Method for extracting effective data in batches based on seismic station monitoring data

Abstract

The invention provides a method for extracting effective data in batches based on seismic station monitoring data, and the method comprises the steps: enabling a sensing element in a geophone to vibrate when a seismic wave is transmitted to the geophone, converting a mechanical vibration signal into an electric signal, and converting the electric signal into an EVT digital signal through a signal conversion module; and the communication module sends the EVT file to the seismic analysis software. Earthquake data are preliminarily sorted in earthquake measurement analysis software, and parameters such as the epicentral position, the earthquake occurrence moment and the earthquake magnitude of an earthquake are obtained. The method comprises the following steps: converting an EVT file into an SAC file by utilizing seismometry analysis software, starting a JUPter Notebook at an operation terminal, opening a Notebook file, calling a python library function in a Notebook window to execute a command, extracting seismic time and amplitude recorded in the SAC file, calculating to obtain speed and acceleration at corresponding time on the basis of time and amplitude, and writing obtained data into an Excel file.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake monitoring and data processing, and mainly to a method for batch extracting effective data based on earthquake station monitoring data. Background Art

[0002] Seismic data is the core of earthquake monitoring and early warning systems. These data record the propagation of seismic waves in real time, including characteristics such as amplitude, frequency, and waveform. Through calculation, the epicenter, time of occurrence, and magnitude of the earthquake can be quickly determined. This information is crucial in earthquake early warning, which can provide more time for evacuation in disaster-stricken areas and reduce casualties and property losses. At the same time, seismic data plays an important role in earth science research. By deeply analyzing the propagation characteristics of seismic waves, the internal structure and material composition of the earth can be revealed, and a scientific basis can be provided for the prediction and prevention of geological disasters.

[0003] At present, some earthquake monitoring software will generate earthquake briefings after detecting an earthquake, but the content is relatively simple and lacks earthquake data that can be used for research. Most earthquake monitoring software and systems mainly record earthquake data by generating event log files after detecting an earthquake. The log files are in binary format and require special software to read the log files. The earthquake data recorded in them are also difficult to be directly used for later earthquake parameter inversion and finite element calculations. Event log files are files used to record important events that occur during the operation of the system. According to the settings of the system or program, event types, time, levels, descriptions, data, etc. can be recorded. They are widely used in problem tracking, performance optimization, and security monitoring, and play an important role in systems and programs. However, as the running time of software and systems increases, event log files will also become very large. During operation, event log files may contain a lot of redundant information, such as repeated warnings or informational messages, which will increase the difficulty of analyzing log files. Reading event log files usually depends on dedicated log management tools or software. When such tools or software cannot be used due to failures, the corresponding event log files may not be read and analyzed. Event log files generated by different systems, programs or devices mostly have different formats and standards. This non-uniform format brings great difficulties to the reading and analysis of log files. In order to deal with log files of different formats, additional format conversion is often required before reading and analyzing. For a large number of event log files, if we rely on manual analysis one by one, it is not only inefficient but also prone to errors. Therefore, there is a need for a method to achieve batch data extraction. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for batch extracting valid data based on seismic station monitoring data. This method uses seismic analysis software to convert earthquake event log files into SAC format files, starts Jupyter Notebook in the running terminal, opens the Notebook file, calls the python library function execution command in the Notebook window, extracts the earthquake time and amplitude recorded in the SAC file, calculates the velocity and acceleration at the corresponding time based on the time and amplitude, writes the obtained data into an Excel file, and obtains the time, amplitude, velocity, acceleration and other data recorded by the earthquake monitoring station when the corresponding earthquake event occurs.

[0005] In order to realize the above technical features, the purpose of the present invention is achieved as follows: a method for batch extracting effective data based on seismic station monitoring data: Step 1, the seismic detector detects the occurrence of an earthquake and generates an EVT file; Step 2: The seismic analysis software calculates the epicenter location, earthquake occurrence time, and magnitude data, and converts the EVT file into a SAC file; Step 3: Run the terminal to start Jupyter Notebook and extract the earthquake time and amplitude recorded in the SAC file; Step 4, based on time and amplitude, calculate the velocity and acceleration at the corresponding time; Step 5, write the data obtained in step 4 into an Excel file and output the Excel file.

