An earthquake prediction method, device, and system based on repeated seismic signals.

Abstract

This invention provides a method, apparatus, and system for earthquake prediction based on repeated seismic signals, relating to the field of seismic surveying technology. It receives signals from at least two receiving sensors. The received signals are electrical signals corresponding to a second acoustic signal obtained after reflection from a first acoustic signal emitted towards a target geological fault by an excitation source. The system calculates the coma wave velocity of the signals to obtain the change in coma wave velocity over time. Based on the change in coma wave velocity over time, the time range corresponding to the seismic nucleation process in the target geological fault is determined. Since the excitation and reception of acoustic signals are easily implemented under both laboratory and field conditions, and are unaffected by the environment, the accuracy is high. This allows the coma wave velocity determined by the acoustic signals to accurately characterize the seismic nucleation process, and it is also easily implemented under field conditions.

Description

Technical Field

[0001] This invention relates to the field of seismic survey technology, and more specifically, to a seismic prediction method, apparatus, and system based on repeated seismic signals. Background Technology

[0002] Earthquake prediction has always been a global challenge, and finding stable earthquake precursor signals is crucial for successful earthquake prediction. It is generally believed that the appearance of earthquake precursors is related to the earthquake nucleation process. For the earthquake nucleation process, relevant technologies typically determine the process through the following methods:

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

[0004] (2) Determining the earthquake nucleation process by obtaining pre-seismic activity. However, this method has certain limitations because some mainshocks do not have pre-seismic activity.

[0005] (3) Determine the earthquake nucleation process by obtaining the earthquake b-value. Obtaining the earthquake b-value requires a large number of foreshock events, which is quite difficult.

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

[0007] In summary, there is an urgent need for an accurate and readily implementable method to characterize earthquake nucleation processes in the field. Summary of the Invention

[0008] The problem addressed by this invention is how to accurately and easily characterize seismic nucleation processes in the field.

[0009] To address the above problems, this invention provides an earthquake prediction method, apparatus, and system based on repeated earthquake signals.

[0010] In a first aspect, the present invention provides an earthquake prediction method based on repeated seismic signals, comprising:

[0011] The system receives signals from at least two receiving sensors, wherein the signals are electrical signals corresponding to a second acoustic signal, and the second acoustic signal is a signal reflected by the target geological fault after a periodically changing first acoustic signal emitted by an excitation source toward the target geological fault; wherein the first acoustic signal is used to simulate a seismic wave signal.

[0012] The wake wave velocity of the signals from at least two receiving sensors is calculated to obtain the change of the wake wave velocity of the second acoustic signal over time.

[0013] Based on the change in the wake velocity of the second acoustic signal over time, the time range corresponding to the seismic nucleation process in the target geological fault is determined.

[0014] Optionally, the step of calculating the wake wave velocity of the signals from at least two receiving sensors to obtain the change in the wake wave velocity of the second acoustic signal over time includes:

[0015] A sliding window with a preset time length is moved among the signals from at least two receiving sensors, and cross-correlation calculation is performed 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 signal;

[0016] Extract the tailband signal from the cross-correlation signal corresponding to each sliding window, and perform interference calculation on the tailband signal to obtain the tailwave velocity of the cross-correlation signal corresponding to each sliding window;

[0017] Based on the wake wave velocity of the cross-correlation signal corresponding to each sliding window, the change of the wake wave velocity of the second acoustic signal over time is obtained.

[0018] Optionally, the step of calculating the wake wave velocity of the signals from at least two receiving sensors to obtain the change in the wake wave velocity of the second acoustic signal over time further includes:

[0019] Identify the initial motion signal from the signals from at least two receiving sensors, wherein the initial motion signal is the signal in which the amplitude first changes abruptly;

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

[0021] For each signal from the receiving sensor, a sliding window is moved within the signal for a preset time length, starting from the initial signal in the signal.

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

[0023] The signals from at least two receiving sensors are subjected to high-pass filtering.

