Earthquake prediction method, device and system based on earthquake repetition signals

Abstract

The invention provides an earthquake prediction method, device and system based on earthquake repetition signals, relates to the technical field of earthquake exploration, and receives signals from at least two receiving sensors, the received signal from the receiving sensor is an electric signal corresponding to a second sound wave signal obtained by reflecting a first sound wave signal emitted to a target geological fault through an excitation source, calculating the wave velocity of the tail wave of the signal to obtain a change result of the wave velocity of the tail wave along with time lapse, and calculating the wave velocity of the tail wave according to the change result of the wave velocity of the tail wave along with time lapse. And determining a time range corresponding to the seismic nucleation process in the target geological fault. Due to the fact that excitation and receiving of the sound wave signals are easy to achieve under laboratory conditions or field conditions, excitation and receiving of the sound wave signals are not affected by the environment, accuracy is high, the wave velocity of the tail wave determined through the sound wave signals can accurately represent the earthquake nucleation process, and the excitation and receiving are easy to achieve under the field conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and more particularly, to a seismic prediction method, apparatus, and system based on seismic repeat signals. Background Art

[0002] Seismic prediction has always been a worldwide problem, and finding stable seismic precursor signals is the key to successful seismic prediction. It is generally believed that the appearance of seismic precursors is related to the earthquake nucleation process. For the earthquake nucleation process, related technologies usually determine the earthquake nucleation process in the following ways:

[0003] (1) Strain gauges are set in geological faults to detect the shear stress of the faults, and the earthquake nucleation process is determined by the change trend of the shear stress of the faults over time. This method is limited in field precursor research and it is difficult to obtain in-situ formation stress in the deep part.

[0004] (2) The earthquake nucleation process is determined by obtaining pre-seismic activities. Since some main earthquakes do not have pre-seismic activities, this method has certain limitations.

[0005] (3) The earthquake nucleation process is determined by obtaining the seismic b-value. For obtaining the seismic b-value, a large number of foreshock events are required, and it is difficult to obtain.

[0006] (4) The earthquake nucleation process is determined by collecting the fault temperature. Since it is difficult to obtain field temperature data, and generally only temperature data of shallow (hundred-meter level) formations can be obtained, temperature data of deep kilometer-level formations cannot be obtained (the focal depth of the main earthquake is generally 10 - 15 km).

[0007] In summary, there is an urgent need for a method to accurately characterize the earthquake nucleation process and be easy to implement in the field. Summary of the Invention

[0008] The problem solved by the present invention is how to accurately characterize the earthquake nucleation process and be easy to implement in the field.

[0009] To solve the above problems, the present invention provides a seismic prediction method, apparatus, and system based on seismic repeat signals.

[0010] In a first aspect, the present invention provides a seismic prediction method based on seismic repeat signals, including:

[0011] Receiving signals from at least two receiving sensors, where the signals are electrical signals corresponding to second acoustic signals, and the second acoustic signals are signals reflected by a target geological fault of a first acoustic signal that is periodically changed and emitted by an excitation source to the target geological fault; wherein, the first acoustic signal is used to simulate a seismic wave signal;

[0012] Perform coda wave velocity calculation on the signals from at least two receiving sensors to obtain the variation result of the coda wave velocity corresponding to the second acoustic wave signal over time;

[0013] Determine the time range corresponding to the earthquake nucleation process in the target geological fault according to the variation result of the coda wave velocity corresponding to the second acoustic wave signal over time.

[0014] Optionally, the performing coda wave velocity calculation on the signals from at least two receiving sensors to obtain the variation result of the coda wave velocity corresponding to the second acoustic wave signal over time includes:

[0015] Move a sliding window with a preset time length in the signals from at least two receiving sensors, perform cross-correlation calculation on the signals from at least two receiving sensors in the sliding window to obtain the cross-correlation signal corresponding to each sliding window; wherein, the preset time length is at least less than the period of the first acoustic wave signal;

[0016] Extract the coda wave band signals from the cross-correlation signals corresponding to each sliding window, and perform interference calculation on the coda wave band signals to obtain the coda wave velocity of the cross-correlation signals corresponding to each sliding window;

[0017] Obtain the variation result of the coda wave velocity corresponding to the second acoustic wave signal over time according to the coda wave velocity of the cross-correlation signals corresponding to each sliding window.