[0006] The step 1 specifically includes: detecting earthquakes through a seismic detector, which is mainly composed of a sensing element, a signal processing module, a communication module and a power supply module; the sensing element is the main detection element, which generates vibration and sends out an electrical signal after receiving seismic waves; the signal processing module amplifies and filters the electrical signal sent by the sensing element and converts the electrical signal into a .EVT format digital signal, and the communication module transmits the .EVT format file to the seismic analysis software.

[0007] The step 2 specifically includes: after receiving the .EVT format file, the seismic analysis software performs a preliminary sorting of the file, calculates the earthquake epicenter, the time of earthquake occurrence, and the magnitude data through the earthquake location algorithm integrated in the software, and uses the export function under the software file menu bar to export the earthquake event log file into a SAC format file.

[0008] The step 3 specifically includes: inputting Jupyter Notebook in the terminal environment, launching the Jupyter Notebook server, and starting the browser at the same time, the Jupyter Notebook server drives the browser to open the Notebook main page, selecting different Jupyter kernels to create a new Notebook file, setting the SAC file path in the cell window by calling the function contained in the python library, and realizing the input of the data file by pointing to the location of the SAC file.

[0009] The step 4 specifically includes: extracting the earthquake time and amplitude data recorded in the input SAC file by using the trace function in the cell window, calculating the first-order differential of the amplitude and time by using the gradient function, and using the result as the velocity at the amplitude and time; based on the obtained velocity and time data, calculating the first-order differential of the velocity and time by using the gradient function, and using the result as the acceleration at the velocity and time.

[0010] The step 5 specifically includes: writing the obtained four data items into an Excel file in the order of time, amplitude, speed, and acceleration, and outputting the Excel file by using the output function.

[0011] The Jupyter Notebook is an interactive computing environment that opens in the form of a web page. The function of reading data can be realized by writing Python code in Jupyter Notebook. At the same time, when running the code, Jupyter Notebook allows running the code line by line or block by block, which can quickly and effectively process and analyze data.

[0012] The data extraction and processing by Jupyter Notebook includes the following five steps: Step 3.1, start Jupyter Notebook; start Jupyter Notebook in the running terminal, enter the command: jupyter notebook in the terminal environment, the terminal will start the Jupyter Notebook server after executing the command, the terminal will display a series of Notebook server information after the server is started, and open the browser at the same time, driving the browser to start Jupyter Notebook; when the terminal executes the start command, the browser will open the Notebook main page; select different jupyter kernels to create a new Notebook file, use the Notebook file to call the python library function to extract data; the Notebook file main page includes a menu bar, toolbar and cell window, the cell window contains three cell formats, which can be changed by pulling down the toolbar list, the three formats are Code, Markdown, and Raw; Code: Write and run program code. The code format should be consistent with the language format specified by the Jupyter kernel. Markdown: used to edit text and mark the document format content with symbols; Raw: Original cell. The text and code in Raw will not respond during the running process. Use the code cell in the cell window as the basic format, select python3 as the kernel when creating the Notebook file, and use the python language to write the SAC file data extraction code; Step 3.2, import library functions. Before calling the library functions to extract seismic data, you need to import the library functions into the running program. The above commands mainly implement the import of four modules, namely: import os to import the os module, and use the os module to view, create, and delete files; import pandas as pd to import the pandas library and name it pd. The pandas library can process large amounts of data and perform data conversion, grouping, and other operations; import numpy as np to import the numpy library and name it np. The numpy library provides various mathematical functions for data processing and analysis; fromobspy import read to import the read function into the obspy library. The obspy library is often used to process and analyze seismic data formats; Step 3.3, determine the file location; before using Jupyter Notebook to extract file data, you need to ensure that the SAC file has been stored in the terminal. Use the file command to determine the storage location of the SAC file in the terminal, and identify the file to prevent errors caused by other files; folder path is the SAC file location, and filenmae is the file name; Step 3.4, data extraction and calculation, use the trace command to realize the identification and tracking of file data, extract the seismic time data in the SAC file through the time mark and store it in times; extract the seismic amplitude data in the SAC file through the amplitude mark and store it in amplitude, and ensure that the time and amplitude data correspond one to one, use the gradient function to calculate the first-order differential of amplitude and time, and the result is the velocity, which is stored in velocities; use the gradient function to calculate the first-order differential of velocity and time, and the result is the acceleration, which is stored in accelerations; Step 3.5, result output; use the output command to save the data in the register to the specified file, and clear the register to ensure normal operation when it is continued to be used; extract the data from the SAC file as time and amplitude, calculate the specific speed and acceleration, and import them into the specified file in the order of time, amplitude, speed, and acceleration; the output_filename = os.path.splitext(filename)[0] + '.xlsx' command can be used to specify the file type to be saved. In this example, the file is saved as an Excel file; output_file_path=os.path.join(output_folder_path, output_filename) can be used to specify the file save location and name the file at the same time. The save location is output_folder_path and the file is named output_filename.

