wherein the step of processing the extracted surface wave data comprises: using a brute-stack method to convert the surface wave data into a 3D volume; determining the first surface wave in each sequence; and mapping the first surface waves into a 2D surface image.</claim-text> <claim-text>6. A method as claimed in any of claims 1 to 5, wherein the step of processing the p-wave seismic data comprises: reducing noise in the extracted p-wave seismic data.</claim-text> <claim-text>7. A method as claimed in any preceding claim, wherein the step of processing the p-wave seismic data comprises: convolving a wavelet with each trace in the extracted p-wave seismic data to give wavelet data; and converting the wavelet data into acoustic impedance data.</claim-text> <claim-text>8. A method as claimed in claim 7, wherein the step of processing the p-wave seismic data further comprises: reducing noise in the acoustic impedance data by discarding data where changes in the vertical direction are smaller than a predefined threshold.</claim-text> <claim-text>9. A method as claimed in any preceding claim, further comprising the steps of: identifying lateral continuities in the seismic survey data larger than a predetermined size; and discarding the seismic data that corresponds to the identified lateral continuities, such that the discarded data is not processed in subsequent processing steps.</claim-text> <claim-text>10. A method as claimed in any preceding claim, wherein the step of processing the extracted p-wave seismic data for the areas in which buried objects are located comprises: processing the extracted p-wave seismic data to determine velocity signatures for each grid cell in a plurality of grid cells representing the areas.</claim-text> <claim-text>11. A method as claimed in claim 10, wherein the step of processing the extracted p-wave seismic data for the areas in which buried objects are located further comprises: examining the velocity signatures to identify characteristics associated with a buried object.</claim-text> <claim-text>12. A method as claimed in any preceding claim, wherein the step of processing the extracted p-wave seismic data for the areas in which buried objects are located comprises: processing the extracted p-wave seismic data to determine attenuation signatures for each grid cell in a plurality of grid cells representing the areas.</claim-text> <claim-text>13. A method as claimed in any preceding claim, the method further comprising the steps of: processing electro-magnetic survey data for the areas in which buried objects are located to determine conductivity and/or attenuation signatures for each grid cell in a plurality of grid cells representing the areas; and using the conductivity and/or attenuation signatures determined from the electro-magnetic survey data with the results of the processing of the extracted p-wave seismic data to identify the composition of the buried object.</claim-text> <claim-text>14. A method as claimed in any of claims 10, 11, 12 or 13, further comprising the step of comparing the determined signatures to a plurality of signatures corresponding to respective types of materials.</claim-text> <claim-text>16. A method as claimed in any preceding claim, further comprising the steps of: storing information on buried objects identified in the region of interest; and using the stored information on buried objects identified in the region of interest in the processing of subsequently obtained seismic survey data for the region of interest.</claim-text> <claim-text>16. A processing unit for processing seismic survey data to identify objects buried close to the surface in a region of interest, the processing unit comprising processing means configured to perform the method as claimed in any of claims 1 to 15.</claim-text> <claim-text>17. A tomographic post for use in seismic surveys, comprising: an outer housing; an inner housing located inside the outer housing, the inner and outer housings being arranged to form a chamber therebetween; and a seismic wave source located within the inner housing for generating seismic waves; wherein the chamber is configured to be pressurised to a predetermined pressure with an inert gas prior to generation of seismic waves.</claim-text> <claim-text>18. A seismic survey system, comprising: a processing unit as claimed in claim 16 for processing the seismic survey data obtained using the one or more tomographic posts.</claim-text> <claim-text>19. A seismic survey system as claimed in claim 18, further comprising one or more tomographic posts as claimed in claim 17.</claim-text> <claim-text>20. A computer program product comprising computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is configured to perform the method as claimed in any of claims ito 15.</claim-text>