[0018] Optionally, the performing coda wave velocity calculation on the signals from at least two receiving sensors to obtain the variation result of the coda wave velocity corresponding to the second acoustic wave signal over time further includes:

[0019] Identify the first motion signals in the signals from at least two receiving sensors respectively, and the first motion signal is the signal with the first amplitude mutation in the signal;

[0020] The moving a sliding window with a preset time length in the signals from at least two receiving sensors includes:

[0021] For the signal from each receiving sensor, starting from the first motion signal in the signal, move a sliding window with a preset time length in the signal.

[0022] Optionally, before moving a sliding window with a preset time length in the signals from at least two receiving sensors, it further includes:

[0023] Perform high-pass filtering processing on the signals from at least two receiving sensors respectively.

[0024] Optionally, before extracting the tail wave band signals in the cross-correlation signals corresponding to each sliding window, the method further includes:

[0025] Determining the spectral information of the cross-correlation signals corresponding to each sliding window;

[0026] Extracting the cross-correlation signals corresponding to a predetermined frequency band from the cross-correlation signals corresponding to each sliding window according to the spectral information of the cross-correlation signals corresponding to each sliding window;

[0027] Performing reference zero alignment processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window;

[0028] Performing normalization processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window after the reference zero processing.

[0029] Optionally, the excitation source is a piezoelectric crystal sensor; and / or, the receiving sensor is a piezoelectric ceramic sensor.

[0030] Optionally, the method further includes:

[0031] Generating and outputting a square wave signal to the excitation source through a waveform generator, so that the excitation source generates the first acoustic wave signal based on the square wave signal.

[0032] In a second aspect, the present invention provides a seismic prediction device based on seismic repeat signals, including:

[0033] A receiving module, configured to receive signals from at least two receiving sensors, where the signals are electrical signals corresponding to a second acoustic wave signal, and the second acoustic wave signal is a signal reflected by a target geological fault after a first acoustic wave signal that is periodically changed and emitted by an excitation source to the target geological fault; wherein, the first acoustic wave signal is used to simulate a seismic wave signal;

[0034] A tail wave velocity calculation module, configured to calculate the tail wave velocity of the signals from at least two receiving sensors, and obtain the change result of the tail wave velocity corresponding to the second acoustic wave signal over time;

[0035] A seismic nucleation process determination module, configured to determine the time range corresponding to the seismic nucleation process in the target geological fault according to the change result of the tail wave velocity corresponding to the second acoustic wave signal over time.

[0036] In a third aspect, the present invention provides a seismic prediction system, which is characterized by including an excitation source, at least two receiving sensors, and a processor;

[0037] The excitation source is configured to generate and emit a first acoustic wave signal that is periodically changed to a target geological fault, and the first acoustic wave signal is used to simulate a seismic wave signal;

[0038] Each of the receiving sensors is configured to receive a second acoustic wave signal reflected by the target geological fault from the first acoustic wave signal and output a corresponding electrical signal.

[0039] The processor is configured to execute the seismic prediction method based on seismic repeat signals as described in the first aspect.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, implements the seismic prediction method based on seismic repeat signals as described in the first aspect.

[0041] The beneficial effects of the seismic prediction method, apparatus, and system based on seismic repeat signals of the present invention are as follows: Signals from at least two receiving sensors are received, where the signals received from the receiving sensors are electrical signals corresponding to the second acoustic wave signals obtained after reflection of the first acoustic wave signals emitted by an excitation source towards the target geological fault. The first acoustic wave signals emitted by the excitation source are used to simulate seismic wave signals to obtain the acoustic wave signals after the simulated seismic wave signals pass through the target geological fault. The coda wave velocity of the signals from at least two receiving sensors is calculated to obtain the change result of the coda wave velocity corresponding to the second acoustic wave signal over time, so as to obtain the coda wave velocity corresponding to the acoustic wave signal after the simulated seismic wave signal passes through the target geological fault. According to the change result of the coda wave velocity corresponding to the second acoustic wave signal over time, the time range corresponding to the earthquake nucleation process in the target geological fault is determined to predict earthquake precursors. Due to the excitation and reception of acoustic wave signals, it is easy to implement both under laboratory conditions and field conditions, and the excitation and reception of acoustic wave signals are not affected by the environment and have high accuracy, so that the coda wave velocity determined by the acoustic wave signals can accurately characterize the earthquake nucleation process and is also easy to implement under field conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic structural diagram of a seismic prediction system according to an embodiment of the present invention;

[0043] Figure 2 is a schematic flowchart of a seismic prediction method based on seismic repeat signals according to an embodiment of the present invention;