[0013] The present invention has the following beneficial effects: The present invention generates different types of seismic waves when an earthquake occurs. When the seismic wave propagates to the seismic detector, the inductive element inside the detector vibrates and converts the mechanical vibration signal into an electrical signal. The electrical signal is converted into an EVT digital signal by a signal conversion module, and the EVT file is sent to the seismic analysis software by a communication module. In the seismic analysis software, the seismic data is preliminarily sorted out to obtain parameters such as the epicenter position, the time of the earthquake, and the magnitude of the earthquake. The EVT file is converted into a SAC file using the seismic analysis software, Jupyter Notebook is started in the running terminal, the Notebook file is opened, the python library function execution command is called in the Notebook window, the earthquake time and amplitude recorded in the SAC file are extracted, and the speed and acceleration at the corresponding time are calculated based on time and amplitude, and the obtained data are written into an Excel file to obtain the time, amplitude, speed, acceleration and other data recorded by the seismic monitoring station when the corresponding earthquake event occurs. This effectively improves the data processing efficiency and ensures the data processing quality and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0015] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0016] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0017] Embodiment 1: See also Figure 1 , a method for batch extracting valid data based on seismic station monitoring data: Step 1, the seismic detector detects the occurrence of an earthquake and generates an EVT file; Earthquakes are detected through seismic detectors, which are mainly composed of sensing elements, signal processing modules, communication modules and power modules. The sensing elements are the main detection elements, which generate vibrations and emit electrical signals after receiving seismic waves. The signal processing module amplifies and filters the electrical signals emitted by the sensing elements and converts them into .EVT format digital signals. The communication module transmits the .EVT format files to the seismic analysis software.

[0018] Step 2: The seismic analysis software calculates the epicenter location, earthquake occurrence time, and magnitude data, and converts the EVT file into a SAC file; After receiving the .EVT format file, the seismic analysis software will preliminarily sort out the file, calculate the earthquake epicenter, earthquake time, and magnitude data through the earthquake location algorithm integrated in the software, and use the export function under the software file menu bar to export the earthquake event log file into a SAC format file; Step 3: Run the terminal to start Jupyter Notebook and extract the earthquake time and amplitude recorded in the SAC file; Enter Jupyter Notebook in the terminal environment, pull up the Jupyter Notebook server, and start the browser at the same time. The Jupyter Notebook server drives the browser to open the Notebook main page. Select different Jupyter kernels to create a new Notebook file. In the cell window, set the SAC file path by calling the function contained in the Python library, and input the data file by pointing to the location of the SAC file. Step 4, based on time and amplitude, calculate the velocity and acceleration at the corresponding time; The input SAC file uses the trace function in the cell window to extract the earthquake time and amplitude data recorded in the file, and uses the gradient function to calculate the first-order differential of the amplitude and time, and uses the result as the velocity at the amplitude and time; based on the obtained velocity and time data, the gradient function is used to calculate the first-order differential of the velocity and time, and the result is used as the acceleration at the velocity and time; Step 5, write the data obtained in step 4 into an Excel file and output the Excel file.