[0024] Optionally, before extracting the tailband signal from the cross-correlation signal corresponding to each sliding window, the method further includes:

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

[0026] Based on the spectral information of the cross-correlation signal corresponding to each sliding window, extract the cross-correlation signal corresponding to the predetermined frequency band from the cross-correlation signal corresponding to each sliding window;

[0027] The cross-correlation signals corresponding to the predetermined frequency band in each sliding window are subjected to reference zero-point alignment processing.

[0028] The cross-correlation signals corresponding to the predetermined frequency bands in each sliding window after reference zero-point processing are standardized.

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

[0030] Optionally, it also includes:

[0031] A square wave signal is generated and output to the excitation source by a waveform generator, so that the excitation source generates the first acoustic wave signal based on the square wave signal.

[0032] Secondly, the present invention provides an earthquake prediction device based on repeated seismic signals, comprising:

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

[0034] The wake wave velocity calculation module is used to calculate the wake wave velocity of the signals from at least two receiving sensors to obtain the change result of the wake wave velocity of the second acoustic signal over time.

[0035] The earthquake nucleation process determination module is used to determine the time range corresponding to the earthquake nucleation process in the target geological fault based on the change of the wake wave velocity corresponding to the second acoustic signal over time.

[0036] Thirdly, the present invention provides an earthquake prediction system, characterized in that it includes an excitation source, at least two receiving sensors, and a processor;

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

[0038] Each of the receiving sensors is used to receive the second acoustic signal reflected by the first acoustic signal through the target geological fault and output the corresponding electrical signal;

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

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the earthquake prediction method based on repeating seismic signals as described in the first aspect.

[0041] The beneficial effects of the earthquake prediction method, apparatus, and system based on repeated seismic signals of the present invention are as follows: Signals are received from at least two receiving sensors, wherein the received signals from the receiving sensors are electrical signals corresponding to a second acoustic signal obtained after reflection of a first acoustic signal emitted by an excitation source towards a target geological fault. The first acoustic signal emitted by the excitation source is used to simulate a seismic wave signal to obtain the acoustic signal after the simulated seismic wave signal passes through the target geological fault. The wake wave velocity of the signals from at least two receiving sensors is calculated to obtain the change in the wake wave velocity corresponding to the second acoustic signal over time, thereby obtaining the wake wave velocity corresponding to the acoustic signal after the simulated seismic wave signal passes through the target geological fault. Based on the change in the wake wave velocity corresponding to the second acoustic signal over time, the time range corresponding to the earthquake nucleation process in the target geological fault is determined to predict earthquake precursors. Since the excitation and reception of acoustic signals are easy to achieve in both laboratory and field conditions, and the excitation and reception of acoustic signals are not affected by the environment and have high accuracy, the wake wave velocity determined by acoustic signals can accurately characterize the earthquake nucleation process, and is also easy to achieve in field conditions. Attached Figure Description

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

[0043] Figure 2 This is a flowchart illustrating an earthquake prediction method based on repeated seismic signals according to an embodiment of the present invention.

[0044] Figure 3 This is a schematic diagram of the process for calculating the wake velocity of a signal according to an embodiment of the present invention;

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

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

[0047] Figure 6 This is a schematic diagram of the process for extracting a predetermined frequency band from a cross-correlation signal according to an embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram of the structure of an earthquake prediction device based on repeated seismic signals according to an embodiment of the present invention;

[0049] Figure 8 This is a schematic diagram of the seismic nucleation process, outlined based on peak shear stress.

[0050] Figure 9 This is a schematic diagram of stress, wave velocity, and fault thickness curves recorded by a sensor during one stick-slip cycle.

[0051] Figure 10 This is a schematic diagram of the stress, wave velocity, and fault thickness curves recorded by another sensor during a stick-slip cycle. Detailed Implementation

[0052] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below 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 set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0053] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0054] The term "comprising" and its variations as used herein are open-ended, meaning "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"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0055] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

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

[0057] The terminology involved in this invention will be explained below:

[0058] Earthquake nucleation refers to the process by which a geological fault accelerates from a localized quasi-static rupture to a dynamic rupture, encompassing both temporal and spatial processes.