[0044] Figure 3 is a schematic flowchart of calculating the coda wave velocity of a signal according to an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram of a cross-correlation signal according to an embodiment of the present invention;

[0046] Figure 5 is a schematic flowchart of moving a sliding window in a signal according to an embodiment of the present invention;

[0047] Figure 6 Schematic flowchart of extracting a predetermined frequency band from a cross-correlation signal according to an embodiment of the present invention;

[0048] Figure 7 Schematic structural diagram of a seismic prediction device based on seismic repeat signals according to an embodiment of the present invention;

[0049] Figure 8 Schematic diagram of a seismic nucleation process outlined according to peak shear stress;

[0050] Figure 9 Schematic diagram of stress, wave velocity, and fault thickness curves within a stick-slip cycle recorded by one sensor pair;

[0051] Figure 10 Schematic diagram of stress, wave velocity, and fault thickness curves within a stick-slip cycle recorded by another sensor pair. Detailed implementation manners

[0052] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0053] It should be understood that the various steps recorded in the method implementation manners of the present invention can be executed in different orders and / or in parallel. In addition, the method implementation manners may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0054] The term "including" and its variants used herein are open-ended, that is, "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules, or units, and are not used to limit the order of functions performed by these devices, modules, or units or their interdependent relationships.

[0055] It should be noted that the modifiers "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0056] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0057] The following terms related to the present invention are explained:

[0058] Seismic nucleation refers to the process in which a geological fault evolves from local quasi-static rupture to dynamic rupture, including temporal and spatial processes.

[0059] The coda wave of an acoustic wave signal (seismic wave signal) refers to the waveform signal that appears at the tail and shows attenuation in amplitude. The coda wave velocity refers to the wave velocity corresponding to the coda wave band signal.

[0060] As Figure 1 shown, a seismic prediction system provided by an embodiment of the present invention may include: an excitation source 110, at least two receiving sensors 120, and a processor 130, where:

[0061] The excitation source 110 is configured to generate and transmit a first acoustic wave signal that varies periodically to a target geological fault, and the first acoustic wave signal is used to simulate a seismic wave signal. Generally, the envelope of a seismic wave signal evolves exponentially. Therefore, in this embodiment, the envelope of the first acoustic wave signal used to simulate the seismic wave signal also needs to evolve exponentially.

[0062] Specifically, the target geological fault may be a geological fault structure built under laboratory conditions or an actual geological fault under field conditions. This embodiment does not make any limitations in this regard.

[0063] Specifically, whether under laboratory conditions or field conditions, the excitation source 110 is disposed on one side of the target geological fault in its vertical direction, such as the upper side. It can be understood that the closer the excitation source 110 is to the target geological fault, the less the first acoustic wave signal it emits is affected by non-geological faults.

[0064] Optionally, the seismic prediction system may further include: a waveform generator 140. The waveform generator 140 generates and outputs a square wave signal to the excitation source 110. After receiving the square wave signal, the excitation source 110 performs some signal processing to generate the first acoustic wave signal. Specifically, the waveform generator 140 may be a commercial function generator, and the square wave signal it outputs has a short pulse width, such as a pulse width of 10 us.

[0065] Optionally, the excitation source 110 may be a piezoelectric crystal sensor.

[0066] In this alternative embodiment, the piezoelectric crystal sensor uses natural or artificial crystals, such as quartz, which has excellent stability and high precision, good temperature stability, and is suitable for applications that require high precision and long-term stability. Therefore, it can be used stably for a long time and generate high-precision acoustic wave signals.

[0067] The receiving sensor 120 is configured to receive the second acoustic wave signal reflected by the target geological fault of the first acoustic wave signal and output a corresponding electrical signal.

[0068] Specifically, in laboratory conditions, the receiving sensor 120 is usually arranged on the lower side of the target geological fault in its vertical direction, but it can also be arranged on the upper side of the target geological fault; in field conditions, due to environmental constraints, it is impossible to arrange the receiving sensor 120 on the lower side of the target geological fault. Therefore, in field conditions, the receiving sensor 120 is usually also arranged on the upper side of the target geological fault. It should be noted that when the receiving sensor 120 is also arranged on the upper side of the target geological fault, it needs to be spaced a certain distance from the excitation source 110, and there also needs to be a certain distance between multiple receiving sensors 120. For example, multiple receiving sensors 120 can be spaced 10 - 50 meters apart. After each receiving sensor 120 receives the reflected second acoustic wave signal, it performs some signal processing to output a corresponding electrical signal.