[0019] Write the four data obtained into an Excel file in the order of time, amplitude, velocity, and acceleration, and output the Excel file by using the output function.

[0020] Embodiment 2: The present invention provides a method for batch extracting effective data based on seismic station monitoring data, which detects earthquakes and records seismic events and extracts seismic data for studying the impact of earthquakes. The main extracted data include the epicenter of the earthquake, the time of the earthquake, the magnitude, amplitude, velocity, acceleration, etc.

[0021] When the crust breaks, shifts or moves, huge amounts of energy are released, which propagate in the earth and on the surface in the form of seismic waves. Seismic waves are mainly divided into P waves (longitudinal waves), S waves (transverse waves) and surface waves, which have different propagation speeds and characteristics. P waves (longitudinal waves): They are propulsion waves, with the fastest propagation speed in the crust, about 5.5 to 7 km / s. They arrive at the epicenter first, causing the ground to vibrate up and down, but are less destructive; S waves (transverse waves): They are shear waves, with the second fastest propagation speed in the crust, about 3.5 to 4.0 km / s. They arrive at the epicenter second, causing the ground to shake back and forth and left and right, and are more destructive; Surface waves: They are mixed waves excited by the encounter of longitudinal waves and transverse waves on the surface, with large wavelengths and strong amplitudes, and can only propagate along the surface. They are the main factor causing severe damage to buildings.

[0022] Since there are certain differences in the reflection, refraction and transmission of different seismic waves in the medium, seismic detectors can be used to monitor and record seismic wave signals. The seismic detector is mainly composed of a sensing unit, a signal processing module, a communication module and a power module. Among them, the sensing unit adopts a three-component sensing element, including three uniaxial sensing elements installed perpendicular to each other, forming a three-component sensing element, which can simultaneously measure the components of seismic waves in three directions. The seismic detector is arranged at an appropriate position on the ground or underground to receive seismic waves. When seismic waves propagate through the crust, they will cause the strata to vibrate, and then act on the sensing unit, causing the internal sensing element to vibrate. Electromagnetic induction occurs inside the sensing element to convert the mechanical vibration caused by the seismic wave into an electrical signal. The electrical signal emitted by the sensing unit is usually very weak and contains a lot of noise and interference. The induced electrical signal is input into the signal processing module, and the electrical signal is amplified, filtered, and processed to improve the signal-to-noise ratio and clarity of the signal, remove the unnecessary parts, and only retain the useful information related to the seismic wave. The electrical signal after amplification and filtering is converted into a digital signal in .EVT format by the signal processing module, and the .EVT format file is transmitted to the seismic analysis software by the communication module.

[0023] .EVT format files are a file format used in systems and software to store event logs, including event types (such as information, warnings, errors, etc.), event IDs, event descriptions, occurrence times, etc. In general, in order to ensure the integrity and authenticity of event logs, .EVT format files are generally not edited directly. Since .EVT format files are stored in binary format, they cannot be opened and viewed with a regular text editor. The seismic analysis software MSDP is a software dedicated to receiving, processing, and publishing seismic data, and can correctly read .EVT format files generated by seismic detectors. Use the seismic analysis software MSDP to open .EVT format files. MSDP integrates a variety of earthquake location algorithms, such as the simplex method, HYPOSAT, LOCSAT, etc., and can accurately calculate the epicenter location, time of occurrence, magnitude, and other parameters of an earthquake based on different types of earthquake events and station distribution.

[0024] Since the seismic analysis software MSDP cannot export data information such as velocity and acceleration corresponding to the time of earthquake occurrence, it is necessary to use Jupyter Notebook to extract earthquake data such as velocity and acceleration. First, you need to use the export function under the file menu bar in the seismic analysis software to export the .EVT format file to a SAC format file (Seismic Analysis Code). The SAC format is a file format used to store and process seismic data, which contains complete seismic wave related data.