[0059] The wake of a sound wave signal (seismic wave signal) refers to a waveform signal that exhibits attenuation in amplitude and appears at the tail end. The wake wave velocity refers to the wave velocity corresponding to the wake segment of the signal.

[0060] like Figure 1 As shown, an earthquake prediction system provided in this embodiment of the invention may include: an excitation source 110, at least two receiving sensors 120, and a processor 130, wherein:

[0061] The excitation source 110 is used to generate and emit a periodically varying first acoustic signal to the target geological fault. This first acoustic signal is used to simulate a seismic wave signal. Typically, the envelope of a seismic wave signal evolves exponentially; therefore, in this embodiment, the envelope of the first acoustic signal used to simulate a seismic wave signal also needs to evolve exponentially.

[0062] Specifically, the target geological fault can be a geological fault structure constructed under laboratory conditions or an actual geological fault under field conditions; this embodiment does not limit this.

[0063] Specifically, whether in laboratory or field conditions, the excitation source 110 is placed 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 signal it emits will be affected by non-geological faults.

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

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

[0066] In this optional 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 requiring high precision and long-term stability. Therefore, it can be used stably for a long time and generate high-precision acoustic signals.

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

[0068] Specifically, in laboratory conditions, the receiving sensor 120 is typically positioned below the target geological fault in its vertical direction, but it can also be positioned above the target geological fault. In field conditions, due to environmental constraints, the receiving sensor 120 cannot be positioned below the target geological fault; therefore, in field conditions, the receiving sensor 120 is usually positioned above the target geological fault. It should be noted that when the receiving sensor 120 is positioned above the target geological fault, it needs to be spaced a certain distance from the excitation source 110, and multiple receiving sensors 120 also need to be spaced a certain distance apart, for example, multiple receiving sensors 120 can be spaced 10-50 meters apart. After receiving the reflected second wave acoustic signal, each receiving sensor 120 performs certain signal processing to output a corresponding electrical signal.

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

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

[0071] In this optional embodiment, the excitation source 110 can be controlled by the processor 130 to transmit the first acoustic signal, or it can be manually or remotely controlled by the user to transmit the first acoustic 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-purpose 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. Furthermore, the processor 130 can control the earthquake prediction system to perform the corresponding steps of the methods in the various embodiments of this specification.

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

[0074] Understandable Figure 1 The components included in the earthquake prediction system shown are merely illustrative and may include more or fewer components; this invention is not limited thereto.

[0075] like Figure 2 As shown in the figure, an embodiment of the present invention provides an earthquake prediction method based on repeated seismic signals, comprising:

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

[0077] Specifically, the signal from the receiving sensor 120 is an electrical signal corresponding to the second acoustic signal. The second acoustic signal is a signal reflected by the target geological fault after the periodically changing first acoustic signal emitted by the excitation source 110 to the target geological fault. The first acoustic signal is used to simulate a seismic wave signal.

[0078] Specifically, the excitation source 110 is set on one side of the target geological fault (e.g., the upper side in the vertical direction) and emits a first acoustic signal to the target geological fault to simulate a seismic wave signal. The first acoustic signal has the same envelope variation trend as the seismic wave signal. The first acoustic 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 set on one side of the target geological fault, and there is a certain distance between each pair. Since each receiving sensor 120 is located at a different position on the target fault, the second acoustic signal received by each receiving sensor 120 is slightly different. Each receiving sensor 120 outputs a corresponding signal after performing some signal processing on the second acoustic signal it receives. Each receiving sensor 120 corresponds to one output signal.

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

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

[0082] Specifically, the change in wake wave velocity over time can be represented by a wake wave velocity change curve over time.

[0083] S230: Based on the change in the wake velocity of the second acoustic signal over time, determine the time range corresponding to the earthquake nucleation process in the target geological fault, so as to predict earthquake precursors.