[0069] Optionally, the receiving sensor 120 can be a piezoelectric ceramic sensor.

[0070] In this alternative embodiment, the piezoelectric ceramic sensor uses artificial piezoelectric ceramics, such as barium titanate or aluminum zirconate titanate, which has a relatively large piezoelectric coefficient and can generate a higher output electrical signal. The electrical signal can be a voltage signal.

[0071] In this alternative embodiment, the excitation source 110 can be controlled by the processor 130 to emit the first acoustic wave signal, or can be manually or remotely controlled by the user to emit the first acoustic wave signal.

[0072] The processor 130 can be implemented as software, hardware, firmware, or any combination thereof, and can use one or more application-specific integrated circuits (ASICs), one or more general integrated circuits, one or more microprocessors, one or more programmable logic devices, or any combination of the foregoing circuits and / or devices, or other suitable circuits or devices. And the processor 130 can control the earthquake prediction system to execute the corresponding steps of the methods in the various embodiments of this specification.

[0073] In addition, the earthquake prediction system may further include a memory for storing instructions executed by the processor, storing received signals, etc. The memory may be a flash card, solid state memory, hard disk, etc. It may be volatile memory and / or non-volatile memory, removable memory and / or non-removable memory, etc.

[0074] It can be understood that Figure 1 The components included in the illustrated earthquake prediction system are only illustrative, and it may include more or fewer components, which is not limited in the present invention.

[0075] Such as Figure 2 As shown, a method for earthquake prediction based on earthquake repeating signals provided by an embodiment of the present invention includes:

[0076] S210: Receive signals from at least two receiving sensors 120.

[0077] Specifically, the signals from the receiving sensor 120 are electrical signals corresponding to the second acoustic wave signals, and the second acoustic wave signals are signals reflected by the target geological fault of the first acoustic wave signal that is periodically changed and emitted to the target geological fault by the excitation source 110; wherein, the first acoustic wave signal is used to simulate the seismic wave signal.

[0078] Specifically, the excitation source 110 is arranged on one side of the target geological fault (for example, the upper side in the vertical direction), and emits the first acoustic wave signal for simulating the seismic wave signal to the target geological fault. The first acoustic wave signal has the same envelope change trend as the seismic wave signal, and the first acoustic wave signal is received by the receiving sensor 120 after being reflected in the target geological fault.

[0079] Specifically, at least two receiving sensors 120 are all arranged on one side of the target geological fault, and there is a certain distance between each two. Since the position of each receiving sensor 120 in the target position fault is different, the second acoustic wave signals received by each receiving sensor 120 are slightly different. Each receiving sensor 120 outputs the corresponding signal after processing some signals of the second acoustic wave signal it receives, and each receiving sensor 120 corresponds to one output signal.

[0080] Specifically, the first acoustic wave signal emitted by the excitation source 110 is a repeating acoustic wave signal with periodic changes, and the second acoustic wave signal received by the receiving sensor 120 is also a repeating acoustic wave signal with periodic changes.

[0081] S220: Calculate the tail wave velocity of the signals from at least two receiving sensors 120 to obtain the change result of the tail wave velocity corresponding to the second acoustic wave signal over time.

[0082] Specifically, the variation result of the tail wave velocity over time can be a curve showing the variation of the tail wave velocity over time.

[0083] S230: Determine the time range corresponding to the earthquake nucleation process in the target geological fault based on the variation result of the tail wave velocity of the second acoustic wave signal over time, so as to predict earthquake precursors.

[0084] Specifically, during the earthquake nucleation process, the tail wave velocity shows a downward trend over time. After obtaining the variation result of the tail wave velocity over time, the peak position of the tail wave velocity can be recorded, and the time range starting from the peak position is the time range corresponding to the earthquake nucleation process.

[0085] In this embodiment, a first acoustic wave signal of a simulated seismic wave is emitted to the target geological fault by the excitation source 110. The first acoustic wave signal forms a second acoustic wave signal after being reflected by the geological fault. At least two receiving sensors 120 receive and process the second acoustic wave signal to output corresponding signals, and the tail wave velocity of the signals from at least two receiving sensors 120 is processed to obtain the tail wave velocity corresponding to the second acoustic wave signal. Since the second acoustic wave signal is the acoustic wave signal after the first acoustic wave signal is reflected by the target geological fault, therefore, through the variation result of the tail wave velocity corresponding to the second acoustic wave signal over time, the earthquake nucleation process can be determined, and then the prediction of earthquake precursor signals can be realized through the tail wave velocity of the acoustic wave signal.

