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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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pretty_name: patch-the-planet |
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--- |
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# Dataset Card for Patch the Planet |
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## Dataset Description |
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This data was produced by ThinkOnward for the [Patch the Planet Challenge](https://thinkonward.com/app/c/challenges/patch-the-planet), using a synthetic seismic dataset generator called [Synthoseis](https://github.com/sede-open/synthoseis). This dataset consists of 500 training volumes and 15 test volumes. You will also be provided with a training data generation code in the starter notebook to build the training data. This code allows experimentation with different-sized missing data volumes in the seismic data. The challenger can increase the percentage of the missing section in each seismic volume to increase the difficulty. The default missing section will be set to 25%. |
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- **Created by:** Mike McIntire at ThinkOnward |
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- **License:** CC 4.0 |
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## Uses |
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### How to generate a dataset |
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This dataset is provided as whole seismic volumes. It is the users responsibility to generate the missing sections of the seismic volumes. Please follow the steps below to generate the missing sections of the seismic volumes. |
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Step 1: Load the seismic volume and convert from parquet to numpy array |
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```python |
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import pandas as pd |
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import numpy as np |
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def parquet2array(parquet_file, original_shape=(300,300,1259)): |
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df = pd.read_parquet(parquet_file) |
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data_only = df.drop(columns=['Row', 'Col']) |
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# Convert the DataFrame back to a 2D numpy array |
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reshaped_array = data_only.values |
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# Reshape the 2D array back into a 3D array |
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array = reshaped_array.reshape(original_shape) |
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return array |
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``` |
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Step 2: Generate the missing sections of the seismic volume. This code will delete a random section of the seismic volume and return the target region and the mask of the target region. |
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```python |
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def training_data_generator(seismic: np.ndarray, axis: Literal['i_line', 'x_line', None]=None, percentile: int=25): |
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"""Function to delete part of original seismic volume and extract target region |
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Parameters: |
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seismic: np.ndarray 3D matrix with original survey |
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axis: one of 'i_line','x_line' or None. Axis along which part of survey will be deleted. |
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If None (default), random will be chosen |
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percentile: int, size of deleted part relative to axis. Any integer between 1 and 99 (default 20) |
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Returns: |
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seismic: np.ndarray, original survey 3D matrix with deleted region |
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target: np.ndarray, 3D deleted region |
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target_mask: np.ndarray, position of target 3D matrix in seismic 3D matrix. |
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This mask is used to reconstruct original survey -> seismic[target_mask]=target.reshape(-1) |
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""" |
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# check parameters |
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assert isinstance(seismic, np.ndarray) and len(seismic.shape)==3, 'seismic must be 3D numpy.ndarray' |
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assert axis in ['i_line', 'x_line', None], 'axis must be one of: i_line, x_line or None' |
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assert type(percentile) is int and 0<percentile<100, 'percentile must be an integer between 0 and 100' |
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# rescale volume |
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minval = np.percentile(seismic, 2) |
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maxval = np.percentile(seismic, 98) |
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seismic = np.clip(seismic, minval, maxval) |
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seismic = ((seismic - minval) / (maxval - minval)) * 255 |
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# if axis is None get random choice |
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if axis is None: |
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axis = np.random.choice(['i_line', 'x_line'], 1)[0] |
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# crop subset |
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if axis == 'i_line': |
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sample_size = np.round(seismic.shape[0]*(percentile/100)).astype('int') |
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sample_start = np.random.choice(range(seismic.shape[0]-sample_size), 1)[0] |
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sample_end = sample_start+sample_size |
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target_mask = np.zeros(seismic.shape).astype('bool') |
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target_mask[sample_start:sample_end, :, :] = True |
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target = seismic[sample_start:sample_end, :, :].copy() |
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seismic[target_mask] = np.nan |
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else: |
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sample_size = np.round(seismic.shape[1]*(percentile/100)).astype('int') |
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sample_start = np.random.choice(range(seismic.shape[1]-sample_size), 1)[0] |
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sample_end = sample_start+sample_size |
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target_mask = np.zeros(seismic.shape).astype('bool') |
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target_mask[:, sample_start:sample_end, :] = True |
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target = seismic[:, sample_start:sample_end, :].copy() |
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seismic[target_mask] = np.nan |
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return seismic, target, target_mask |
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``` |
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## Dataset Structure |
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- train (500 volumes) |
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- seismicCubes_RFC_fullstack_2023_1234567.parquet |
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- seismicCubes_RFC_fullstack_2023_1234568.parquet |
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- ... |
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- seismicCubes_RFC_fullstack_2023_1234568.parquet |
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- test (15 volumes, 25% missing, target region provided) |
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- seismicCubes_RFC_fullstack_2023_1234567.parquet |
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- seismicCubes_RFC_fullstack_2023_1234568.parquet |
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- ... |
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- seismicCubes_RFC_fullstack_2023_1234568.parquet |
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## Dataset Creation |
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### Source Data |
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This data was produced by ThinkOnward for the Patch the Planet Challenge, using a synthetic seismic dataset generator called [Synthoseis](https://github.com/sede-open/synthoseis). |
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#### Who are the source data producers? |
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This data was produced by ThinkOnward for the Patch the Planet Challenge, using a synthetic seismic dataset generator called [Synthoseis](https://github.com/sede-open/synthoseis). The data is provided as whole seismic volumes. It is the users responsibility to generate the missing sections of the seismic volumes. using the provided code. |
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### Recommendations |
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This is a synthetically generated dataset, and differs from real-world seismic data. It is recommended that this dataset be used for research purposes only. |
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## Citation |
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This dataset was released in conjunction with the presentation of a poster at the 2024 IMAGE Conference in Houston, Texas (August 26-29th, 2024) |
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**BibTeX:** |
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@misc {thinkonward_2024, |
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author = { {ThinkOnward} }, |
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title = { patch-the-planet (Revision 5e94745) }, |
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year = 2024, |
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url = { https://huggingface.co/datasets/thinkonward/patch-the-planet }, |
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doi = { 10.57967/hf/2909 }, |
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publisher = { Hugging Face } |
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} |
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**APA:** |
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McIntire, M., Tanovic, O., Mazura, J., Suurmeyer, N., & Pisel, J. (n.d.). Geophysical Foundation Model: Improving results with trace masking. In https://imageevent.aapg.org/portals/26/abstracts/2024/4092088.pdf. 2024 IMAGE Conference, Houston, United States of America. |
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## Dataset Card Contact |
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Please contact `[email protected]` for questions, comments, or concerns about this model. |