[0084] Specifically, during earthquake nucleation, the wake wave velocity decreases over time. After obtaining the change in wake wave velocity over time, the peak position of the wake wave velocity can be recorded, and the time range from the peak position is the time range corresponding to the earthquake nucleation process.

[0085] In this embodiment, a first acoustic signal simulating a seismic wave signal is emitted to the target geological fault by an excitation source 110. The first acoustic signal is reflected by the geological fault to form a second acoustic signal. The second acoustic signal is received and processed by at least two receiving sensors 120 to output a corresponding signal. The signals from the at least two receiving sensors 120 are processed to obtain the wake wave velocity corresponding to the second acoustic signal. Since the second acoustic signal is the acoustic signal after the first acoustic signal is reflected by the target geological fault, the change of the wake wave velocity corresponding to the second acoustic signal over time can determine the earthquake nucleation process, thereby enabling the prediction of earthquake precursor signals by using the wake wave velocity of the acoustic signal.

[0086] Optionally, such as Figure 3 As shown, the wake wave velocity of signals from at least two receiving sensors 120 is calculated, and the change of the wake wave velocity of the second acoustic signal over time is obtained, including:

[0087] S310: Move a sliding window with a preset time length among 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 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 signal.

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

[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 cross-correlation signal is obtained by cross-correlation calculation of the two discrete signals according to the following expression:

[0090] R xy[k] = ∑x[n]·y[k+n+N-1];

[0091] Where n is the period index of the second acoustic signal corresponding to the discrete signal, N is the number of periods contained in the second acoustic 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 signals corresponding to the two discrete signals, and R xy [k] represents the cross-correlation signal.

[0092] Specifically, taking two receiving sensors 120 as an example, the received signals are two signals. After cross-correlation calculation, the length of the cross-correlation signal is twice that of a single signal. The cross-correlation signal includes a causal part and an anti-causal part, and only a portion of it needs to be retained. In this embodiment, the causal part is retained, such as... Figure 4 As shown.

[0093] S320: Extract the tailband signal from the cross-correlation signal corresponding to each sliding window, and perform interference calculation on the tailband signal to obtain the tailwave velocity of the cross-correlation signal corresponding to each sliding window.

[0094] Specifically, the tailband signal in the cross-correlation signal corresponding to the sliding window should be at least a portion or all of the signal longer than the predetermined time within the sliding window. Taking an 850µs sliding window as an example, the tailband signal in the cross-correlation signal should be at least a portion or all of the signal longer than 300µs within the sliding window. Furthermore, the tailband signal should contain at least five peaks; therefore, refer to... Figure 4 The signal segment corresponding to the time range A of the causal part of the cross-correlation signal is taken as the tailband signal.

[0095] It should be noted that different lengths of the tailwave signal extracted from the cross-correlation signal may result in different calculated tailwave velocities, but the final tailwave velocity change over time will be the same. Therefore, this embodiment does not specifically limit the length of the tailwave signal extracted from the cross-correlation signal.

[0096] Specifically, by iteratively performing interference calculations on the tailband signal of the cross-correlation signal for each sliding window, the tailwave velocity of the cross-correlation signal corresponding to each sliding window can be obtained.

[0097] S330: Based on the wake wave velocity of the cross-correlation signal corresponding to each sliding window, obtain the result of the change of the wake wave velocity of the second acoustic signal over time.

[0098] Specifically, by observing the movement of the sliding window in the signal and the tailwave velocity of the cross-correlation signal corresponding to each sliding window, the change of the calculated tailwave velocity over time can be obtained, and this change of tailwave velocity over time can be represented as a curve of the change of tailwave velocity over time.

[0099] In this optional embodiment, compared with other waveforms in the seismic wave signal, the coda wave has the characteristics of carrying more medium information and being able to reflect a larger area of ​​medium anomaly. Therefore, in this embodiment, the signals received by at least two receiving sensors are cross-correlated to obtain cross-correlation signals. Then, the coda wave signal is extracted from the cross-correlation signals, and the coda wave signal is subjected to interference calculation to obtain the change of the coda wave velocity of the second acoustic signal over time, so as to determine the accurate seismic nucleation process.