[0086] Optionally, as Figure 3 shown, calculating the tail wave velocity of the signals from at least two receiving sensors 120 to obtain the variation result of the tail wave velocity corresponding to the second acoustic wave signal over time includes:

[0087] S310: Move a sliding window with a preset time length in the signals from at least two receiving sensors 120, and perform cross-correlation calculation on the signals from at least two receiving sensors 120 in the sliding window to obtain a cross-correlation signal corresponding to each sliding window; wherein, the preset time length is at least less than the period of the first acoustic wave signal.

[0088] Specifically, if the period of the first acoustic wave signal emitted by the excitation source 110 is 200 s, the preset time length of the sliding window can be a time length less than 200 s, for example, the sliding window is 850 us.

[0089] Specifically, taking two receiving sensors 120 as an example, the signals from the two receiving sensors 120 in the sliding window are converted into discrete signals, and the following expression is used to perform cross-correlation calculation on the two discrete signals to obtain a cross-correlation signal:

[0090] R xy[k] = ∑x[n]·y[k + n + N - 1];

[0091] Where n is the period number of the second acoustic wave signal corresponding to the discrete signal, N is the number of periods included in the second acoustic wave signal corresponding to the discrete signal, x[] is one of the two discrete signals, y[] is the other of the two discrete signals, k is the offset between the second acoustic wave signals corresponding to the two discrete signals, and R xy [k] is the cross-correlation signal.

[0092] Specifically, taking two receiving sensors 120 as an example, the received signals are two paths of signals. The length of the cross-correlation signal obtained after cross-correlation calculation of the two paths of signals is twice that of one path of signal. The cross-correlation signal includes a causal part and an anti-causal part. Only one of them can be retained. In this embodiment, the retained part is the causal part, as Figure 4 shown.

[0093] S320: Extract the tail band signal in the cross-correlation signal corresponding to each sliding window, and perform interference calculation on the tail band signal to obtain the tail wave velocity of the cross-correlation signal corresponding to each sliding window.

[0094] Specifically, the tail band signal in the cross-correlation signal corresponding to the sliding window should be at least part or all of the signal greater than the predetermined time in the sliding window. Taking a sliding window with a time length of 850 us as an example, the tail band signal in the cross-correlation signal should be at least part or all of the signal greater than 300 us in the sliding window. In addition, the tail band signal should also include at least 5 wave peaks. Therefore, reference can be made to Figure 4 , and a section of the signal corresponding to the A time range of the causal part of the cross-correlation signal is taken as the tail band signal.

[0095] It should be noted that different lengths of the tail band signal extracted from the cross-correlation signal may result in different calculated tail wave velocities, but the variation result of the finally obtained tail wave velocity over time is the same. Therefore, in this embodiment, the length of the tail band signal extracted from the cross-correlation signal is not specifically limited.

[0096] Specifically, by cyclically performing interference calculation on the tail band signal of the cross-correlation signal of each sliding window, the tail wave velocity of the cross-correlation signal corresponding to each sliding window can be obtained.

[0097] S330: Obtain the variation result of the tail wave velocity corresponding to the second acoustic wave signal over time according to the tail wave velocity of the cross-correlation signal corresponding to each sliding window.

[0098] Specifically, according to the movement of the sliding window in the signal and the tail wave velocity of the cross-correlation signal corresponding to each sliding window, the change result of the calculated tail wave velocity over time can be obtained, and the change result of the tail wave velocity over time can be a change curve of the tail wave velocity over time.

[0099] In this alternative embodiment, compared with other waveforms in the seismic wave signal, the tail wave has the characteristics of carrying more medium information and being able to reflect medium anomalies in a larger area. Therefore, in this embodiment, cross-correlation calculations are performed on the signals received by at least two receiving sensors to obtain cross-correlation signals, and then the tail wave band signals are extracted from the cross-correlation signals, and interference calculations are performed on the tail wave band signals to obtain the change result of the tail wave velocity of the second acoustic wave signal over time, so as to determine an accurate earthquake nucleation process.

[0100] Optionally, as Figure 5 shown, moving a sliding window with a preset time length in the signals from at least two receiving sensors 120 in S310 includes:

[0101] S510: Identify the first motion signals in the signals from at least two receiving sensors 120 respectively.