[0025] Jupyter Notebook is an interactive computing environment that opens as a web page. In Jupyter Notebook, you can read data by writing Python code. At the same time, when running the code, Jupyter Notebook allows you to run the code line by line or block by block, which can quickly and effectively process and analyze data.

[0026] The data extraction and processing by Jupyter Notebook includes the following five steps: Step 3.1, start Jupyter Notebook; start Jupyter Notebook in the running terminal, enter the command: jupyter notebook in the terminal environment, the terminal will start the Jupyter Notebook server after executing the command, the terminal will display a series of Notebook server information after the server is started, and open the browser at the same time, driving the browser to start Jupyter Notebook; when the terminal executes the start command, the browser will open the Notebook main page; select different jupyter kernels to create a new Notebook file, use the Notebook file to call the python library function to extract data; the Notebook file main page includes a menu bar, toolbar and cell window, the cell window contains three cell formats, which can be changed by pulling down the toolbar list, the three formats are Code, Markdown, and Raw; Code: Write and run program code. The code format should be consistent with the language format specified by the Jupyter kernel. Markdown: used to edit text and mark the document format content with symbols; Raw: Original cell. The text and code in Raw will not respond during the running process. Use the code cell in the cell window as the basic format, select python3 as the kernel when creating the Notebook file, and use the python language to write the SAC file data extraction code; Step 3.2, import library functions. Before calling the library functions to extract seismic data, you need to import the library functions into the running program. The above commands mainly implement the import of four modules, namely: import os to import the os module, and use the os module to view, create, and delete files; import pandas as pd to import the pandas library and name it pd. The pandas library can process large amounts of data and perform data conversion, grouping, and other operations; import numpy as np to import the numpy library and name it np. The numpy library provides various mathematical functions for data processing and analysis; fromobspy import read to import the read function into the obspy library. The obspy library is often used to process and analyze seismic data formats; Step 3.3, determine the file location; before using Jupyter Notebook to extract file data, you need to ensure that the SAC file has been stored in the terminal. Use the file command to determine the storage location of the SAC file in the terminal, and identify the file to prevent errors caused by other files; folder path is the SAC file location, and filenmae is the file name; Step 3.4, data extraction and calculation, use the trace command to realize the identification and tracking of file data, extract the seismic time data in the SAC file through the time mark and store it in times; extract the seismic amplitude data in the SAC file through the amplitude mark and store it in amplitude, and ensure that the time and amplitude data correspond one to one, use the gradient function to calculate the first-order differential of amplitude and time, and the result is the velocity, which is stored in velocities; use the gradient function to calculate the first-order differential of velocity and time, and the result is the acceleration, which is stored in accelerations; Step 3.5, result output; use the output command to save the data in the register to the specified file, and clear the register to ensure normal operation when it is continued to be used; extract the data from the SAC file as time and amplitude, calculate the specific speed and acceleration, and import them into the specified file in the order of time, amplitude, speed, and acceleration; the output_filename = os.path.splitext(filename)[0] + '.xlsx' command can be used to specify the file type to be saved. In this example, the file is saved as an Excel file; output_file_path=os.path.join(output_folder_path, output_filename) can be used to specify the file save location and name the file at the same time. The save location is output_folder_path and the file is named output_filename.

[0027] Use Jupyter Notebook to extract the SAC file and write the results into an Excel file. The two types of data obtained in the SAC file are time and amplitude. Based on the obtained time and amplitude data, the gradient function is used to calculate the first-order differential of amplitude and time, and the result is used as the speed under the amplitude and time; based on the obtained speed and time data, the gradient function is used to calculate the first-order differential of speed and time, and the result is used as the acceleration under the speed and time. Thus, four types of data are obtained, namely time, amplitude, speed, and acceleration, and the obtained data are written into the Excel file in order.