[0100] Optionally, such as Figure 5 As shown, S310 involves moving a sliding window with a preset time length among signals from at least two receiving sensors 120, including:

[0101] S510: Identify the initial movement signal from signals from at least two receiving sensors 120 respectively.

[0102] Specifically, the initial motion signal is the signal in which the amplitude changes abruptly for the first time in the signal, that is, the P-wave initial motion in the seismic wave signal.

[0103] S520: For each signal from the receiving sensor, starting from the initial signal in the signal, a sliding window with a preset time length is moved in the signal.

[0104] In this optional embodiment, by identifying the initial movement signal and starting the sliding window from the initial movement signal, it is possible to avoid performing cross-correlation calculations on some unrelated signals in the early stage.

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

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

[0107] In this optional embodiment, high-pass filtering is performed on the signals from at least two receiving sensors to remove some low-frequency oscillation signals output by the excitation source 110 under laboratory conditions.

[0108] Optionally, such as Figure 6 As shown, before extracting the tailband signal from the cross-correlation signal corresponding to each sliding window, the process also includes:

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

[0110] S620: Based on the spectral information of the cross-correlation signal corresponding to each sliding window, extract the cross-correlation signal corresponding to the predetermined frequency band from the cross-correlation signal corresponding to each sliding window.

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

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

[0113] As mentioned earlier, the cross-correlation signal includes a causal component and an anti-causal component. Reference zero alignment aligns the intersection point of the causal and anti-causal components with the reference zero. (See reference...) Figure 4 .

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

[0115] Specifically, the cross-correlation signal can be normalized to its maximum value, that is, the cross-correlation signal can be distributed between 0 and 1 or between -1 and 1, in order to avoid errors caused by weak signals or sensor differences.

[0116] In this optional embodiment, by extracting the dominant frequency band, aligning the reference zero point, and standardizing the cross-correlation signal, the accuracy of the processed cross-correlation signal is improved, thereby enhancing the accuracy of subsequent calculation of the wake wave velocity.

[0117] like Figure 7 As shown in the figure, an embodiment of the present invention provides an earthquake prediction device 700 based on repeated earthquake signals, comprising:

[0118] The receiving module 710 is used to receive signals from at least two receiving sensors. The signals are electrical signals corresponding to a second acoustic signal. The second acoustic signal is a signal reflected by the target geological fault after a periodically changing first acoustic signal emitted by an excitation source toward the target geological fault. The first acoustic signal is used to simulate a seismic wave signal.

[0119] The wakewave velocity calculation module 720 is used to calculate the wakewave velocity of signals from at least two receiving sensors, and obtain the change of the wakewave velocity of the second acoustic signal over time.

[0120] The earthquake nucleation process determination module 730 is used to determine the time range corresponding to the earthquake nucleation process in the target geological fault based on the change of the wake wave velocity corresponding to the second acoustic signal over time.

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

[0122] Optionally, the wake wave velocity of the signals from at least two receiving sensors is calculated to obtain the change in the wake wave velocity of the second acoustic signal over time, including:

[0123] A sliding window with a preset time length is moved among signals from at least two receiving sensors, and cross-correlation calculation is performed 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 signal;

[0124] Extract the tailband signal from the cross-correlation signal corresponding to each sliding window, and perform interference calculation on the tailband signal to obtain the tailwave velocity of the cross-correlation signal corresponding to each sliding window;

[0125] Based on the wake wave velocity of the cross-correlation signal corresponding to each sliding window, the change of the wake wave velocity of the second acoustic signal over time is obtained.

[0126] Optionally, calculating the wake wave velocity of signals from at least two receiving sensors to obtain the change in the wake wave velocity of the second acoustic signal over time also includes:

[0127] Identify the initial movement signal from signals from at least two receiving sensors, where the initial movement signal is the signal that first shows a change in amplitude.