[0102] Specifically, the first motion signal is the signal with the first amplitude mutation in the signal, that is, the P-wave first motion in the seismic wave signal.

[0103] S520: For the signals from each receiving sensor, starting from the first motion signal in the signal, move a sliding window with a preset time length in the signal.

[0104] In this alternative embodiment, by identifying the first motion signal and starting to move the sliding window from the first motion signal, cross-correlation calculations on some irrelevant signals in the early stage can be avoided.

[0105] Optionally, before moving a sliding window with a preset time length in the signals from at least two receiving sensors, it further includes: performing high-pass filtering processing on the signals from at least two receiving sensors respectively.

[0106] Specifically, the frequency of the high-pass filtering can be 20 kHz.

[0107] In this alternative embodiment, by performing high-pass filtering processing on the signals from at least two receiving sensors respectively, some low-frequency oscillation signals output by the excitation source 110 under laboratory conditions can be removed.

[0108] Optionally, as Figure 6 shown, before extracting the tail wave band signals in the cross-correlation signals corresponding to each sliding window, it further includes:

[0109] S610: Determine the spectral information of the cross-correlation signals corresponding to each sliding window.

[0110] S620: Extract the cross-correlation signals corresponding to a predetermined frequency band from the cross-correlation signals corresponding to each sliding window according to the spectral information of the cross-correlation signals corresponding to each sliding window.

[0111] Specifically, the extracted predetermined frequency band is the dominant frequency band of the cross-correlation signal and is related to the dominant frequency band of the receiving sensor 120.

[0112] S630: Perform reference zero alignment processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window.

[0113] As described above, the cross-correlation signal includes a causal part and an anti-causal part. The reference zero alignment is to align the junction point of the causal part and the anti-causal part with the reference zero point, which can be referred to Figure 4 .

[0114] S640: Perform normalization processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window after the reference zero point processing.

[0115] Specifically, maximum normalization processing can be performed on the cross-correlation signals, that is, making the cross-correlation signals distributed between 0 - 1 or -1 to 1 to avoid errors caused by some weak signals or sensor differences.

[0116] In this optional embodiment, by performing dominant frequency band extraction, reference zero alignment, and normalization processing on the cross-correlation signals, the accuracy of the processed cross-correlation signals is higher, so as to improve the accuracy of calculating the tail wave velocity in the subsequent process.

[0117] As Figure 7 shown, a seismic prediction device 700 provided by an embodiment of the present invention based on seismic repeat signals includes:

[0118] A receiving module 710, configured to receive signals from at least two receiving sensors, where the signals are electrical signals corresponding to the second acoustic wave signals, and the second acoustic wave signals are signals reflected by a target geological fault of a first acoustic wave signal that is periodically changed and emitted by an excitation source to the target geological fault; wherein, the first acoustic wave signal is used to simulate a seismic wave signal.

[0119] A tail wave velocity calculation module 720, configured to calculate the tail wave velocity of signals from at least two receiving sensors to obtain the change result of the tail wave velocity corresponding to the second acoustic wave signal over time,

[0120] The earthquake nucleation process determination module 730 is configured to determine the time range corresponding to the earthquake nucleation process in the target geological fault according to the change result of the coda wave velocity corresponding to the second acoustic wave signal over time.

[0121] The earthquake prediction device based on earthquake repeating signals in this embodiment is used to implement the earthquake prediction method based on earthquake repeating signals as described above. Its advantages compared with the prior art are the same as those of the above method compared with the prior art, and will not be elaborated here.

[0122] Optionally, calculating the coda wave velocity of the signals from at least two receiving sensors to obtain the change result of the coda wave velocity corresponding to the second acoustic wave signal over time includes:

[0123] Moving a sliding window with a preset time length in the signals from at least two receiving sensors, and performing cross-correlation calculation on the signals from at least two receiving sensors in the sliding window to obtain the cross-correlation signal corresponding to each sliding window; wherein, the preset time length is at least less than the period of the first acoustic wave signal;

[0124] Extracting the coda wave band signals from the cross-correlation signals corresponding to each sliding window, and performing interference calculation on the coda wave band signals to obtain the coda wave velocity of the cross-correlation signal corresponding to each sliding window;

[0125] Obtaining the change result of the coda wave velocity corresponding to the second acoustic wave signal over time according to the coda wave velocity of the cross-correlation signal corresponding to each sliding window.