[0028] An Excel file containing four columns of data is obtained, namely time, amplitude, velocity, and acceleration. Taking the earthquake monitoring station near a large-scale water conservancy project in the southwest region as an example, the earthquake event log file of the earthquake monitoring station in the region is extracted by the method described above, and the EVT earthquake event log file is converted into the SAC format using the MSDP software. The corresponding time and amplitude data in the SAC format file are extracted, and the velocity and acceleration are calculated and written into the Excel file. An Excel file containing four columns of data is obtained. The first column is the time unit in seconds (s); the second column is the amplitude unit in micrometers (um); the third column is the velocity unit (um / s); and the fourth column is the acceleration unit (um / s 2 ).

[0029] The following is the code used for data extraction: import os import pandas as pd import numpy as np from obspy import read folder_path = r'C:\Users\28435\Desktop\dizhenbo\dizhenbo' output_folder_path = os.path.join(folder_path, 'excel_files') os.makedirs(output_folder_path, exist_ok=True) for filename in os.listdir(folder_path): if filename.endswith('.SAC'): file_path = os.path.join(folder_path, filename) try: st = read(file_path) except Exception as e: print(f"Error reading {filename}: {e}") continue data = [] for trace in st: times = trace.times() amplitudes = trace.data velocities = np.gradient(amplitudes, times) accelerations = np.gradient(velocities, times) for time, amplitude, velocity, acceleration in zip(times, amplitudes,velocities, accelerations): data.append([time, amplitude, velocity, acceleration]) df = pd.DataFrame(data, columns=['Time', 'Amplitude', 'Velocity', 'Acceleration']) output_filename = os.path.splitext(filename)[0] + '.xlsx' output_file_path = os.path.join(output_folder_path, output_filename) df.to_excel(output_file_path, index=False) print(f"Data from {filename} has been written to {output_file_path}")。

Claims

1. A method for batch extracting effective data based on seismic station monitoring data, characterized in that: Step 1, the seismic detector detects the occurrence of an earthquake and generates an EVT file; Step 2: The seismic analysis software calculates the epicenter location, earthquake occurrence time, and magnitude data, and converts the EVT file into a SAC file; Step 3: Run the terminal to start Jupyter Notebook and extract the earthquake time and amplitude recorded in the SAC file; Step 4, based on time and amplitude, calculate the velocity and acceleration at the corresponding time; Step 5, write the data obtained in step 4 into an Excel file and output the Excel file.

2. A method for extracting effective data in batches based on seismic station monitoring data according to claim 1, characterized in that: The step 1 specifically includes: detecting earthquakes through a seismic detector, which is mainly composed of a sensing element, a signal processing module, a communication module and a power supply module; the sensing element is the main detection element, which generates vibration and sends out an electrical signal after receiving seismic waves; the signal processing module amplifies and filters the electrical signal sent by the sensing element and converts the electrical signal into a .EVT format digital signal, and the communication module transmits the .EVT format file to the seismic analysis software.

3. The method for extracting effective data in batches based on seismic station monitoring data according to claim 1, characterized in that: The step 2 specifically includes: after receiving the .EVT format file, the seismic analysis software performs a preliminary sorting of the file, calculates the earthquake epicenter, the time of earthquake occurrence, and the magnitude data through the earthquake location algorithm integrated in the software, and uses the export function under the software file menu bar to export the earthquake event log file into a SAC format file.

4. The method for extracting effective data in batches based on seismic station monitoring data according to claim 1, characterized in that: The step 3 specifically includes: inputting Jupyter Notebook in the terminal environment, launching the Jupyter Notebook server, and starting the browser at the same time, the Jupyter Notebook server drives the browser to open the Notebook main page, selecting different Jupyter kernels to create a new Notebook file, setting the SAC file path in the cell window by calling the function contained in the python library, and realizing the input of the data file by pointing to the location of the SAC file.