[0128] Moving a sliding window for a predetermined time length among signals from at least two receiving sensors includes:

[0129] For each signal from the receiving sensor, a sliding window with a preset time length is moved through the signal, starting from the initial moving signal in the signal.

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

[0131] High-pass filtering is performed on signals from at least two receiving sensors.

[0132] Optionally, before extracting the tailband signal from the cross-correlation signal corresponding to each sliding window, the method further includes:

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

[0134] Based on the spectral information of the cross-correlation signal corresponding to each sliding window, extract the cross-correlation signal corresponding to the predetermined frequency band from the cross-correlation signal corresponding to each sliding window;

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

[0136] The cross-correlation signals corresponding to the predetermined frequency bands in each sliding window after reference zero-point processing are standardized.

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

[0138] Optionally, it also includes a square wave signal generation module, 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] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the earthquake prediction method based on repeated earthquake signals as described above.

[0140] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

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

[0142] The wake wave velocity of the signals from at least two receiving sensors is calculated to obtain the change of the wake wave velocity of the second acoustic signal over time.

[0143] Based on the change in the wake velocity of the second acoustic signal over time, the time range corresponding to the seismic nucleation process in the target geological fault is determined.

[0144] Please refer to Figure 8 , Figure 8For the seismic nucleation process (grey area) delineated based on peak shear stress, r5,10 and r6,9 are two corresponding shear receivers located in the nucleation zone of the geological fault, one pair near the nucleation start point and the other located at the edge of the nucleation zone; Figure 9 and Figure 10 To correspond to the stress, wave velocity, and fault thickness curves recorded by two sensors within a stick-slip cycle, curve g represents the wake wave velocity curve at different locations on the fault, curve bl represents the shear stress curve at different locations on the fault (indicating high accuracy of the calculated wake wave velocity), curve b2 represents the local normal stress curve at different locations on the fault, curve p represents the macroscopic shear stress curve at different locations on the fault, reflecting the shear stress of the entire fault, and curve b3 represents the correlation coefficient curve. Due to the large correlation coefficient (>0.8), the calculated wave velocity is also highly accurate. It can be seen that the peak times of the wake wave velocity (curve g) at different locations on the fault coincide with the peak times of the shear stress (curve b1) at the corresponding locations. Furthermore, the accelerated increase in fault thickness (curve br) after the shear stress peak indicates the occurrence of dilatation during the nucleation stage.

[0145] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0146] While the present invention has been disclosed above, its scope of protection 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 all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A seismic prediction method based on repetitive seismic signals, characterized in that, include: The system receives signals from at least two receiving sensors. The signals are electrical signals corresponding to a second acoustic signal, which is a signal reflected by the target geological fault after a periodically changing first acoustic signal emitted by an excitation source toward the target geological fault. The target geological fault is a fault plane. The first acoustic signal is used to simulate a seismic wave signal. The at least two receiving sensors are spaced a certain distance apart from each other. The wake wave velocity of the signals from at least two receiving sensors is calculated to obtain a cross-correlation signal characterizing the change of the medium in the region between the at least two receiving sensors, and the result of the change of the wake wave velocity of the second acoustic signal over time is obtained. Based on the change in the wake velocity of the second acoustic signal over time, the time range corresponding to the seismic nucleation process in the target geological fault is determined. The step of calculating the wake wave velocity of the signals from at least two receiving sensors to obtain the change in the wake wave velocity of the second acoustic signal over time includes: A sliding window with a preset time length is moved among the signals from at least two receiving sensors, and cross-correlation calculation is performed 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 signal; Extract the tailband signal from the cross-correlation signal corresponding to each sliding window, and perform interference calculation on the tailband signal to obtain the tailwave velocity of the cross-correlation signal corresponding to each sliding window; Based on the wake wave velocity of the cross-correlation signal corresponding to each sliding window, the change of the wake wave velocity of the second acoustic signal over time is obtained. Before extracting the tailband signal from the cross-correlation signal corresponding to each sliding window, the method further includes: Determine the spectral information of the cross-correlation signal corresponding to each sliding window; Based on the spectral information of the cross-correlation signal corresponding to each sliding window, extract the cross-correlation signal corresponding to the predetermined frequency band from the cross-correlation signal corresponding to each sliding window; The cross-correlation signals corresponding to the predetermined frequency band in each sliding window are subjected to reference zero-point alignment processing. The cross-correlation signals corresponding to the predetermined frequency bands in each sliding window after reference zero-point alignment are standardized.