[0126] Optionally, calculating the coda wave velocity of the signals from at least two receiving sensors to obtain the change result of the coda wave velocity corresponding to the second acoustic wave signal over time further includes:

[0127] Identifying the first motion signals in the signals from at least two receiving sensors respectively, where the first motion signal is the signal with the first amplitude mutation in the signal;

[0128] Moving a sliding window with a preset time length in the signals from at least two receiving sensors includes:

[0129] For the signal from each receiving sensor, starting from the first motion signal in the signal, moving a sliding window with a preset time length in the signal.

[0130] Optionally, before moving a sliding window with a preset time length in the signals from at least two receiving sensors, it further includes:

[0131] Performing high-pass filtering processing on the signals from at least two receiving sensors respectively.

[0132] Optionally, before extracting the tail wave band signals in the cross-correlation signals corresponding to each sliding window, the following steps are further included:

[0133] Determine the spectral information of the cross-correlation signals corresponding to each sliding window;

[0134] Extract the cross-correlation signals corresponding to a predetermined frequency band from the cross-correlation signals corresponding to each sliding window according to the spectral information of the cross-correlation signals corresponding to each sliding window;

[0135] Perform reference zero point alignment processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window;

[0136] Perform normalization processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window after reference zero point processing.

[0137] Optionally, the excitation source is a piezoelectric crystal sensor; and / or, the receiving sensor is a piezoelectric ceramic sensor.

[0138] Optionally, a square wave signal generation module is further included, which is used to generate and output a square wave signal to the excitation source through a waveform generator, so that the excitation source generates a first acoustic wave signal based on the square wave signal.

[0139] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored. When the computer program is executed by a processor, the seismic prediction method based on seismic repeat signals as described above is implemented.

[0140] Or, a non-volatile computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following operations:

[0141] Receive signals from at least two receiving sensors, where the signals are electrical signals corresponding to a second acoustic wave signal, and the second acoustic wave signal is a signal reflected by a target geological fault after a first acoustic wave signal that changes periodically and is emitted by an excitation source to the target geological fault; wherein, the first acoustic wave signal is used to simulate a seismic wave signal;

[0142] Calculate the tail wave velocity of the signals from at least two receiving sensors to obtain the change result of the tail wave velocity corresponding to the second acoustic wave signal over time;

[0143] Determine the time range corresponding to the earthquake nucleation process in the target geological fault according to the change result of the tail wave velocity corresponding to the second acoustic wave signal over time.

[0144] Please refer to Figure 8 , Figure 8The seismic nucleation process outlined according to the peak shear stress (gray area), where r5,10 and r6,9 are two corresponding shear stress receivers located in the nucleation area of the geological fault, one pair close to the starting point of nucleation and one located at the edge of the nucleation area; Figure 9 and Figure 10 are the stress, wave velocity, and fault thickness curves for a stick-slip cycle recorded by two pairs of sensors. Among them, curve g is the tail wave velocity curve at different positions of the fault, curve bl is the shear stress curve at different positions of the fault, indicating that the calculated tail wave velocity has a high accuracy. Curve b2 is the local normal stress curve at different positions of the fault, curve p is the macroscopic shear stress curve at different positions of the fault, used to reflect the shear stress of the entire fault. Curve b3 is the correlation coefficient curve. Since the correlation coefficient is large (>0.8), it shows that the calculated wave velocity has a high accuracy. It can be seen that the moment when the peak of the tail wave velocity (curve g) appears at different positions of the fault is consistent with the moment when the peak of the corresponding shear stress (curve b1) appears. In addition, after the peak point of the shear stress, the fault thickness (curve br) increases rapidly, which also indicates the occurrence of dilatancy during the nucleation stage.

[0145] Those of ordinary skill in the art can understand that all or part of the processes in the above-described method embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-integrated units can be implemented in the form of hardware or in the form of software functional units.

[0146] Although the present invention is disclosed as above, the scope of protection of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the scope of protection of the present invention.

Claims

1. A method for earthquake prediction based on earthquake repetitive signals, characterized in that: include: Receiving signals from at least two receiving sensors, wherein the signals are electrical signals corresponding to a second acoustic wave signal, wherein the second acoustic wave signal is a signal reflected by a target geological fault from a first acoustic wave signal that is periodically changed and emitted by an excitation source to the target geological fault; wherein the first acoustic wave signal is used to simulate a seismic wave signal; Calculating the coda wave velocity of the signals from the at least two receiving sensors to obtain a change result of the coda wave velocity corresponding to the second acoustic wave signal over time; According to the change result of the coda wave velocity corresponding to the second acoustic wave signal over time, the time range corresponding to the earthquake nucleation process in the target geological fault is determined.