5. The method for extracting effective data in batches based on seismic station monitoring data according to claim 1, characterized in that: The step 4 specifically includes: extracting the earthquake time and amplitude data recorded in the input SAC file by using the trace function in the cell window, calculating the first-order differential of the amplitude and time by using the gradient function, and using the result as the velocity at the amplitude and time; based on the obtained velocity and time data, calculating the first-order differential of the velocity and time by using the gradient function, and using the result as the acceleration at the velocity and time.

6. A method for extracting effective data in batches based on seismic station monitoring data according to claim 1, characterized in that: The step 5 specifically includes: writing the obtained four data items into an Excel file in the order of time, amplitude, velocity, and acceleration, and outputting the Excel file by using the output function.

7. A method for extracting effective data in batches based on seismic station monitoring data according to claim 4, characterized in that: The Jupyter Notebook is an interactive computing environment that opens in the form of a web page. The function of reading data can be realized by writing Python code in Jupyter Notebook. At the same time, when running the code, Jupyter Notebook allows running the code line by line or block by block, which can quickly and effectively process and analyze data.

8. A method for extracting effective data in batches based on seismic station monitoring data according to claim 7, characterized in that: The data extraction and processing by Jupyter Notebook includes the following five steps: Step 3.1, start Jupyter Notebook; start Jupyter Notebook in the running terminal, enter the command: jupyter notebook in the terminal environment, the terminal will start the Jupyter Notebook server after executing the command, the terminal will display a series of Notebook server information after the server is started, and open the browser at the same time, driving the browser to start Jupyter Notebook; when the terminal executes the start command, the browser will open the Notebook main page; select different jupyter kernels to create a new Notebook file, use the Notebook file to call the python library function to extract data; the Notebook file main page includes a menu bar, toolbar and cell window, the cell window contains three cell formats, which can be changed by pulling down the toolbar list, the three formats are Code, Markdown, and Raw; Code: Write and run program code. The code format should be consistent with the language format specified by the Jupyter kernel. Markdown: used to edit text and mark the document format content with symbols; Raw: Original cell. The text and code in Raw will not respond during the running process. Use the code cell in the cell window as the basic format, select python3 as the kernel when creating the Notebook file, and use python language to write the SAC file data extraction code; Step 3.2, import library functions. Before calling the library functions to extract earthquake data, you need to import the library functions into the running program. The above commands mainly implement the import of four modules, namely: import os to import the os module, and use the os module to implement file viewing, creation, and deletion operations; import pandas as pd to import the pandas library and name it pd. The pandas library can process large amounts of data and perform data conversion, grouping, and other operations; import numpy as np imports the numpy library and names it np. The numpy library provides various mathematical functions for data processing and analysis. from obspyimport read imports the read function into the obspy library. The obspy library is often used to process and analyze seismic data formats. Step 3.3, determine the file location; before using Jupyter Notebook to extract file data, you need to ensure that the SAC file has been stored in the terminal. Use the file command to determine the storage location of the SAC file in the terminal, and identify the file to prevent errors caused by other files; folder path is the SAC file location, and filenmae is the file name; Step 3.4, data extraction and calculation, use the trace command to realize the identification and tracking of file data, extract the seismic time data in the SAC file through the time mark and store it in times; extract the seismic amplitude data in the SAC file through the amplitude mark and store it in amplitude, and ensure that the time and amplitude data correspond one to one, use the gradient function to calculate the first-order differential of amplitude and time, and the result is the velocity, which is stored in velocities; use the gradient function to calculate the first-order differential of velocity and time, and the result is the acceleration, which is stored in accelerations; Step 3.5, result output; use the output command to save the data in the register to the specified file, and clear the register to ensure normal operation when it is continued to be used; extract the data from the SAC file as time and amplitude, calculate the specific speed and acceleration, and import them into the specified file in the order of time, amplitude, speed, and acceleration; the output_filename = os.path.splitext(filename)[0] + '.xlsx' command can be used to specify the file type to be saved. In this example, the file is saved as an Excel file; output_file_path=os.path.join(output_folder_path, output_filename) can be used to specify the file save location and name the file at the same time. The save location is output_folder_path and the file is named output_filename.

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