2. The earthquake prediction method based on repeated seismic signals according to claim 1, characterized in that, The step of calculating the wake wave velocity of the signals from at least two receiving sensors to obtain the change in the wake wave velocity of the second acoustic signal over time also includes: Identify the initial motion signal from the signals from at least two receiving sensors, wherein the initial motion signal is the signal in which the amplitude first changes abruptly; The movement of the sliding window for a preset time length in the signals from at least two receiving sensors includes: For each signal from the receiving sensor, a sliding window is moved within the signal for a preset time length, starting from the initial signal in the signal.

3. The earthquake prediction method based on repeated seismic signals according to claim 1, characterized in that, Before moving the sliding window for a preset time length among the signals from at least two receiving sensors, the method further includes: The signals from at least two receiving sensors are subjected to high-pass filtering.

4. The earthquake prediction method based on repeated seismic 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.

5. The earthquake prediction method based on repeated seismic signals according to any one of claims 1 to 4, characterized in that, Also includes: A square wave signal is generated and output to the excitation source by a waveform generator, so that the excitation source generates the first acoustic wave signal based on the square wave signal.

6. An earthquake prediction device based on repetitive seismic signals, characterized in that, include: A receiving module is used to receive signals from at least two receiving sensors. The signals are electrical signals corresponding to a second acoustic signal, which is a signal reflected by the target geological fault after a periodically changing first acoustic signal emitted by an excitation source toward the target geological fault. The first acoustic signal is used to simulate a seismic wave signal. The at least two receiving sensors are spaced a certain distance apart from each other. The wake wave velocity calculation module is used to calculate the wake wave velocity of the signals from at least two receiving sensors to obtain the change result of the wake wave velocity of the second acoustic 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 based on the change of the wake wave velocity corresponding to the second acoustic signal over time. The wake wave velocity calculation module is used to calculate the wake wave velocity of the signals from at least two receiving sensors, and to obtain the change of the wake wave velocity of the second acoustic signal over time, including: A sliding window with a preset time length is moved among the signals from at least two receiving sensors, and cross-correlation calculation is performed 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 signal; Extract the tailband signal from the cross-correlation signal corresponding to each sliding window, and perform interference calculation on the tailband signal to obtain the tailwave velocity of the cross-correlation signal corresponding to each sliding window; Based on the wake wave velocity of the cross-correlation signal corresponding to each sliding window, the change of the wake wave velocity of the second acoustic signal over time is obtained. Before extracting the tailband signal from the cross-correlation signal corresponding to each sliding window, the method further includes: Determine the spectral information of the cross-correlation signal corresponding to each sliding window; Based on the spectral information of the cross-correlation signal corresponding to each sliding window, extract the cross-correlation signal corresponding to the predetermined frequency band from the cross-correlation signal corresponding to each sliding window; The cross-correlation signals corresponding to the predetermined frequency band in each sliding window are subjected to reference zero-point alignment processing. The cross-correlation signals corresponding to the predetermined frequency bands in each sliding window after reference zero-point alignment are standardized.

7. An earthquake prediction system, characterized in that, Includes an excitation source, at least two receiving sensors, and a processor; The excitation source is used to generate and emit a periodically varying first acoustic signal to the target geological fault, the first acoustic signal being used to simulate a seismic wave signal; Each of the receiving sensors is used to receive the second acoustic signal reflected by the first acoustic signal through the target geological fault and output the corresponding electrical signal; The processor is configured to execute the earthquake prediction method based on repeated earthquake signals as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the earthquake prediction method based on repeated earthquake signals as described in any one of claims 1 to 5.

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