2. The earthquake prediction method based on earthquake repetitive signals according to claim 1, characterized in that: The step of calculating the coda wave velocity of the signals from the at least two receiving sensors to obtain a result of a change in the coda wave velocity corresponding to the second acoustic wave signal over time includes: Moving a sliding window with a preset time length in the signals from the at least two receiving sensors, performing cross-correlation calculation on the signals from the at least two receiving sensors in the sliding window, and obtaining a cross-correlation signal corresponding to each sliding window; wherein the preset time length is at least less than the period of the first sound wave signal; Extracting a tail band signal from the cross-correlation signal corresponding to each sliding window, and performing interference calculation on the tail band signal to obtain a tail wave velocity of the cross-correlation signal corresponding to each sliding window; According to the coda wave velocity of the cross-correlation signal corresponding to each sliding window, a change result of the coda wave velocity corresponding to the second acoustic wave signal over time is obtained.

3. The earthquake prediction method based on earthquake repetitive signals according to claim 2, characterized in that: The step of calculating the coda wave velocity of the signals from the at least two receiving sensors to obtain a result of a change in the coda wave velocity corresponding to the second acoustic wave signal over time also includes: Respectively identifying the initial motion signals in the signals from the at least two receiving sensors, wherein the initial motion signals are the signals in which a sudden amplitude change occurs for the first time; The moving a sliding window having a preset time length in the signals from the at least two receiving sensors comprises: For a signal from each receiving sensor, a sliding window with a preset time length is moved in the signal, starting from an initial signal in the signal.

4. The earthquake prediction method based on earthquake repetitive signals according to claim 2, characterized in that: Before moving the sliding window having a preset time length in the signals from the at least two receiving sensors, the method further includes: The signals from the at least two receiving sensors are respectively subjected to high-pass filtering.

5. The earthquake prediction method based on earthquake repetitive signals according to claim 2, characterized in that: Before extracting the tail band signal from the cross-correlation signal corresponding to each sliding window, the method further includes: Determine frequency spectrum information of the cross-correlation signal corresponding to each sliding window; Extracting a cross-correlation signal corresponding to a predetermined frequency band from the cross-correlation signal corresponding to each sliding window according to the frequency spectrum information of the cross-correlation signal corresponding to each sliding window; Performing reference zero point alignment processing on the cross-correlation signals corresponding to the predetermined frequency band in each sliding window; The cross-correlation signal corresponding to the predetermined frequency band in each sliding window after the reference zero point alignment processing is standardized.

6. The earthquake prediction method based on earthquake repetitive signals according to claim 1, characterized in that: The excitation source is a piezoelectric crystal sensor; and / or the receiving sensor is a piezoelectric ceramic sensor.

7. The earthquake prediction method based on earthquake repetitive signals according to any one of claims 1 to 6, characterized in that: Also includes: A square wave signal is generated and outputted to the excitation source by a waveform generator, so that the excitation source generates the first sound wave signal based on the square wave signal.

8. An earthquake prediction device based on earthquake repetitive signals, characterized in that: include: A receiving module, used for receiving signals from at least two receiving sensors, wherein the signals are electrical signals corresponding to a second acoustic wave signal, wherein the second acoustic wave signal is a signal reflected by a target geological fault from a first acoustic wave signal that is periodically changed and emitted by an excitation source to the target geological fault; wherein the first acoustic wave signal is used for simulating a seismic wave signal; a wake wave velocity calculation module, used for calculating the wake wave velocity of the signals from the at least two receiving sensors, and obtaining a change result of the wake wave velocity corresponding to the second sound wave signal over time; The earthquake nucleation process determination module is used to determine the time range corresponding to the earthquake nucleation process in the target geological fault according to the change result of the coda wave velocity corresponding to the second acoustic wave signal over time.

9. An earthquake prediction system, characterized in that: comprising an excitation source, at least two receiving sensors and a processor; The excitation source is used to generate and transmit a first acoustic wave signal that changes periodically to the target geological fault, wherein the first acoustic wave signal is used to simulate a seismic wave signal; Each of the receiving sensors is used to receive the second acoustic wave signal reflected by the first acoustic wave signal through the target geological fault and output a corresponding electrical signal; The processor is used to execute the earthquake prediction method based on earthquake repetitive signals as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the earthquake prediction method based on earthquake repetitive signals as described in any one of claims 1 to 7 is implemented.

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